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Rationalizing Underprediction of Drug Clearance from Enzyme and Transporter Kinetic Data: From In Vitro Tools to Mechanistic Modeling

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Enzyme Kinetics in Drug Metabolism

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1113))

Abstract

Over the years, there has been an increase in the number and quality of available in vitro tools for the assessment of clearance. Complexity of data analysis and modelling of corresponding in vitro data has increased in an analogous manner, in particular for the simultaneous characterization of transporter and metabolism kinetics, together with intracellular binding and passive diffusion. In the current chapter, the impact of different factors on the in vitro–in vivo extrapolation of clearance will be addressed in a stepwise manner, from the selection of the most adequate in vitro system and experimental design/condition to the corresponding modelling of data generated. The application of static or physiologically based pharmacokinetic models in the prediction of clearance will be discussed, highlighting limitations and current challenges of some of the approaches. Particular focus will be on the ability of in vitro and in silico predictive tools to overcome the trend of clearance underprediction. Improvements made as a result of inclusion of extrahepatic metabolism and consideration of transporter–metabolism interplay across different organs will be discussed.

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References

  1. Houston JB, Galetin A (2008) Methods for predicting in vivo pharmacokinetics using data from in vitro assays. Curr Drug Metab 9:940–951

    CAS  PubMed  Google Scholar 

  2. Chiba M, Ishii Y, Sugiyama Y (2009) Prediction of hepatic clearance in human from in vitro data for successful drug development. AAPS J 11:262–276

    CAS  PubMed  Google Scholar 

  3. Lave T, Chapman K, Goldsmith P et al (2009) Human clearance prediction: shifting the paradigm. Expert Opin Drug Metab Toxicol 5:1039–1048

    CAS  PubMed  Google Scholar 

  4. Miners JO, Knights KM, Houston JB et al (2006) In vitro-in vivo correlation for drugs and other compounds eliminated by glucuronidation in humans: pitfalls and promises. Biochem Pharmacol 71:1531–1539

    CAS  PubMed  Google Scholar 

  5. Ring BJ, Chien JY, Adkison KK et al (2011) PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: Comparative assessment of prediction methods of human clearance. J Pharm Sci. doi:10.1002/jps.22552, Epub ahead of print

    Google Scholar 

  6. Menochet K, Kenworthy KE, Houston JB et al (2012) Simultaneous assessment of uptake and metabolism in rat hepatocytes: a comprehensive mechanistic model. J Pharmacol Exp Ther 341:2–15

    CAS  PubMed  Google Scholar 

  7. Menochet K, Kenworthy KE, Houston JB et al (2012) Use of mechanistic modelling to assess inter-individual variability and inter-species differences in active uptake in human and rat hepatocytes. Drug Metab Dispos 40:1744–1756

    CAS  PubMed  Google Scholar 

  8. Kimoto E, Yoshida K, Balogh LM et al (2012) Characterization of organic anion transporting polypeptide (OATP) expression and its functional contribution to the uptake of substrates in human hepatocytes. Mol Pharm 9:3535–3542

    CAS  PubMed  Google Scholar 

  9. Kotani N, Maeda K, Watanabe T et al (2011) Culture period-dependent changes in the uptake of transporter substrates in sandwich-cultured rat and human hepatocytes. Drug Metab Dispos 39:1503–1510

    CAS  PubMed  Google Scholar 

  10. Yabe Y, Galetin A, Houston JB (2011) Kinetic characterization of rat hepatic uptake of 16 actively transported drugs. Drug Metab Dispos 39:1808–1814

    CAS  PubMed  Google Scholar 

  11. Badolo L, Rasmussen LM, Hansen HR et al (2011) Screening of OATP1B1/3 and OCT1 inhibitors in cryopreserved hepatocytes in suspension. Eur J Pharm Sci 40:282–288

    Google Scholar 

  12. Ulvestad M, Bjorquist P, Molden E et al (2011) OATP1B1/1B3 activity in plated primary human hepatocytes over time in culture. Biochem Pharmacol 82:1219–1226

    CAS  PubMed  Google Scholar 

  13. Ulvestad M, Darnell M, Molden E et al (2012) Evaluation of organic anion-transporting polypeptide 1B1 and CYP3A4 activities in primary human hepatocytes and HepaRG cells cultured in a dynamic three-dimensional bioreactor system. J Pharmacol Exp Ther 343:145–156

    CAS  PubMed  Google Scholar 

  14. Sivertsson L, Synnergren J, Jensen J et al (2013) Hepatic differentiation and maturation of human embryonic stem cells cultured in a perfused three-dimensional bioreactor. Stem Cells Dev 22:581–594. doi:10.1089/scd.2012.0202

    CAS  PubMed  Google Scholar 

  15. Darnell M, Ulvestad M, Ellis E et al (2012) In vitro evaluation of major in vivo drug metabolic pathways using primary human hepatocytes and HepaRG cells in suspension and a dynamic three-dimensional bioreactor system. J Pharmacol Exp Ther 343:134–144

