Clinical Pharmacokinetics

, Volume 50, Issue 12, pp 809–822 | Cite as

Application of a Systems Approach to the Bottom-Up Assessment of Pharmacokinetics in Obese Patients

Expected Variations in Clearance
  • Cyrus Ghobadi
  • Trevor N. Johnson
  • Mohsen Aarabi
  • Lisa M. Almond
  • Aurel Constant Allabi
  • Karen Rowland-Yeo
  • Masoud Jamei
  • Amin Rostami-Hodjegan
Original Research Article

Abstract

Background and Objectives: The maintenance dose of a drug is dependent on drug clearance, and thus any biochemical and physiological changes in obesity that affect parameters such as cardiac output, renal function, expression of drug-metabolizing enzymes and protein binding may result in altered clearance compared with that observed in normal-weight subjects (corrected or uncorrected for body weight). Because of the increasing worldwide incidence of obesity, there is a need for more information regarding the optimal dosing of drug therapy to be made available to prescribers. This is usually provided via clinical studies in obese people; however, such studies are not available for all drugs that might be used in obese subjects. Incorporation of the relevant physiological and biochemical changes into predictive bottom-up pharmacokinetic models in order to optimize dosage regimens may offer a logical way forward for the cases where no clinical data exist. The aims of the current report are to apply such a ‘systems approach’ to identify the likelihood of observing variations in the clearance of drugs in obesity and morbid obesity for a set of compounds for which clinical data, as well as the necessary in vitro information, are available, and to provide a framework for assessing other drugs in the future.

Methods: The population-specific changes in demographic, physiological and biochemical parameters that are known to be relevant to obese and morbidly obese subjects were collated and incorporated into two separate population libraries. These libraries, together with mechanistic in vitro-in vivo extrapolations (IVIVE) within the Simcyp Population-based Simulator™, were used to predict the clearance of oral alprazolam, oral caffeine, oral chlorzoxazone, oral ciclosporin, intravenous and oral midazolam, intravenous phenytoin, oral theophylline and oral triazolam. The design of the simulated studies was matched as closely as possible with that of the clinical studies. Outcome was measured by the predicted ratio of the clearance of the drug in obese and lean subjects ± its 90% confidence interval, compared with observed values. The overall statistical measures of the performance of the model to detect differences in compound clearance between obese and lean populations were investigated by measuring sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). A power calculation was carried out to investigate the impact of the sample size on the overall outcome of clinical studies.

Results: The model was successful in predicting clearance in obese subjects, with the degree to which simulations could mimic the outcome of in vivo studies being greater than 60% for six of the eight drugs. A clear difference in the clearance of chlorzoxazone was correctly picked up via simulation. The overall statistical measures of the performance of the Simcyp Simulator were 100% sensitivity, 66% specificity, 60% PPV and 100% NPV. Studies designed on the basis of the ratio of the absolute values required substantial numbers of participants in order to detect a significant difference, except for phenytoin and chlorzoxazone, where the ratios of the weight-normalized clearances generally showed statistically significant differences with a smaller number of subjects.

Conclusion: Extension of a mechanistic predictive pharmacokinetic model to accommodate physiological and biochemical changes associated with obesity and morbid obesity allowed prediction of changes in drug clearance on the basis of in vitro data, with reasonable accuracy across a range of compounds that are metabolized by different enzymes. Prediction of the effects of obesity on drug clearance, normalized by various body size scalars, is of potential value in the design of clinical studies during drug development and in the introduction of dosage adjustments that are likely to be needed in clinical practice.

Notes

Acknowledgements

All members of the Simcyp science team are acknowledged for the useful discussions they provided for preparation of this article. This work was funded by Simcyp Limited. Trevor N. Johnson, Lisa M. Almond, Karen Rowland-Yeo and Masoud Jamei are employees of Simcyp Limited. Cyrus Ghobadi, Mohsen Aarabi and Aurel Allabi are former employees of Simcyp Limited. Amin Rostami-Hodjegan is seconded part-time from the University of Manchester to Simcyp Limited. Trevor N. Johnson, Lisa M. Almond, Karen Rowland-Yeo, Masoud Jamei and Amin Rostami-Hodjegan hold shares in Simcyp Limited. The Simcyp Simulator is available, after training costs only, to approved members of academic institutions and other non-profit organizations for research and teaching purposes.

Supplementary material

40262_2012_50120809_MOESM1_ESM.pdf (449 kb)
Supplementary material, approximately 460 KB.

