Predicting drug-drug interactions: Application of physiologically based pharmacokinetic models under a systems biology approach

Expert Review of Clinical Pharmacology

Volumn 6, Issue 2, 2013, Pages 143-157

  Yeo, Karen Rowland ^a   Jamei, Masoud ^a   Rostami Hodjegan, Amin ^a,b  

^a CERTARA UK LIMITED   (United Kingdom)

^b UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

drug drug interactions; modeling; pharmacokinetics; simulation; variability

Indexed keywords

ATOMOXETINE; CYCLOSPORIN; CYTOCHROME P448; DESIPRAMINE; METOPROLOL; PAROXETINE; REPAGLINIDE; TERBINAFINE;

AREA UNDER THE CURVE; DRUG ABSORPTION; DRUG CLEARANCE; DRUG DISTRIBUTION; DRUG DRUG INTERACTION; DRUG INDUSTRY; DRUG INTERACTION; DRUG METABOLISM; IC 50; IN VITRO STUDY; IN VIVO STUDY; PHARMACOLOGICAL PARAMETERS; PREDICTION; REVIEW; SINGLE DRUG DOSE;

COMPUTER SIMULATION; DRUG DISCOVERY; DRUG INTERACTIONS; HUMANS; MODELS, BIOLOGICAL; PHARMACOKINETICS; PHARMACOLOGY, CLINICAL; SYSTEMS BIOLOGY;

EID: 84874849830     PISSN: 17512433     EISSN: 17512441     Source Type: Journal    
DOI: 10.1586/ecp.13.4     Document Type: Review

References (133)

1. Ribbing J, Jonsson EN. Power, selection bias and predictive performance of the population pharmacokinetic covariate model. J. Pharmacokinet. Pharmacodyn. 31(2), 109-134 (2004
2. Rostami-Hodjegan A, Tucker GT. Simulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nat. Rev. Drug Discov. 6(2), 140-148 (2007
3. Rostami-Hodjegan A. Physiologically based pharmacokinetics joined with in vitro-.in vivo extrapolation of ADME: A marriage under the arch of systems pharmacology. Clin. Pharmacol. Ther. 92(1), 50-61 (2012
4. Gibson GG, Rostami-Hodjegan A. Modelling and simulation in prediction of human xenobiotic absorption, distribution, metabolism and excretion (ADME): In vitro-in vivo extrapolations (IVIVE Xenobiotica 37(10-11), 1013-1014 (2007
5. Jamei M, Dickinson GL, RostamiHodjegan A. A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: A tale of 'bottom-up' vs 'top-down' recognition of covariates. Drug Metab. Pharmacokinet. 24(1), 53-75 (2009
6. Rowland M, Peck C, Tucker G. Physiologically-.based pharmacokinetics in drug development and regulatory science. Annu. Rev. Pharmacol. Toxicol. 51, 45-73 (2011
7. Zhao P, Zhang L, Grillo JA et al. Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review. Clin. Pharmacol. Ther. 89(2), 259-267 (2011
8. Zhao P, Rowland M, Huang SM. Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions. Clin. Pharmacol. Ther. 92(1), 17-20 (2012
9. Jonker DM, Visser SA, van der Graaf PH, Voskuyl RA, Danhof M. Towards a mechanism-based analysis of pharmacodynamic drug-drug interactions in vivo. Pharmacol. Ther. 106(1), 1-18 (2005
10. van der Graaf PH, Benson N. Systems pharmacology: Bridging systems biology and pharmacokinetics-pharmacodynamics (PKPD) in drug discovery and development. Pharm. Res. 28(7), 1460-1464 (2011
11. Chin TW, Loeb M, Fong IW. Effects of an acidic beverage (Coca-Cola) on absorption of ketoconazole. Antimicrob. Agents Chemother. 39(8), 1671-1675 (1995
12. Giacomini KM, Huang SM, Tweedie DJ et al. Membrane transporters in drug development. Nat. Rev. Drug Discov. 9(3), 215-236 (2010
13. Huang SM, Zhang L, Giacomini KM. The International Transporter Consortium: A collaborative group of scientists from academia, industry, and the FDA. Clin. Pharmacol. Ther. 87(1), 32-36 (2010).