    CAS  PubMed  Google Scholar 

  16. Zanelli U, Caradonna NP, Hallifax D et al (2012) Comparison of cryopreserved HepaRG cells with cryopreserved human hepatocytes for prediction of clearance for 26 drugs. Drug Metab Dispos 40:104–110

    CAS  PubMed  Google Scholar 

  17. Galetin A, Houston JB (2006) Intestinal and hepatic metabolic activity of five cytochrome P450 enzymes: impact on prediction of first-pass metabolism. J Pharmacol Exp Ther 318:1220–1229

    CAS  PubMed  Google Scholar 

  18. Gertz M, Harrison A, Houston JB et al (2010) Prediction of human intestinal first-pass metabolism of 25 CYP3A substrates from in vitro clearance and permeability data. Drug Metab Dispos 38:1147–1158

    CAS  PubMed  Google Scholar 

  19. Gill KL, Houston JB, Galetin A (2012) Characterization of in vitro glucuronidation clearance of a range of drugs in human kidney microsomes: comparison with liver and intestinal glucuronidation and impact of albumin. Drug Metab Dispos 40:825–835

    CAS  PubMed  Google Scholar 

  20. Gaganis P, Miners JO, Brennan JS et al (2007) Human renal cortical and medullary UDP-glucuronosyltransferases (UGTs): immunohistochemical localization of UGT2B7 and UGT1A enzymes and kinetic characterization of S-naproxen glucuronidation. J Pharmacol Exp Ther 323:422–430

    CAS  PubMed  Google Scholar 

  21. Cubitt HE, Houston JB, Galetin A (2011) Prediction of human drug clearance by multiple metabolic pathways: integration of hepatic and intestinal microsomal and cytosolic data. Drug Metab Dispos 39:864–873

    CAS  PubMed  Google Scholar 

  22. Camenisch G, Umehara K (2012) Predicting human hepatic clearance from in vitro drug metabolism and transport data: a scientific and pharmaceutical perspective for assessing drug-drug interactions. Biopharm Drug Dispos 33:179–194

    CAS  PubMed  Google Scholar 

  23. Gill KL, Gertz M, Houston JB et al (2013) Application of a physiologically-based pharmacokinetic model to assess propofol hepatic and renal glucuronidation in isolation; utility of in vitro and in vivo data. Drug Metab Dispos 41:744–753

    CAS  PubMed  Google Scholar 

  24. Obach RS (1999) 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 27:1350–1359

    CAS  PubMed  Google Scholar 

  25. Hallifax D, Foster JA, Houston JB (2010) Prediction of human metabolic clearance from in vitro systems: retrospective analysis and prospective view. Pharm Res 27:2150–2161

    CAS  PubMed  Google Scholar 

  26. Riley RJ, McGinnity DF, Austin RP (2005) A unified model for predicting human hepatic, metabolic clearance from in vitro intrinsic clearance data in hepatocytes and microsomes. Drug Metab Dispos 33:1304–1311

    CAS  PubMed  Google Scholar 

  27. Kilford PJ, Stringer R, Sohal B et al (2009) Prediction of drug clearance by glucuronidation from in vitro data: use of combined cytochrome P450 and UDP-glucuronosyltransferase cofactors in alamethicin-activated human liver microsomes. Drug Metab Dispos 37:82–89

    CAS  PubMed  Google Scholar 

  28. Watanabe T, Kusuhara H, Maeda K et al (2010) Investigation of the rate-determining process in the hepatic elimination of HMG-CoA reductase inhibitors in rats and humans. Drug Metab Dispos 38:215–222

    CAS  PubMed  Google Scholar 

  29. Engtrakul JJ, Foti RS, Strelevitz TJ et al (2005) Altered AZT (3′-azido-3′-deoxythymidine) glucuronidation kinetics in liver microsomes as an explanation for underprediction of in vivo clearance: comparison to hepatocytes and effect of incubation environment. Drug Metab Dispos 33:1621–1627

    CAS  PubMed  Google Scholar 

  30. Soars MG, Burchell B, Riley RJ (2002) In vitro analysis of human drug glucuronidation and prediction of in vivo metabolic clearance. J Pharmacol Exp Ther 301:382–390. doi:10.1124/jpet.301.1.382

    CAS  PubMed  Google Scholar 

  31. Cubitt HE, Houston JB, Galetin A (2009) Relative importance of intestinal and hepatic glucuronidation-impact on the prediction of drug clearance. Pharm Res 26:1073–1083

    CAS  PubMed  Google Scholar 

  32. Pang KS, Rowland M (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 blood cell binding, and the hepatocellular enzymatic activity on hepatic drug clearance. J Pharmacokinet Biopharm 5:625–653