References

  1. 1.
    Williams JA, Johnson K, Paulauskis J, et al. So many studies, too few subjects: establishing functional relevance of genetic polymorphisms on pharmacokinetics. J Clin Pharmacol 2006 Mar; 46(3): 258–64PubMedCrossRefGoogle Scholar
  2. 2.
    Ette EI, Williams PJ. Pharmacometrics: the science of quantitative pharmacology. London: Wiley, 2007CrossRefGoogle Scholar
  3. 3.
    Karlsson MO, Jonsson EN, Wiltse CG, et al. Assumption testing in population pharmacokinetic models: illustrated with an analysis of moxonidine data from congestive heart failure patients. J Pharmacokinet Biopharm 1998 Apr; 26(2): 207–46PubMedCrossRefGoogle Scholar
  4. 4.
    Edginton AN, Theil FP, Schmitt W, et al. Whole body physiologically-based pharmacokinetic models: their use in clinical drug development. Expert Opin Drug Metab Toxicol 2008 Sep; 4(9): 1143–52PubMedCrossRefGoogle Scholar
  5. 5.
    Johnson TN, Boussery K, Rowland-Yeo K, et al. A semi-mechanistic model to predict the effects of liver cirrhosis on drug clearance. Clin Pharmacokinet 2010 Mar 1; 49(3): 189–206PubMedCrossRefGoogle Scholar
  6. 6.
    Johnson TN, Tucker GT, Tanner MS, et al. Changes in liver volume from birth to adulthood: a meta-analysis. Liver Transpl 2005 Dec; 11(12): 1481–93PubMedCrossRefGoogle Scholar
  7. 7.
    Johnson TN, Rostami-Hodjegan A, Tucker GT. Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children. Clin Pharmacokinet 2006; 45(9): 931–56PubMedCrossRefGoogle Scholar
  8. 8.
    World Health Organization. Obesity and overweight [fact sheet no. 311; online]. Available from URL: http://www.who.int/mediacentre/factsheets/fs311/en/index.html [Accessed 2011 Oct 7]
  9. 9.
    Mingfang L, Cheung BMY. Pharmacotherapy for obesity. Br J Clin Pharmacol 2009; 68(6): 804–10CrossRefGoogle Scholar
  10. 10.
    Holford NH, Sheiner LB. Understanding the dose-effect relationship: clinical application of pharmacokinetic-pharmacodynamic models. Clin Pharmacokinet 1981 Nov–Dec; 6(6): 429–53PubMedCrossRefGoogle Scholar
  11. 11.
    Cheymol G. Effects of obesity on pharmacokinetics: implications for drug therapy. Clin Pharmacokinet 2000 Sep; 39(3): 215–31PubMedCrossRefGoogle Scholar
  12. 12.
    Hanley MJ, Abernethy DR, Greenblatt DJ. Effect of obesity on the pharmacokinetics of drugs in humans. Clin Pharmacokinet 2010; 49(2): 71–87PubMedCrossRefGoogle Scholar
  13. 13.
    Atkinson Jr AJ, Lyster PM. Systems clinical pharmacology. Clin Pharmacol Ther 2010 Jul; 88(1): 3–6PubMedCrossRefGoogle Scholar
  14. 14.
    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 2007 Oct–Nov; 37(10–11): 1013–4PubMedGoogle Scholar
  15. 15.
    Green B, Duffull SB. What is the best size descriptor to use for pharmacokinetic studies in the obese? Br J Clin Pharmacol 2004 Aug; 58(2): 119–33PubMedCrossRefGoogle Scholar
  16. 16.
    Bauer LA, Black DJ, Lill JS. Vancomycin dosing in morbidly obese patients. Eur J Clin Pharmacol 1998 Oct; 54(8): 621–5PubMedCrossRefGoogle Scholar
  17. 17.
    Traynor AM, Nafziger AN, Bertino Jr JS. Aminoglycoside dosing weight correction factors for patients of various body sizes. Antimicrob Agents Chemother 1995 Feb; 39(2): 545–8PubMedCrossRefGoogle Scholar
  18. 18.
    Cheymol G, Poirier JM, Carrupt PA, et al. Pharmacokinetics of beta-adrenoceptor blockers in obese and normal volunteers. Br J Clin Pharmacol 1997 Jun; 43(6): 563–70PubMedCrossRefGoogle Scholar
  19. 19.
    Mathijssen RH, Sparreboom A. Influence of lean body weight on anticancer drug clearance [letter]. Clin Pharmacol Ther 2009 Jan; 85(1): 23; author reply 24PubMedCrossRefGoogle Scholar