14. Silverman RB. Mechanism-based enzyme inactivators. Meth. Enzymol. 249, 240-283 (1995
15. Venkatakrishnan K, Obach RS. Drug-.drug interactions via mechanism-based cytochrome P450 inactivation: Points to consider for risk assessment from in vitro data and clinical pharmacologic evaluation. Curr. Drug Metab. 8(5), 449-462 (2007
16. Huang SM, Lesko LJ. Drug-drug, drug-dietary supplement, and drug-citrus fruit and other food interactions: What have we learned? J. Clin. Pharmacol. 44(6), 559-569 (2004
17. Somogyi A, Stockley C, Keal J, Rolan P, Bochner F. Reduction of metformin renal tubular secretion by cimetidine in man. Br. J. Clin. Pharmacol. 23(5), 545-551 (1987
18. Tsuda M, Terada T, Ueba M et al. Involvement of human multidrug and toxin extrusion 1 in the drug interaction between cimetidine and metformin in renal epithelial cells. J. Pharmacol. Exp. Ther. 329(1), 185-191 (2009
19. Schinkel AH, Wagenaar E, van Deemter L, Mol CA, Borst P. Absence of the mdr1a P-glycoprotein in mice affects tissue distribution and pharmacokinetics of dexamethasone, digoxin, and cyclosporin A. J. Clin. Invest. 96(4), 1698-1705 (1995
20. Ito S, Koren G, Harper PA, Silverman M. Energy-dependent transport of digoxin across renal tubular cell monolayers (LLC-PK1). Can. J. Physiol. Pharmacol. 71(1), 40-47 (1993
21. Rameis H. Quinidine-digoxin interaction: Are the pharmacokinetics of both drugs altered? Int. J. Clin. Pharmacol. Ther. Toxicol. 23(3), 145-153 (1985
22. Pedersen KE, Christiansen BD, Klitgaard NA, Nielsen-Kudsk F. Effect of quinidine on digoxin bioavailability. Eur. J. Clin. Pharmacol. 24(1), 41-47 (1983
23. Hedman A, Angelin B, Arvidsson A et al. Digoxin-verapamil interaction: Reduction of biliary but not renal digoxin clearance in humans. Clin. Pharmacol. Ther. 49(3), 256-262 (1991
24. Niemi M. Role of OATP transporters in the disposition of drugs. Pharmacogenomics (7), 787-802 (2007
25. Lau YY, Huang Y, Frassetto L, Benet LZ. effect of OATP1B transporter inhibition on the pharmacokinetics of atorvastatin in healthy volunteers. Clin. Pharmacol. Ther. 81(2), 194-204 (2007
26. Niemi M, Pasanen MK, Neuvonen PJ Organic anion transporting polypeptide 1: A genetically polymorphic transporter of major importance for hepatic drug uptake. Pharmacol. Rev. 63(1), 157-181 (2011
27. Bidstrup TB, Bjørnsdottir I, Sidelmann UG, Thomsen MS, Hansen KT. CYP2C8 and CYP3A4 are the principal enzymes involved in the human in vitro biotransformation of the insulin secretagogue repaglinide. Br. J. Clin. Pharmacol. 56(3), 305-314 (2003
28. Kajosaari LI, Laitila J, Neuvonen PJ, Backman JT. Metabolism of repaglinide by CYP2C8 and CYP3A4 in vitro: Effect of fibrates and rifampicin. Basic Clin. Pharmacol. Toxicol. 97(4), 249-256 (2005
29. Niemi M, Kajosaari LI, Neuvonen M, Backman JT, Neuvonen PJ. The CYP2C8 inhibitor trimethoprim increases the plasma concentrations of repaglinide in healthy subjects. Br. J. Clin. Pharmacol. 57(4), 441-447 (2004
30. Hatorp V, Hansen KT, Thomsen MS. Influence of drugs interacting with CYP3A4 on the pharmacokinetics, pharmacodynamics, and safety of the prandial glucose regulator repaglinide. J. Clin. Pharmacol. 43(6), 649-660 (2003
31. Niemi M, Backman JT, Neuvonen M, Neuvonen PJ. Effects of gemfibrozil, itraconazole, and their combination on the pharmacokinetics and pharmacodynamics of repaglinide: Potentially hazardous interaction between gemfibrozil and repaglinide. Diabetologia 46(3), 347-351 (2003