    CAS  PubMed  Google Scholar 

  33. Rowland M, Peck C, Tucker G (2011) Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol 51:45–73

    CAS  PubMed  Google Scholar 

  34. Gertz M, Houston JB, Galetin A (2011) Physiologically based pharmacokinetic modeling of intestinal first-pass metabolism of CYP3A substrates with high intestinal extraction. Drug Metab Dispos 39:1633–1642

    CAS  PubMed  Google Scholar 

  35. Rostami-Hodjegan A (2012) Physiologically based pharmacokinetics joined with in vitro-in vivo extrapolation of ADME: a marriage under the arch of systems pharmacology. Clin Pharmacol Ther 92:50–61

    CAS  PubMed  Google Scholar 

  36. Huang SM, Rowland M (2012) The role of physiologically based pharmacokinetic modeling in regulatory review. Clin Pharmacol Ther 91:542–549

    CAS  PubMed  Google Scholar 

  37. Zamek-Gliszczynski MJ, Lee CA, Poirier A et al (2013) ITC recommendations on transporter kinetic parameter estimation and translational modeling of transport-mediated PK and DDIs in humans. Clin Pharmacol Ther 94:64–79. doi:10.1038/clpt.2013.45

    PubMed Central  Google Scholar 

  38. Brown HS, Chadwick A, Houston JB (2007) Use of isolated hepatocyte preparations for cytochrome P450 inhibition studies: comparison with microsomes for Ki determination. Drug Metab Dispos 35:2119–2126

    CAS  PubMed  Google Scholar 

  39. Hallifax D, Galetin A, Houston JB (2008) Prediction of metabolic clearance using fresh human hepatocytes: comparison with cryopreserved hepatocytes and hepatic microsomes for five benzodiazepines. Xenobiotica 38:353–367

    CAS  PubMed  Google Scholar 

  40. Soars MG, McGinnity DF, Grime K et al (2007) The pivotal role of hepatocytes in drug discovery. Chem Biol Interact 168:2–15

    CAS  PubMed  Google Scholar 

  41. Di L, Keefer C, Scott DO et al (2012) Mechanistic insights from comparing intrinsic clearance values between human liver microsomes and hepatocytes to guide drug design. Eur J Med Chem 57:441–448

    CAS  PubMed  Google Scholar 

  42. Kuester RK, Sipes IG (2007) Prediction of metabolic clearance of bisphenol A (4,4’-dihydroxy-2,2-diphenylpropane) using cryopreserved human hepatocytes. Drug Metab Dispos 35:1910–1915

    CAS  PubMed  Google Scholar 

  43. Hallifax D, Turlizzi E, Zanelli U et al (2012) Clearance-dependent underprediction of in vivo intrinsic clearance from human hepatocytes: comparison with permeabilities from artificial membrane (PAMPA) assay, in silico and caco-2 assay, for 65 drugs. Eur J Pharm Sci 45:570–574

    CAS  PubMed  Google Scholar 

  44. Ito K, Houston JB (2005) Prediction of human drug clearance from in vitro and preclinical data using physiologically based and empirical approaches. Pharm Res 22:103–112

    CAS  PubMed  Google Scholar 

  45. Di L, Trapa P, Obach RS et al (2012) A novel relay method for determining low-clearance values. Drug Metab Dispos 40:1860–1865

    CAS  PubMed  Google Scholar 

  46. Guillouzo A, Corlu A, Aninat C et al (2007) The human hepatoma HepaRG cells: a highly differentiated model for studies of liver metabolism and toxicity of xenobiotics. Chem Biol Interact 168:66–73

    CAS  PubMed  Google Scholar 

  47. Kanebratt KP, Andersson TB (2008) HepaRG cells as an in vitro model for evaluation of cytochrome P450 induction in humans. Drug Metab Dispos 36:137–145

    CAS  PubMed  Google Scholar 

  48. Mohutsky MA, Chien JY, Ring BJ et al (2006) Predictions of the in vivo clearance of drugs from rate of loss using human liver microsomes for phase I and phase II biotransformations. Pharm Res 23:654–662

    CAS  PubMed  Google Scholar 

  49. Sall C, Houston JB, Galetin A (2012) A comprehensive assessment of repaglinide metabolic pathways: impact of choice of in vitro system and relative enzyme contribution to in vitro clearance. Drug Metab Dispos 40:1279–1289

    CAS  PubMed  Google Scholar 

  50. Zientek M, Jiang Y, Youdim K et al (2010) In vitro-in vivo correlation for intrinsic clearance for drugs metabolized by human aldehyde oxidase. Drug Metab Dispos 38:1322–1327

    CAS  PubMed  Google Scholar 

  51. Hutzler JM, Yang YS, Albaugh D et al (2012) Characterization of aldehyde oxidase enzyme activity in cryopreserved human hepatocytes. Drug Metab Dispos 40:267–275

    CAS  PubMed  Google Scholar 

  52. Pryde DC, Dalvie D, Hu Q et al (2010) Aldehyde oxidase: an enzyme of emerging importance in drug discovery. J Med Chem 53:8441–8460