  20. 20.
    Jamei M, Dickinson GL, Rostami-Hodjegan 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 2009; 24(1): 53–75PubMedCrossRefGoogle Scholar
  21. 21.
    Jamei M, Marciniak S, Feng K, et al. The Simcyp® Population-Based ADME Simulator. Expert Opin Drug Metab Toxicol 2009 Feb; 5(2): 211–23PubMedCrossRefGoogle Scholar
  22. 22.
    Rostami-Hodjegan A, Tucker GT. In silico simulations to assess the in vivo consequences of in vitro metabolic drug-drug interactions. Drug Discov Today Technol 2004; 1: 441–8CrossRefGoogle Scholar
  23. 23.
    Howgate EM, Rowland-Yeo K, Proctor NJ, et al. Prediction of in vivo drug clearance from in vitro data. I: Impact of inter-individual variability. Xenobiotica 2006 Jun; 36(6): 473–97PubMedCrossRefGoogle Scholar
  24. 24.
    Wilkinson GR, Shand DG. Commentary: a physiological approach to hepatic drug clearance. Clin Pharmacol Ther 1975 Oct; 18(4): 377–90PubMedGoogle Scholar
  25. 25.
    Agoram B, Woltosz WS, Bolger MB. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv Drug Deliv Rev 2001 Oct 1; 50 Suppl. 1: S41–67PubMedCrossRefGoogle Scholar
  26. 26.
    Jamei M, Turner D, Yang J, et al. Population-based mechanistic prediction of oral drug absorption. AAPS J 2009 Jun; 11(2): 225–37PubMedCrossRefGoogle Scholar
  27. 27.
    Rostami-Hodjegan A, Tucker GT. The effects of portal shunts on intestinal cytochrome P450 3A activity [letter]. Hepatology 2002 Jun; 35(6): 1549–50; author reply 1550–1PubMedCrossRefGoogle Scholar
  28. 28.
    Gertz M, Harrison A, Houston JB, et al. Prediction of human intestinal first-pass metabolism of 25 CYP3A substrates from in vitro clearance and permeability data. Drug Metab Dispos 2010 Jul; 38(7): 1147–58PubMedCrossRefGoogle Scholar
  29. 29.
    Yang J, Jamei M, Yeo KR, et al. Prediction of intestinal first-pass drug metabolism. Curr Drug Metab 2007 Oct; 8(7): 676–84PubMedCrossRefGoogle Scholar
  30. 30.
    Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser 2000; 894: i–xii, 1–253Google Scholar
  31. 31.
    De Lorenzo A, Deurenberg P, Pietrantuono M, et al. How fat is obese? Acta Diabetol 2003 Oct; 40 Suppl. 1: S254–7PubMedCrossRefGoogle Scholar
  32. 32.
    Dubois D, Dubois EF. A formula to estimate the approximate surface area if height and weight be known. Arch Int Med 1916; 17: 863–71CrossRefGoogle Scholar
  33. 33.
    Verbraecken J, van de Heyning P, De Backer W, et al. Body surface area in normal-weight, overweight, and obese adults: a comparison study. Metabolism 2006 Apr; 55(4): 515–24PubMedCrossRefGoogle Scholar
  34. 34.
    Yu CY, Lo YH, Chiou WK. The 3D scanner for measuring body surface area: a simplified calculation in the Chinese adult. Appl Ergon 2003 May; 34(3): 273–8PubMedCrossRefGoogle Scholar
  35. 35.
    Mosteller RD. Simplified calculation of body-surface area. N Engl J Med 1987 Oct 22; 317(17): 1098PubMedGoogle Scholar
  36. 36.
    Boyd E. The growth of the surface area of the human body. Minneapolis (MN): University of Minnesota Press, 1935Google Scholar
  37. 37.
    Gehan EA, George SL. Estimation of human body surface area from height and weight. Cancer Chemother Rep 1970 Aug; 54(4): 225–35PubMedGoogle Scholar
  38. 38.
    Anderson E, Browne N, Duketsky S, et al. Development of statistical distributions or ranges of standard factors used in exposure assessments: final report [report no. EPA/600/8-85-010]. Washington, DC: US Environmental Protection Agency, 1985 [online]. Available from URL: http://www.epa.gov/opptintr/exposure/pubs/usepa_1985b_development_of_statistical_distributions.pdf [Accessed 2011 Sep 22]Google Scholar