32. Rowland M, Matin SB. Kinetics of DDIs. J. Pharmacokinet. Pharmacodyn. 1(6), 553-567 (1973
33. Ito K, Iwatsubo T, Kanamitsu S, Nakajima Y, Sugiyama Y. Quantitative prediction of in vivo drug clearance and drug interactions from in vitro data on metabolism, together with binding and transport. Annu. Rev. Pharmacol. Toxicol. 38, 461-499 (1998
34. Rostami-Hodjegan A, Tucker GT. 'In silico' simulations to assess the 'in vivo' consequences of 'in vitro' metabolic DDIs. Drug Discov. Today Tech. 1, 441-448 (2004
35. Hisaka A, Ohno Y, Yamamoto T, Suzuki H. Theoretical considerations on quantitative prediction of drug-drug interactions. Drug Metab. Pharmacokinet. 25(1), 48-61 (2010
36. Wang YH, Jones DR, Hall SD. Prediction of cytochrome P450 3A inhibition by verapamil enantiomers and their metabolites. Drug Metab. Dispos. 32(2), 259-266 (2004
37. Galetin A, Burt H, Gibbons L, Houston JB. Prediction of time-dependent CYP3A4 drug-drug interactions: Impact of enzyme degradation, parallel elimination pathways, and intestinal inhibition. Drug Metab. Dispos. 34(1), 166-175 (2006
38. Mayhew BS, Jones DR, Hall SD. An in vitro model for predicting in vivo inhibition of cytochrome P450 3A4 by metabolic intermediate complex formation. Drug Metab. Dispos. 28(9), 1031-1037 (2000
39. Almond LM, Yang J, Jamei M, Tucker GT, Rostami-Hodjegan A. Towards a quantitative framework for the prediction of DDIs arising from cytochrome P450 induction. Curr. Drug Metab. 10(4), 420-432 (2009
40. Kato M, Chiba K, Horikawa M, Sugiyama Y. The quantitative prediction of in vivo enzyme-induction caused by drug exposure from in vitro information on human hepatocytes. Drug Metab. Pharmacokinet. 20(4), 236-243 (2005
41. Shou M, Hayashi M, Pan Y et al. Modeling, prediction, and in vitro-in vivo correlation of CYP3A4 induction. Drug Metab. Dispos. 36(11), 2355-2370 (2008
42. Ohno Y, Hisaka A, Ueno M, Suzuki H. General framework for the prediction of oral drug interactions caused by CYP3A4 induction from in vivo information. Clin. Pharmacokinet. 47(10), 669-680 (2008
43. Yang J, Liao M, Shou M et al. Cytochrome p450 turnover: Regulation of synthesis and degradation, methods for determining rates, and implications for the prediction of drug interactions. Curr. Drug Metab. 9(5), 384-394 (2008
44. Kozawa M, Honma M, Suzuki H. Quantitative prediction of in vivo profiles of CYP3A4 induction in humans from in vitro results with a reporter gene assay. Drug Metab. Dispos. 37(6), 1234-1241 (2009
45. Shou M. Prediction of pharmacokinetics and drug-drug interactions from in vitro metabolism data. Curr. Opin. Drug Discov. Devel. 8(1), 66-77 (2005
46. Obach RS, Walsky RL, Venkatakrishnan K, Gaman EA, Houston JB, Tremaine LM. The utility of in vitro cytochrome P450 inhibition data in the prediction of drug-drug interactions. J. Pharmacol. Exp. Ther. 316(1), 336-348 (2006
47. Grime K, Riley RJ. The impact of in vitro binding on in vitro-in vivo extrapolations, projections of metabolic clearance and clinical drug-drug interactions. Curr. Drug Metab. 7(3), 251-264 (2006
48. Hyland R, Dickins M, Collins C, Jones H, Jones B. Maraviroc: In vitro assessment of drug-drug interaction potential. Br. J. Clin. Pharmacol. 66(4), 498-507 (2008
49. Xu L, Chen Y, Pan Y, Skiles GL, Shou M. Prediction of human drug-drug interactions from time-dependent inactivation of CYP3A4 in primary hepatocytes using a population-based simulator. Drug Metab. Dispos. 37(12), 2330-2339 (2009