    CAS  PubMed  Google Scholar 

  53. Rowland A, Gaganis P, Elliot DJ et al (2007) Binding of inhibitory fatty acids is responsible for the enhancement of UDP-glucuronosyltransferase 2B7 activity by albumin: implications for in vitro-in vivo extrapolation. J Pharmacol Exp Ther 321:137–147

    CAS  PubMed  Google Scholar 

  54. Fisher MB, Campanale K, Ackermann BL et al (2000) In vitro glucuronidation using human liver microsomes and the pore-forming peptide alamethicin. Drug Metab Dispos 28:560–566

    CAS  PubMed  Google Scholar 

  55. Rowland A, Knights KM, Mackenzie PI et al (2008) The “albumin effect” and drug glucuronidation: bovine serum albumin and fatty acid-free human serum albumin enhance the glucuronidation of UDP-glucuronosyltransferase (UGT) 1A9 substrates but not UGT1A1 and UGT1A6 activities. Drug Metab Dispos 36:1056–1062

    CAS  PubMed  Google Scholar 

  56. Carlile DJ, Hakooz N, Bayliss MK et al (1999) Microsomal prediction of in vivo clearance of CYP2C9 substrates in humans. Br J Clin Pharmacol 47:625–635, doi:bcp935 [pii]

    CAS  PubMed Central  PubMed  Google Scholar 

  57. Webborn PJ, Parker AJ, Denton RL et al (2007) In vitro-in vivo extrapolation of hepatic clearance involving active uptake: theoretical and experimental aspects. Xenobiotica 37:1090–1109

    CAS  PubMed  Google Scholar 

  58. Baker M, Parton T (2007) Kinetic determinants of hepatic clearance: plasma protein binding and hepatic uptake. Xenobiotica 37:1110–1134

    CAS  PubMed  Google Scholar 

  59. Sugano K, Kansy M, Artursson P et al (2010) Coexistence of passive and carrier-mediated processes in drug transport. Nat Rev Drug Discov 9:597–614

    CAS  PubMed  Google Scholar 

  60. Yoshida K, Maeda K, Sugiyama Y (2013) Hepatic and intestinal drug transporters: prediction of pharmacokinetic effects caused by drug-drug interactions and genetic polymorphisms. Annu Rev Pharmacol Toxicol 53:581–612

    CAS  PubMed  Google Scholar 

  61. Giacomini KM, Huang SM, Tweedie DJ et al (2010) Membrane transporters in drug development. Nat Rev Drug Discov 9:215–236

    CAS  PubMed  Google Scholar 

  62. Chu X, Korzekwa KR, Elsby R et al (2013) Intracellular drug concentrations and transporters: measurement, modeling and implications in the liver. Clin Pharmacol Ther 94:126–141. doi:10.1038/clpt.2013.78

    Google Scholar 

  63. Poirier A, Lave T, Portmann R et al (2008) Design, data analysis, and simulation of in vitro drug transport kinetic experiments using a mechanistic in vitro model. Drug Metab Dispos 36:2434–2444

    CAS  PubMed  Google Scholar 

  64. Parker AJ, Houston JB (2008) Rate-limiting steps in hepatic drug clearance: comparison of hepatocellular uptake and metabolism with microsomal metabolism of saquinavir, nelfinavir, and ritonavir. Drug Metab Dispos 36:1375–1384

    CAS  PubMed  Google Scholar 

  65. Soars MG, Grime K, Sproston JL et al (2007) Use of hepatocytes to assess the contribution of hepatic uptake to clearance in vivo. Drug Metab Dispos 35:859–865

    CAS  PubMed  Google Scholar 

  66. Jigorel E, Houston JB (2012) Utility of drug depletion-time profiles in isolated hepatocytes for accessing hepatic uptake clearance: identifying rate-limiting steps and role of passive processes. Drug Metab Dispos 40:1596–1602

    CAS  PubMed  Google Scholar 

  67. Jones HM, Barton HA, Lai Y et al (2012) Mechanistic pharmacokinetic modeling for the prediction of transporter-mediated disposition in humans from sandwich culture human hepatocyte data. Drug Metab Dispos 40:1007–1017

    CAS  PubMed  Google Scholar 

  68. Houston JB (1994) Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance. Biochem Pharmacol 47:1469–1479

    CAS  PubMed  Google Scholar 

  69. Barter ZE, Bayliss MK, Beaune PH et al (2007) 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 8:33–45

    CAS  PubMed  Google Scholar 

  70. Paine MF, Khalighi M, Fisher JM et al (1997) Characterization of interintestinal and intraintestinal variations in human CYP3A-dependent metabolism. J Pharmacol Exp Ther 283:1552–1562

    CAS  PubMed  Google Scholar 

  71. Aitio A, Vainio H (1976) UDP glucuronosyltransferase and mixed function oxidase activity in microsomes prepared by differential centrifugation and calcium aggregation. Acta Pharmacol Toxicol 39:555–561