  39. 39.
    Haycock GB, Schwartz GJ, Wisotsky DH. Geometric method for measuring body surface area: a height-weight formula validated in infants, children, and adults. J Pediatr 1978 Jul; 93(1): 62–6PubMedCrossRefGoogle Scholar
  40. 40.
    Mattar JA. A simple calculation to estimate body surface area in adults and its correlation with the Du Bois formula. Crit Care Med 1989; 17: 846–7PubMedCrossRefGoogle Scholar
  41. 41.
    Livingston EH, Lee S. Body surface area prediction in normal-weight and obese patients. Am J Physiol Endocrinol Metab 2001 Sep; 281(3): E586–91PubMedGoogle Scholar
  42. 42.
    Colles SL, Dixon JB, Marks P, et al. Preoperative weight loss with a very-low-energy diet: quantitation of changes in liver and abdominal fat by serial imaging. Am J Clin Nutr 2006 Aug; 84(2): 304–11PubMedGoogle Scholar
  43. 43.
    Vasan RS. Cardiac function and obesity. Heart 2003 Oct; 89(10): 1127–9PubMedCrossRefGoogle Scholar
  44. 44.
    Collis T, Devereux RB, Roman MJ, et al. Relations of stroke volume and cardiac output to body composition: the Strong Heart Study. Circulation 2001 Feb 13; 103(6): 820–5PubMedCrossRefGoogle Scholar
  45. 45.
    de Simone G, Devereux RB, Daniels SR, et al. Stroke volume and cardiac output in normotensive children and adults: assessment of relations with body size and impact of overweight. Circulation 1997 Apr 1; 95(7): 1837–43PubMedCrossRefGoogle Scholar
  46. 46.
    Messerli FH, Christie B, DeCarvalho JG, et al. Obesity and essential hypertension: hemodynamics, intravascular volume, sodium excretion, and plasma renin activity. Arch Intern Med 1981 Jan; 141(1): 81–5PubMedCrossRefGoogle Scholar
  47. 47.
    Messerli FH, Sundgaard-Riise K, Reisin E, et al. Disparate cardiovascular effects of obesity and arterial hypertension. Am J Med 1983 May; 74(5): 808–12PubMedCrossRefGoogle Scholar
  48. 48.
    Salvadori A, Fanari P, Fontana M, et al. Oxygen uptake and cardiac performance in obese and normal subjects during exercise. Respiration 1999; 66(1): 25–33PubMedCrossRefGoogle Scholar
  49. 49.
    Stelfox HT, Ahmed SB, Ribeiro RA, et al. Hemodynamic monitoring in obese patients: the impact of body mass index on cardiac output and stroke volume. Crit Care Med 2006 Apr; 34(4): 1243–6PubMedCrossRefGoogle Scholar
  50. 50.
    Taylor HL, Brozek J, Keys A. Basal cardiac function and body composition with special reference to obesity. J Clin Invest 1952 Nov; 31(11): 976–83PubMedCrossRefGoogle Scholar
  51. 51.
    Jegier W, Sekelj P, Auld PA, et al. The relation between cardiac output and body size. Br Heart J 1963 Jul; 25: 425–30PubMedCrossRefGoogle Scholar
  52. 52.
    Abernethy DR, Greenblatt DJ, Divoll M, et al. The influence of obesity on the pharmacokinetics of oral alprazolam and triazolam. Clin Pharmacokinet 1984 Mar–Apr; 9(2): 177–83PubMedCrossRefGoogle Scholar
  53. 53.
    Blouin RA, Kolpek JH, Mann HJ. Influence of obesity on drug disposition. Clin Pharm 1987 Sep; 6(9): 706–14PubMedGoogle Scholar
  54. 54.
    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 2007 Jan; 8(1): 33–45PubMedCrossRefGoogle Scholar
  55. 55.
    Barter ZE, Chowdry JE, Harlow JR, et al. Covariation of human microsomal protein per gram of liver with age: absence of influence of operator and sample storage may justify interlaboratory data pooling. Drug Metab Dispos 2008 Dec; 36(12): 2405–9PubMedCrossRefGoogle Scholar
  56. 56.
    Kotlyar M, Carson SW. Effects of obesity on the cytochrome P450 enzyme system. Int J Clin Pharmacol Ther 1999 Jan; 37(1): 8–19PubMedGoogle Scholar