50. Youdim KA, Zayed A, Dickins M et al. Application of CYP3A4 in vitro data to predict clinical drug-drug interactions; predictions of compounds as objects of interaction. Br. J. Clin. Pharmacol. 65(5), 680-692 (2008
51. Fahmi OA, Hurst S, Plowchalk D et al. Comparison of different algorithms for predicting clinical drug-drug interactions, based on the use of CYP3A4 in vitro data: Predictions of compounds as precipitants of interaction. Drug Metab. Dispos. 37(8), 1658-1666 (2009
52. Gandelman K, Zhu T, Fahmi OA et al. Unexpected effect of rifampin on the pharmacokinetics of linezolid: In silico and in vitro approaches to explain its mechanism. J. Clin. Pharmacol. 51(2), 229-236 (2011
53. Zhang L, Zhang YD, Strong JM, Reynolds KS, Huang SM. A regulatory viewpoint on transporter-based drug interactions. Xenobiotica 38(7-8), 709-724 (2008
54. Agarwal S, Arya V, Zhang L. Review of P-gp inhibition data in recently approved new drug applications: Utility of the proposed [I1]/IC50 and [I2]/IC50 criteria in the P-gp decision tree. J. Clin. Pharmacol. doi:10.1177/ 0091270011436344 (2012) (Epub ahead of print
55. Fenner KS, Troutman MD, Kempshall S et al. Drug-drug interactions mediated through P-glycoprotein: Clinical relevance and in vitro-in vivo correlation using digoxin as a probe drug. Clin. Pharmacol. Ther. 85(2), 173-181 (2009
56. Lee CA. Application of receiver operating characteristics to refine the prediction of potential digoxin drug interactions. Drug Metab. Dispos. (2012) (In Press).
57. Shitara Y, Sato H, Sugiyama Y. Evaluation of drug-drug interaction in the hepatobiliary and renal transport of drugs. Annu. Rev. Pharmacol. Toxicol. 45, 689-723 (2005
58. Hinton LK, Galetin A, Houston JB. Multiple inhibition mechanisms and prediction of drug-drug interactions: Status of metabolism and transporter models as exemplified by gemfibrozil-drug interactions. Pharm. Res. 25(5), 1063-1074 (2008
59. Karlgren M, Ahlin G, Bergström CA, Svensson R, Palm J, Artursson P. In vitro and in silico strategies to identify OATP1B1 inhibitors and predict clinical drug-drug interactions. Pharm. Res. 29(2), 411-426 (2012
60. Yoshida K, Maeda K, Sugiyama Y. Transporter-mediated drug-drug interactions involving OATP substrates: Predictions based on in vitro inhibition studies. Clin. Pharmacol. Ther. 91(6), 1053-1064 (2012
61. Einolf HJ. Comparison of different approaches to predict metabolic drug-drug interactions. Xenobiotica. 37(10-11), 1257-1294 (2007
62. Baneyx G, Fukushima Y, Parrott N. Use of physiologically based pharmacokinetic modeling for assessment of drug-drug interactions. Future Med. Chem. 4(5), 681-693 (2012
63. Rane A, Wilkinson GR, Shand DG. Prediction of hepatic extraction ratio from in vitro measurement of intrinsic clearance. J. Pharmacol. Exp. Ther. 200(2), 420-424 (1977
64. Wilkinson GR. Clearance approaches in pharmacology. Pharmacol. Rev. 39(1), 1-47 (1987
65. Houston JB. Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance. Biochem. Pharmacol. 47(9), 1469-1479 (1994
66. Barter ZE, Bayliss MK, Beaune PH et al. Scaling factors for the extrapolation of in vivo metabolic drug clearance from in vitro data: Reaching a consensus on values of human microsomal protein and hepatocellularity per gram of liver. Curr. Drug Metab. 8(1), 33-45 (2007
67. Proctor NJ, Tucker GT, Rostami-Hodjegan A. Predicting drug clearance from recombinantly expressed CYPs: Intersystem extrapolation factors. Xenobiotica 34(2), 151-178 (2004