    CAS  Google Scholar 

  72. Al-Jahdari WS, Yamamoto K, Hiraoka H et al (2006) Prediction of total propofol clearance based on enzyme activities in microsomes from human kidney and liver. Eur J Clin Pharmacol 62:527–533

    CAS  PubMed  Google Scholar 

  73. Nestorov I (2007) Whole-body physiologically based pharmacokinetic models. Expert Opin Drug Metab Toxicol 3:235–249

    CAS  PubMed  Google Scholar 

  74. Ito K, Houston JB (2004) Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes. Pharm Res 21:785–792

    CAS  PubMed  Google Scholar 

  75. Shitara Y, Sugiyama Y (2006) Pharmacokinetic and pharmacodynamic alterations of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors: drug-drug interactions and interindividual differences in transporter and metabolic enzyme functions. Pharmacol Ther 112:71–105

    CAS  PubMed  Google Scholar 

  76. Poulin P, Hop CE, Ho Q et al (2012) Comparative assessment of In Vitro-In Vivo extrapolation methods used for predicting hepatic metabolic clearance of drugs. J Pharm Sci 101:4308–4326

    CAS  PubMed  Google Scholar 

  77. Hallifax D, Houston JB (2012) Evaluation of hepatic clearance prediction using in vitro data: emphasis on fraction unbound in plasma and drug ionisation using a database of 107 drugs. J Pharm Sci 101:2645–2652

    CAS  PubMed  Google Scholar 

  78. De Buck SS, Sinha VK, Fenu LA et al (2007) Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs. Drug Metab Dispos 35:1766–1780

    PubMed  Google Scholar 

  79. Jones HM, Parrott N, Jorga K et al (2006) A novel strategy for physiologically based predictions of human pharmacokinetics. Clin Pharmacokinet 45:511–542

    CAS  PubMed  Google Scholar 

  80. Zhao P, Rowland M, Huang SM (2012) Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions. Clin Pharmacol Ther 92:17–20

    CAS  PubMed  Google Scholar 

  81. International Commission on Radiological Protection (2002) Basic anatomical and physiological data for use in radiological protection: reference values. A report of age- and gender-related differences in the anatomical and physiological characteristics of reference individuals. ICRP publication 89. Ann ICRP 32:5–265

    Google Scholar 

  82. Brown RP, Delp MD, Lindstedt SL et al (1997) Physiological parameter values for physiologically based pharmacokinetic models. Toxicol Ind Health 13:407–484

    CAS  PubMed  Google Scholar 

  83. Davies B, Morris T (1993) Physiological parameters in laboratory animals and humans. Pharm Res 10:1093–1095

    CAS  PubMed  Google Scholar 

  84. Jamei M, Dickinson GL, Rostami-Hodjegan A (2009) 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 24:53–75

    CAS  PubMed  Google Scholar 

  85. Nestorov IA, Aarons LJ, Arundel PA et al (1998) Lumping of whole-body physiologically based pharmacokinetic models. J Pharmacokinet Biopharm 26:21–46

    CAS  PubMed  Google Scholar 

  86. Bjorkman S (2003) Reduction and lumping of physiologically based pharmacokinetic models: prediction of the disposition of fentanyl and pethidine in humans by successively simplified models. J Pharmacokinet Pharmacodyn 30:285–307

    PubMed  Google Scholar 

  87. Poulin P, Jones RD, Jones HM et al (2011) PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: prediction of plasma concentration-time profiles in human by using the physiologically-based pharmacokinetic modeling approach. J Pharm Sci. doi:10.1002/jps.22550, Epub ahead of print

    Google Scholar 

  88. Parrott N, Lave T (2008) Applications of physiologically based absorption models in drug discovery and development. Mol Pharm 5:760–775

    CAS  PubMed  Google Scholar 

  89. Jamei M, Marciniak S, Feng K et al (2009) The Simcyp population-based ADME simulator. Expert Opin Drug Metab Toxicol 5:211–223

    CAS  PubMed  Google Scholar 

  90. Zhao P, Zhang L, Grillo JA et al (2011) Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review. Clin Pharmacol Ther 89:259–267

    CAS  PubMed  Google Scholar 

  91. Jones HM, Gardner IB, Collard WT et al (2011) Simulation of human intravenous and oral pharmacokinetics of 21 diverse compounds using physiologically based pharmacokinetic modelling. Clin Pharmacokinet 50:331–347

    CAS  PubMed  Google Scholar 

  92. Varma MV, Lai Y, Feng B et al (2012) Physiologically based modeling of pravastatin transporter-mediated Hepatobiliary disposition and drug–drug interactions. Pharm Res 29(10):2860–2873

    CAS  PubMed  Google Scholar 

  93. Gertz M, Cartwright CM, Hobbs MJ et al (2013) Application of PBPK modeling in the assessment of the interaction potential of cyclosporine against hepatic and intestinal uptake and efflux transporters and CYP3A4. Pharm Res 30:761–780