  57. 57.
    Caraco Y, Zylber-Katz E, Berry EM, et al. Significant weight reduction in obese subjects enhances carbamazepine elimination. Clin Pharmacol Ther 1992 May; 51(5): 501–6PubMedCrossRefGoogle Scholar
  58. 58.
    Caraco Y, Zylber-Katz E, Berry EM, et al. Carbamazepine pharmacokinetics in obese and lean subjects. Ann Pharmacother 1995 Sep; 29(9): 843–7PubMedGoogle Scholar
  59. 59.
    Dunn TE, Ludwig EA, Slaughter RL, et al. Pharmacokinetics and pharmacodynamics of methylprednisolone in obesity. Clin Pharmacol Ther 1991 May; 49(5): 536–49PubMedCrossRefGoogle Scholar
  60. 60.
    Flechner SM, Kolbeinsson ME, Tam J, et al. The impact of body weight on cyclosporine pharmacokinetics in renal transplant recipients. Transplantation 1989 May; 47(5): 806–10PubMedCrossRefGoogle Scholar
  61. 61.
    Greenblatt DJ, Abernethy DR, Locniskar A, et al. Effect of age, gender, and obesity on midazolam kinetics. Anesthesiology 1984 Jul; 61(1): 27–35PubMedGoogle Scholar
  62. 62.
    Bentley JB, Vaughan RW, Gandolfi AJ, et al. Halothane biotransformation in obese and nonobese patients. Anesthesiology 1982 Aug; 57(2): 94–7PubMedCrossRefGoogle Scholar
  63. 63.
    Bentley JB, Vaughan RW, Miller MS, et al. Serum inorganic fluoride levels in obese patients during and after enflurane anesthesia. Anesth Analg 1979 Sep–Oct; 58(5): 409–12PubMedCrossRefGoogle Scholar
  64. 64.
    Frink Jr EJ, Malan Jr TP, Brown EA, et al. Plasma inorganic fluoride levels with sevoflurane anesthesia in morbidly obese and nonobese patients. Anesth Analg 1993 Jun; 76(6): 1333–7PubMedGoogle Scholar
  65. 65.
    Higuchi H, Satoh T, Arimura S, et al. Serum inorganic fluoride levels in mildly obese patients during and after sevoflurane anesthesia. Anesth Analg 1993 Nov; 77(5): 1018–21PubMedCrossRefGoogle Scholar
  66. 66.
    Miller MS, Gandolfi AJ, Vaughan RW, et al. Disposition of enflurane in obese patients. J Pharmacol Exp Ther 1980 Nov; 215(2): 292–6PubMedGoogle Scholar
  67. 67.
    O’Shea D, Davis SN, Kim RB, et al. Effect of fasting and obesity in humans on the 6-hydroxylation of chlorzoxazone: a putative probe of CYP2E1 activity. Clin Pharmacol Ther 1994 Oct; 56(4): 359–67PubMedCrossRefGoogle Scholar
  68. 68.
    Young SR, Stoelting RK, Peterson C, et al. Anesthetic biotransformation and renal function in obese patients during and after methoxyflurane or halothane anesthesia. Anesthesiology 1975 Apr; 42(4): 451–7PubMedCrossRefGoogle Scholar
  69. 69.
    Benedek IH, Blouin RA, McNamara PJ. Serum protein binding and the role of increased alpha 1-acid glycoprotein in moderately obese male subjects. Br J Clin Pharmacol 1984 Dec; 18(6): 941–6PubMedCrossRefGoogle Scholar
  70. 70.
    Benedek IH, Fiske 3rd WD, Griffen WO, et al. Serum alpha 1-acid glycoprotein and the binding of drugs in obesity. Br J Clin Pharmacol 1983 Dec; 16(6): 751–4PubMedCrossRefGoogle Scholar
  71. 71.
    Sola E, Vaya A, Contreras T, et al. Rheological profile in severe and morbid obesity: preliminary results. Clin Hemorheol Microcirc 2004; 30(3–4): 415–8PubMedGoogle Scholar
  72. 72.
    Kasiske BL, Umen AJ. The influence of age, sex, race, and body habitus on kidney weight in humans. Arch Pathol Lab Med 1986 Jan; 110(1): 55–60PubMedGoogle Scholar
  73. 73.
    Lokkegaard N, Haupter I, Kristensen TB. Microalbuminuria in obesity. Scand J Urol Nephrol 1992; 26(3): 275–8PubMedCrossRefGoogle Scholar
  74. 74.
    Naeye RL, Roode P. The sizes and numbers of cells in visceral organs in human obesity. Am J Clin Pathol 1970 Aug; 54(2): 251–3PubMedGoogle Scholar