68. Iwatsubo T, Suzuki H, Sugiyama Y. Prediction of species differences (rats, dogs, humans) in the in vivo metabolic clearance of YM796 by the liver from in vitro data. J. Pharmacol. Exp. Ther. 283(2), 462-469 (1997
69. Obach RS. Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: An examination of in vitro half-life approach and nonspecific binding to microsomes. Drug Metab. Dispos. 27(11), 1350-1359 (1999
70. Riley RJ, McGinnity DF, Austin RP. A unified model for predicting human hepatic, metabolic clearance from in vitro intrinsic clearance data in hepatocytes and microsomes. Drug Metab. Dispos. 33(9), 1304-1311 (2005
71. Howgate EM, Rowland Yeo K, Proctor NJ, Tucker GT, Rostami-Hodjegan A. Prediction of in vivo drug clearance from in vitro data. I: Impact of inter-individual variability. Xenobiotica 36(6), 473-497 (2006
72. Johnson TN, Tucker GT, Tanner MS, Rostami-Hodjegan A. Changes in liver volume from birth to adulthood: A meta-.analysis. Liver Transpl. 11(12), 1481-1493 (2005
73. Dickinson GL, Rezaee S, Proctor NJ, Lennard MS, Tucker GT, Rostami-Hodjegan A. Incorporating in vitro information on drug metabolism into clinical trial simulations to assess the effect of CYP2D6 polymorphism on pharmacokinetics and pharmacodynamics: Dextromethorphan as a model application. J. Clin. Pharmacol. 47(2), 175-186 (2007
74. Johnson TN, Rostami-Hodjegan A, Tucker GT. Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children. Clin. Pharmacokinet. 45(9), 931-956 (2006
75. Inoue S, Howgate EM, Rowland-Yeo K et al. Prediction of in vivo drug clearance from in vitro data. II: Potential inter-ethnic differences. Xenobiotica 36(6), 499-513 (2006
76. Chetty M, Mattison D, Rostami-Hodjegan A. Sex differences in the clearance of CYP3A4 substrates: Exploring possible reasons for the substrate dependency and lack of consensus. Curr. Drug Metab. 13(6), 778-786 (2012
77. Abduljalil K, Furness P, Johnson TN, Rostami-Hodjegan A, Soltani H. Anatomical, physiological and metabolic changes with gestational age during normal pregnancy: A database for parameters required in physiologically based pharmacokinetic modelling. Clin. Pharmacokinet. 51(6), 365-396 (2012
78. Ghobadi C, Johnson TN, Aarabi M et al. Application of a systems approach to the bottom-up assessment of pharmacokinetics in obese patients: Expected variations in clearance. Clin. Pharmacokinet. 50(12), 809-822 (2011
79. Johnson TN, Boussery K, Rowland-Yeo K, Tucker GT, Rostami-Hodjegan A. A semi-mechanistic model to predict the effects of liver cirrhosis on drug clearance. Clin. Pharmacokinet. 49(3), 189-206 (2010
80. Edginton AN, Willmann S. Physiologybased simulations of a pathological condition: Prediction of pharmacokinetics in patients with liver cirrhosis. Clin. Pharmacokinet. 47(11), 743-752 (2008
81. Rowland Yeo K, Aarabi M, Jamei M, Rostami-Hodjegan A. Modeling and predicting drug pharmacokinetics in patients with renal impairment. Expert Rev. Clin. Pharmacol. 4(2), 261-274 (2011
82. Zhao P, Vieira Mde L, Grillo JA et al. Evaluation of exposure change of nonrenally eliminated drugs in patients with chronic kidney disease using physiologically based pharmacokinetic modeling and simulation. J. Clin. Pharmacol. 52(Suppl. 1), 91S-108S (2012
83. Plowchalk DR, Rowland Yeo K. Prediction of drug clearance in a smoking population: Modeling the impact of variable cigarette consumption on the induction of CYP1A2. Eur. J. Clin. Pharmacol. 68(6), 951-960 (2012