    CAS  PubMed  Google Scholar 

  94. Chen Y, Jin JY, Mukadam S et al (2012) 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 33:85–98

    PubMed  Google Scholar 

  95. Jones HM, Dickins M, Youdim K et al (2012) Application of PBPK modelling in drug discovery and development at Pfizer. Xenobiotica 42:94–106

    CAS  PubMed  Google Scholar 

  96. Sinha VK, Snoeys J, Osselaer NV et al (2012) 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 33:111–121

    CAS  PubMed  Google Scholar 

  97. Bouzom F, Ball K, Perdaems N et al (2012) Physiologically based pharmacokinetic (PBPK) modelling tools: how to fit with our needs? Biopharm Drug Dispos 33:55–71

    CAS  PubMed  Google Scholar 

  98. Rostami-Hodjegan A, Tucker GT (2007) Simulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nat Rev Drug Discov 6:140

    CAS  PubMed  Google Scholar 

  99. Ghobadi C, Johnson TN, Aarabi M et al (2011) Application of a systems approach to the bottom-up assessment of pharmacokinetics in obese patients: expected variations in clearance. Clin Pharmacokinet 50:809–822

    CAS  PubMed  Google Scholar 

  100. Darwich AS, Pade D, Ammori BJ et al (2012) A mechanistic pharmacokinetic model to assess modified oral drug bioavailability post bariatric surgery in morbidly obese patients: interplay between CYP3A gut wall metabolism, permeability and dissolution. J Pharm Pharmacol 64:1008–1024

    CAS  PubMed  Google Scholar 

  101. Edginton AN, Willmann S (2008) Physiology-based simulations of a pathological condition: prediction of pharmacokinetics in patients with liver cirrhosis. Clin Pharmacokinet 47:743–752

    PubMed  Google Scholar 

  102. Grillo JA, Zhao P, Bullock J et al (2012) 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 33:99–110

    CAS  PubMed  Google Scholar 

  103. Watanabe T, Kusuhara H, Maeda K et al (2009) Physiologically based pharmacokinetic modeling to predict transporter-mediated clearance and distribution of pravastatin in humans. J Pharmacol Exp Ther 328:652–662

    CAS  PubMed  Google Scholar 

  104. Poirier A, Funk C, Scherrmann JM et al (2009) Mechanistic modeling of hepatic transport from cells to whole body: application to napsagatran and fexofenadine. Mol Pharm 6:1716–1733

    CAS  PubMed  Google Scholar 

  105. Dalvie D, Kang P, Zientek M et al (2008) Effect of intestinal glucuronidation in limiting hepatic exposure and bioactivation of raloxifene in humans and rats. Chem Res Toxicol 21:2260–2271

    CAS  PubMed  Google Scholar 

  106. Yu LX, Amidon GL (1999) A compartmental absorption and transit model for estimating oral drug absorption. Int J Pharm 186:119–125

    CAS  PubMed  Google Scholar 

  107. Dokoumetzidis A, Kalantzi L, Fotaki N (2007) Predictive models for oral drug absorption: from in silico methods to integrated dynamical models. Expert Opin Drug Metab Toxicol 3:491–505

    CAS  PubMed  Google Scholar 

  108. Jamei M, Turner D, Yang J et al (2009) Population-based mechanistic prediction of oral drug absorption. AAPS J 11:225–237

    CAS  PubMed  Google Scholar 

  109. Pang KS, Chow EC (2012) Commentary: theoretical predictions of flow effects on intestinal and systemic availability in physiologically based pharmacokinetic intestine models: the traditional model, segregated flow model, and QGut model. Drug Metab Dispos 40:1869–1877

    CAS  PubMed  Google Scholar 

  110. Chalasani N, Gorski JC, Patel NH et al (2001) Hepatic and intestinal cytochrome P450 3A activity in cirrhosis: effects of transjugular intrahepatic portosystemic shunts. Hepatology 34:1103–1108

    CAS  PubMed  Google Scholar 

  111. Yang J, Jamei M, Yeo KR et al (2007) Prediction of intestinal first-pass drug metabolism. Curr Drug Metab 8:676–684

    CAS  PubMed  Google Scholar 

  112. Galetin A, Gertz M, Houston JB (2010) Contribution of intestinal cytochrome P450-mediated metabolism to drug-drug inhibition and induction interactions. Drug Metab Pharmacokinet 25:28–47

    CAS  PubMed  Google Scholar 

  113. Agoram B, Woltosz WS, Bolger MB (2001) Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv Drug Deliv Rev 50(Suppl 1):S41–67

    CAS  PubMed  Google Scholar 

  114. Tam D, Tirona RG, Pang KS (2003) Segmental intestinal transporters and metabolic enzymes on intestinal drug absorption. Drug Metab Dispos 31:373–383

    CAS  PubMed  Google Scholar 

  115. Pang KS (2003) Modeling of intestinal drug absorption: roles of transporters and metabolic enzymes (for the Gillette review series). Drug Metab Dispos 31:1507–1519