84. McNamara PJ, Alcorn J. Protein binding predictions in infants. AAPS PharmSci. 4(1), E4 (2002
85. Willmann S, Höhn K, Edginton A et al. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. J. Pharmacokinet. Pharmacodyn. 34(3), 401-431 (2007
86. Cubitt HE, Yeo KR, Howgate EM, Rostami-Hodjegan A, Barter ZE. Sources of interindividual variability in IVIVE of clearance: An investigation into the prediction of benzodiazepine clearance using a mechanistic population-based pharmacokinetic model. Xenobiotica 41(8), 623-638 (2011
87. Rowland Yeo K, Jamei M, Yang J, Tucker GT, Rostami-Hodjegan A. Physiologically based mechanistic modelling to predict complex drug-drug interactions involving simultaneous competitive and time-dependent enzyme inhibition by parent compound and its metabolite in both liver and gut - the effect of diltiazem on the time-course of exposure to triazolam. Eur. J. Pharm. Sci. 39(5), 298-309 (2010
88. Wang YH. Confidence assessment of the Simcyp time-based approach and a static mathematical model in predicting clinical drug-drug interactions for mechanismbased CYP3A inhibitors. Drug Metab. Dispos. 38(7), 1094-1104 (2010
89. Fahmi OA, Maurer TS, Kish M, Cardenas E, Boldt S, Nettleton D. A combined model for predicting CYP3A4 clinical net drug-drug interaction based on CYP3A4 inhibition, inactivation, and induction determined in vitro. Drug Metab. Dispos. 36(8), 1698-1708 (2008
90. Vossen M, Sevestre M, Niederalt C, Jang IJ, Willmann S, Edginton AN. Dynamically simulating the interaction of midazolam and the CYP3A4 inhibitor itraconazole using individual coupled whole-body physiologically-based pharmacokinetic (WB-PBPK) models. Theor. Biol. Med. Model. 4, 13 (2007
91. Zhang X, Quinney SK, Gorski JC, Jones DR, Hall SD. Semiphysiologically based pharmacokinetic models for the inhibition of midazolam clearance by diltiazem and its major metabolite. Drug Metab. Dispos. 37(8), 1587-1597 (2009
92. Yang J, Kjellsson M, Rostami-Hodjegan A, Tucker GT. The effects of dose staggering on metabolic drug-drug interactions. Eur. J. Pharm. Sci. 20(2), 223-232 (2003
93. Zhao P, Ragueneau-Majlessi I, Zhang L et al. Quantitative evaluation of pharmacokinetic inhibition of CYP3A substrates by ketoconazole: A simulation study. J. Clin. Pharmacol. 49(3), 351-359 (2009
94. Nestorov I. Whole-body physiologically based pharmacokinetic models. Expert Opin. Drug Metab. Toxicol. 3(2), 235-249 (2007
95. De Buck SS, Mackie CE. Physiologically based approaches towards the prediction of pharmacokinetics: In vitro-in vivo extrapolation. Expert Opin. Drug Metab. Toxicol. 3(6), 865-878 (2007
96. Bouzom F, Walther B. Pharmacokinetic predictions in children by using the physiologically based pharmacokinetic modelling. Fundam. Clin. Pharmacol. 22(6), 579-587 (2008
97. Espié P, Tytgat D, Sargentini-Maier ML, Poggesi I, Watelet JB. Physiologically based pharmacokinetics (PBPK). Drug Metab. Rev. 41(3), 391-407 (2009
98. Perdaems N, Blasco H, Vinson C et al Predictions of metabolic drug-drug interactions using physiologically based modelling: Two cytochrome P450 3A4 substrates coadministered with ketoconazole or verapamil. Clin. Pharmacokinet. 49(4), 239-258 (2010
99. Bouzom F, Ball K, Perdaems N, Walther B. Physiologically based pharmacokinetic (PBPK) modelling tools: How to fit with our needs? Biopharm. Drug Dispos. 33(2), 55-71 (2012