    CAS  PubMed  Google Scholar 

  116. Lennernas H (2007) Intestinal permeability and its relevance for absorption and elimination. Xenobiotica 37:1015–1051

    CAS  PubMed  Google Scholar 

  117. Yu LX, Lipka E, Crison JR et al (1996) Transport approaches to the biopharmaceutical design of oral drug delivery systems: prediction of intestinal absorption. Adv Drug Deliv Rev 19:359–376

    CAS  PubMed  Google Scholar 

  118. Paine MF, Hart HL, Ludington SS et al (2006) The human intestinal cytochrome P450 “pie”. Drug Metab Dispos 34:880–886

    CAS  PubMed Central  PubMed  Google Scholar 

  119. Berggren S, Gall C, Wollnitz N et al (2007) Gene and protein expression of P-glycoprotein, MRP1, MRP2, and CYP3A4 in the small and large human intestine. Mol Pharm 4:252–257

    CAS  PubMed  Google Scholar 

  120. Tucker TG, Milne AM, Fournel-Gigleux S et al (2012) Absolute immunoquantification of the expression of ABC transporters P-glycoprotein, breast cancer resistance protein and multidrug resistance-associated protein 2 in human liver and duodenum. Biochem Pharmacol 83:279–285

    CAS  PubMed  Google Scholar 

  121. Harwood MD, Neuhoff S, Carlson GL et al (2012) 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 34:2–28

    PubMed  Google Scholar 

  122. Rowland Yeo K, Walsky RL, Jamei M et al (2011) Prediction of time-dependent CYP3A4 drug-drug interactions by physiologically based pharmacokinetic modelling: impact of inactivation parameters and enzyme turnover. Eur J Pharm Sci 43:160–173

    CAS  PubMed  Google Scholar 

  123. Korzekwa KR, Nagar S, Tucker J et al (2012) Models to predict unbound intracellular drug concentrations in the presence of transporters. Drug Metab Dispos 40:865–876

    CAS  PubMed  Google Scholar 

  124. Hiraoka H, Yamamoto K, Miyoshi S et al (2005) Kidneys contribute to the extrahepatic clearance of propofol in humans, but not lungs and brain. Br J Clin Pharmacol 60:176–182

    CAS  PubMed Central  PubMed  Google Scholar 

  125. Lee JK, Marion TL, Abe K et al (2010) Hepatobiliary disposition of troglitazone and metabolites in rat and human sandwich-cultured hepatocytes: use of Monte Carlo simulations to assess the impact of changes in biliary excretion on troglitazone sulfate accumulation. J Pharmacol Exp Ther 332:26–34

    CAS  PubMed  Google Scholar 

  126. Kusuhara H, Sugiyama Y (2010) Pharmacokinetic modeling of the hepatobiliary transport mediated by cooperation of uptake and efflux transporters. Drug Metab Rev 42:539–550

    CAS  PubMed  Google Scholar 

  127. Shitara Y, Maeda K, Ikejiri K et al (2013) Clinical significance of organic anion transporting polypeptides (OATPs) in drug disposition: their roles in hepatic clearance and intestinal absorption. Biopharm Drug Dispos 34:45–78

    CAS  PubMed  Google Scholar 

  128. Nestorov I (2003) Whole body pharmacokinetic models. Clin Pharmacokinet 42:883–908

    CAS  PubMed  Google Scholar 

  129. Poirier A, Cascais AC, Funk C et al (2009) Prediction of pharmacokinetic profile of valsartan in human based on in vitro uptake transport data. J Pharmacokinet Pharmacodyn 36:585–611

    CAS  PubMed  Google Scholar 

  130. Sasso AF, Schlosser PM, Kedderis GL et al (2012) Application of an updated physiologically based pharmacokinetic model for chloroform to evaluate CYP2E1-mediated renal toxicity in rats and mice. Toxicol Sci 131:360–374

    PubMed  Google Scholar 

  131. Ball K, Bouzom F, Scherrmann JM et al (2012) Development of a physiologically based pharmacokinetic model for the rat central nervous system and determination of an in vitro-in vivo scaling methodology for the blood-brain barrier permeability of two transporter substrates, morphine and oxycodone. J Pharm Sci 101:4277–4292

    CAS  PubMed  Google Scholar 

  132. Burt HJ, Neuhoff S, Lu G et al (2012) Simulation of the effect of urine pH on renal drug clearance using a novel population based mechanistic kidney model (Mech KiM). In: Gordon Research Conference – Drug Metabolism, Holderness. NH, USA, 8–13 July 2012

    Google Scholar 

  133. Varma MV, Lai Y, Kimoto E et al (2013) Mechanistic modeling to predict the transporter- and enzyme-mediated drug-drug interactions of repaglinide. Pharm Res 30(4):1188–1199