100. Grillo JA, Zhao P, Bullock J et al. Utility of a physiologically-based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug-drug-disease interaction scenario for rivaroxaban during the drug review process: Implications for clinical practice. Biopharm. Drug Dispos. 33(2), 99-110 (2012
101. Shaffer CL, Scialis RJ, Rong H, Obach RS. Using Simcyp to project human oral pharmacokinetic variability in early drug research to mitigate mechanism-based adverse events. Biopharm. Drug Dispos. 33(2), 72-84 (2012
102. Chen Y, Jin JY, Mukadam S, Malhi V, Kenny Jr. Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: Strategy and approach during the drug discovery phase with four case studies. Biopharm. Drug Dispos. 33(2), 85-98 (2012
103. Sinha VK, Snoeys J, Osselaer NV, Peer AV, Mackie C, Heald D. From preclinical to human - prediction of oral absorption and drug-drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach in an industrial setting: A workflow by using case example. Biopharm. Drug Dispos. 33(2), 111-121 (2012
104. Fan J, Chen S, Chow EC, Pang KS. PBPK modeling of intestinal and liver enzymes and transporters in drug absorption and sequential metabolism. Curr. Drug Metab. 11(9), 743-761 (2010
105. Watanabe T, Kusuhara H, Sugiyama Y. Application of physiologically based pharmacokinetic modeling and clearance concept to drugs showing transportermediated distribution and clearance in humans. J. Pharmacokinet. Pharmacodyn. 37(6), 575-590 (2010
106. Darwich AS, Neuhoff S, Jamei M, Rostami-Hodjegan A. Interplay of metabolism and transport in determining oral drug absorption and gut wall metabolism: A simulation assessment using the 'Advanced Dissolution, Absorption, Metabolism (ADAM)' model. Curr. Drug Metab. 11(9), 716-729 (2010
107. Jones HM, Barton HA, Lai Y et al. Mechanistic pharmacokinetic modeling for the prediction of transporter-mediated disposition in humans from sandwich culture human hepatocyte data. Drug Metab. Dispos. 40(5), 1007-1017 (2012
108. Harwood MD, Neuhoff S, Carlson GL, Warhurst G, Rostami-Hodjegan A. Absolute abundance and function of intestinal drug transporters: A prerequisite for fully mechanistic in vitro-in vivo extrapolation of oral drug absorption. Biopharm. Drug Dispos. doi:10.1002/bdd.1810 (2012) (Epub ahead of print
109. Varma MV, Lai Y, Feng B, Litchfield J, Goosen TC, Bergman A. Physiologically based modeling of pravastatin transportermediated hepatobiliary disposition and drug-drug interactions. Pharm. Res. 29(10), 2860-2873 (2012
110. Ohtsuki S, Schaefer O, Kawakami H et al. Simultaneous absolute protein quantification of transporters, cytochromes P450, and UDP- glucuronosyltransferases as a novel approach for the characterization of individual human liver: Comparison with mRNA levels and activities. Drug Metab. Dispos. 40(1), 83-92 (2012
111. Zhang L, Huang SM, Lesko LJ. Transporter-.mediated drug-drug interactions. Clin. Pharmacol. Ther. 89(4), 481-484 (2011
112. Beal SL, Sheiner LB. Estimating population kinetics. Crit. Rev. Biomed. Eng. 8(3), 195-222 (1982
113. Beal SL, Sheiner LB. The NONMEN system. Am. Stat. 34, 118-119 (1980
114. Davidian M, Gallant AR. Smooth nonparametric maximum likelihood estimation for population pharmacokinetics, with application to quinidine. J. Pharmacokinet. Biopharm. 20(5), 529-556 (1992
115. Pillai GC, Mentré F, Steimer JL. Nonlinear mixed effects modeling - from methodology and software development to driving implementation in drug development science. J. Pharmacokinet. Pharmacodyn. 32(2), 161-183 (2005