    CAS  PubMed  Google Scholar 

  134. Bi YA, Kimoto E, Sevidal S et al (2012) In vitro evaluation of hepatic transporter-mediated clinical drug-drug interactions: hepatocyte model optimization and retrospective investigation. Drug Metab Dispos 40:1085–1092

    CAS  PubMed  Google Scholar 

  135. Ohtsuki S, Schaefer O, Kawakami H et al (2012) 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 40:83–92

    CAS  PubMed  Google Scholar 

  136. Schaefer O, Ohtsuki S, Kawakami H et al (2012) Absolute quantification and differential expression of drug transporters, cytochrome P450 enzymes, and UDP-glucuronosyltransferases in cultured primary human hepatocytes. Drug Metab Dispos 40:93–103

    CAS  PubMed  Google Scholar 

  137. van de Steeg E, Greupink R, Schreurs M et al (2013) Drug-drug interactions between rosuvastatin and oral antidiabetic drugs occurring at the level of OATP1B1. Drug Metab Dispos 41(3):592–601

    PubMed  Google Scholar 

  138. Takashima T, Hashizume Y, Katayama Y et al (2011) The involvement of organic anion transporting polypeptide in the hepatic uptake of telmisartan in rats: PET studies with [11C] telmisartan. Mol Pharm 8:1789–1798

    CAS  PubMed  Google Scholar 

  139. Kusuhara H, Sugiyama Y (2009) In vitro-in vivo extrapolation of transporter-mediated clearance in the liver and kidney. Drug Metab Pharmacokinet 24:37–52

    CAS  PubMed  Google Scholar 

  140. Quinney SK, Zhang X, Lucksiri A et al (2010) Physiologically based pharmacokinetic model of mechanism-based inhibition of CYP3A by clarithromycin. Drug Metab Dispos 38:241–248

    CAS  PubMed  Google Scholar 

  141. Ito K, Ogihara K, Kanamitsu S et al (2003) Prediction of the in vivo interaction between midazolam and macrolides based on in vitro studies using human liver microsomes. Drug Metab Dispos 31:945–954

    CAS  PubMed  Google Scholar 

  142. Zhang X, Quinney SK, Gorski JC et al (2009) Semiphysiologically based pharmacokinetic models for the inhibition of midazolam clearance by diltiazem and its major metabolite. Drug Metab Dispos 37:1587–1597

    CAS  PubMed  Google Scholar 

  143. Quinney SK, Galinsky RE, Jiyamapa-Serna VA et al (2008) Hydroxyitraconazole, formed during intestinal first-pass metabolism of itraconazole, controls the time course of hepatic CYP3A inhibition and the bioavailability of itraconazole in rats. Drug Metab Dispos 36:1097–1101

    CAS  PubMed  Google Scholar 

  144. Rowland A, Elliot DJ, Knights KM et al (2008) The “albumin effect” and in vitro-in vivo extrapolation: sequestration of long-chain unsaturated fatty acids enhances phenytoin hydroxylation by human liver microsomal and recombinant cytochrome P450 2C9. Drug Metab Dispos 36:870–877

    CAS  PubMed  Google Scholar 

  145. Obach RS (1996) The importance of nonspecific binding in in vitro matrices, its impact on enzyme kinetic studies of drug metabolism reactions, and implications for in vitro-in vivo correlations. Drug Metab Dispos 24:1047–1049

    CAS  PubMed  Google Scholar 

  146. Gertz M, Kilford PJ, Houston JB et al (2008) Drug lipophilicity and microsomal protein concentration as determinants in the prediction of the fraction unbound in microsomal incubations. Drug Metab Dispos 36:535–542

    CAS  PubMed  Google Scholar 

  147. Kilford PJ, Gertz M, Houston JB et al (2008) Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data. Drug Metab Dispos 36:1194–1197

    CAS  PubMed  Google Scholar 

  148. Zhang H, Cui D, Wang B et al (2007) Pharmacokinetic drug interactions involving 17alpha-ethinylestradiol: a new look at an old drug. Clin Pharmacokinet 46:133–157

    CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Manchester Pharmacy School, The University of Manchester, Stopford Building, Oxford Road, Manchester, UK

    Aleksandra Galetin

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  1. Aleksandra Galetin
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Editors and Affiliations

  1. Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, Pennsylvania, USA

    Swati Nagar

  2. Novartis Inst. for BioMedical Research, Cambridge, Massachusetts, USA

    Upendra A. Argikar

  3. Drug Metabolism, Boehringer Ingelheim, Ridgefield, Connecticut, USA

    Donald J. Tweedie

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Galetin, A. (2014). Rationalizing Underprediction of Drug Clearance from Enzyme and Transporter Kinetic Data: From In Vitro Tools to Mechanistic Modeling. In: Nagar, S., Argikar, U., Tweedie, D. (eds) Enzyme Kinetics in Drug Metabolism. Methods in Molecular Biology, vol 1113. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-758-7_13

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  • DOI: https://doi.org/10.1007/978-1-62703-758-7_13

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