116. Wakefield J, Bennett J. The bayesian modeling of covariates for population pharmacokinetic models. J. Am.Stat. Assoc. 91, 917-927 (1996
117. Gelman A, Rubin DB. Markov chain Monte Carlo methods in biostatistics. Stat. Methods Med. Res. 5(4), 339-355 (1996
118. Idkaidek N, Arafat T. Effect of microgravity on the pharmacokinetics of Ibuprofen in humans. J. Clin. Pharmacol. 51(12), 1685-1689 (2011
119. Vieira ML, Zhao P, Berglund EG et al. Predicting drug interaction potential with a physiologically based pharmacokinetic model: A case study of telithromycin, a time-dependent CYP3A inhibitor. Clin. Pharmacol. Ther. 91(4), 700-708 (2012
120. Brøsen K, Hansen JG, Nielsen KK, Sindrup SH, Gram LF. Inhibition by paroxetine of desipramine metabolism in extensive but not in poor metabolizers of sparteine. Eur. J. Clin. Pharmacol. 44(4), 349-355 (1993
121. Alderman J, Preskorn SH, Greenblatt DJ et al. Desipramine pharmacokinetics when coadministered with paroxetine or sertraline in extensive metabolizers. J. Clin. Psychopharmacol. 17(4), 284-291 (1997
122. Hemeryck A, Lefebvre RA, De Vriendt C, Belpaire FM. Paroxetine affects metoprolol pharmacokinetics and pharmacodynamics in healthy volunteers. Clin. Pharmacol. Ther. 67(3), 283-291 (2000
123. Belle DJ, Ernest CS, Sauer JM, Smith BP, Thomasson HR, Witcher JW. Effect of potent CYP2D6 inhibition by paroxetine on atomoxetine pharmacokinetics. J. Clin. Pharmacol. 42(11), 1219-1227 (2002
124. Kaye CM, Haddock RE, Langley PF et al. A review of the metabolism and pharmacokinetics of paroxetine in man. Acta Psychiatr. Scand. Suppl. 350, 60-75 (1989
125. Sindrup SH, Brøsen K, Gram LF et al. The relationship between paroxetine and the sparteine oxidation polymorphism. Clin. Pharmacol. Ther. 51(3), 278-287 (1992
126. Bertelsen KM, Venkatakrishnan K, Von Moltke LL, Obach RS, Greenblatt DJ. Apparent mechanism-based inhibition of human CYP2D6 in vitro by paroxetine: Comparison with fluoxetine and quinidine. Drug Metab. Dispos. 31(3), 289-293 (2003
127. Jornil J, Jensen KG, Larsen F, Linnet K. Identification of cytochrome P450 isoforms involved in the metabolism of paroxetine and estimation of their importance for human paroxetine metabolism using a population-based simulator. Drug Metab. Dispos. 38(3), 376-385 (2010
128. Venkatakrishnan K, Obach RS. In vitro-.in vivo extrapolation of CYP2D6 inactivation by paroxetine: Prediction of nonstationary pharmacokinetics and drug interaction magnitude. Drug Metab. Dispos. 33(6), 845-852 (2005
129. Yasui-Furukori N, Saito M, Inoue Y et al. Terbinafine increases the plasma concentration of paroxetine after a single oral administration of paroxetine in healthy subjects. Eur. J. Clin. Pharmacol. 63(1), 51-56 (2007
130. Dornhorst A. Insulinotropic meglitinide analogues. Lancet 358(9294), 1709-1716 (2001
131. Niemi M, Backman JT, Neuvonen M, Neuvonen PJ, Kivistö KT. Rifampin decreases the plasma concentrations and effects of repaglinide. Clin. Pharmacol. Ther. 68(5), 495-500 (2000
132. Kajosaari LI, Niemi M, Neuvonen M, Laitila J, Neuvonen PJ, Backman JT. Cyclosporine markedly raises the plasma concentrations of repaglinide. Clin. Pharmacol. Ther. 78(4), 388-399 (2005
133. Rostami-Hodjegan A, Tamai I, Pang KS. Physiologically based pharmacokinetic (PBPK) modeling: It is here to stay! Biopharm. Drug Dispos. 33(2), 47-50 (2012