  75. 75.
    Nyengaard JR, Bendtsen TF. Glomerular number and size in relation to age, kidney weight, and body surface in normal man. Anat Rec 1992 Feb; 232(2): 194–201PubMedCrossRefGoogle Scholar
  76. 76.
    Price PS, Conolly RB, Chaisson CF, et al. Modeling interindividual variation in physiological factors used in PBPK models of humans. Crit Rev Toxicol 2003; 33(5): 469–503PubMedGoogle Scholar
  77. 77.
    Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976; 16(1): 31–41PubMedCrossRefGoogle Scholar
  78. 78.
    Spruill WJ, Wade WE, Cobb 3rd HH. Estimating glomerular filtration rate with a Modification of Diet in Renal Disease equation: implications for pharmacy. Am J Health Syst Pharm 2007 Mar 15; 64(6): 652–60PubMedCrossRefGoogle Scholar
  79. 79.
    Stevens LA, Nolin TD, Richardson MM, et al. Comparison of drug dosing recommendations based on measured GFR and kidney function estimating equations. Am J Kidney Dis 2009 Jul; 54(1): 33–42PubMedCrossRefGoogle Scholar
  80. 80.
    British National Formulary. Principles of dose adjustment in renal impairment [online]. Available from URL: http://bnf.org/bnf/bnf/current/43768.htm [Accessed 2011 Jun]
  81. 81.
    Center for Drug Evaluation and Research, US FDA. Guidance for industry: pharmacokinetics in patients with impaired renal function — study design, data analysis, and impact on dosing and labeling [online]. Available from URL: http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm072127.pdf [Accessed 2011 Sep 20]
  82. 82.
    Dionne RE, Bauer LA, Gibson GA, et al. Estimating creatinine clearance in morbidity obese patients. Am J Hosp Pharm 1981 Jun; 38(6): 841–4PubMedGoogle Scholar
  83. 83.
    Salazar DE, Corcoran GB. Predicting creatinine clearance and renal drug clearance in obese patients from estimated fat-free body mass. Am J Med 1988 Jun; 84(6): 1053–60PubMedCrossRefGoogle Scholar
  84. 84.
    Demirovic JA, Pai AB, Pai MP. Estimation of creatinine clearance in morbidly obese patients. Am J Health Syst Pharm 2009 Apr 1; 66(7): 642–8PubMedCrossRefGoogle Scholar
  85. 85.
    Han PY, Duffull SB, Kirkpatrick CM, et al. Dosing in obesity: a simple solution to a big problem. Clin Pharmacol Ther 2007 Nov; 82(5): 505–8PubMedCrossRefGoogle Scholar
  86. 86.
    Anastasio P, Spitali L, Frangiosa A, et al. Glomerular filtration rate in severely overweight normotensive humans. Am J Kidney Dis 2000 Jun; 35(6): 1144–8PubMedCrossRefGoogle Scholar
  87. 87.
    Bosma RJ, Krikken JA, Homan van der Heide JJ, et al. Obesity and renal hemodynamics. Contrib Nephrol 2006; 151: 184–202PubMedCrossRefGoogle Scholar
  88. 88.
    Chagnac A, Weinstein T, Herman M, et al. The effects of weight loss on renal function in patients with severe obesity. J Am Soc Nephrol 2003 Jun; 14(6): 1480–6PubMedCrossRefGoogle Scholar
  89. 89.
    Chagnac A, Weinstein T, Korzets A, et al. Glomerular hemodynamics in severe obesity. Am J Physiol Renal Physiol 2000 May; 278(5): F817–22PubMedGoogle Scholar
  90. 90.
    Kotchen TA, Piering AW, Cowley AW, et al. Glomerular hyperfiltration in hypertensive African Americans. Hypertension 2000 Mar; 35(3): 822–6PubMedCrossRefGoogle Scholar
  91. 91.
    Scaglione R, Ganguzza A, Corrao S, et al. Central obesity and hypertension: pathophysiologic role of renal haemodynamics and function. Int J Obes Relat Metab Disord 1995 Jun; 19(6): 403–9PubMedGoogle Scholar
  92. 92.
    Verhave JC, Fesler P, Ribstein J, et al. Estimation of renal function in subjects with normal serum creatinine levels: influence of age and body mass index. Am J Kidney Dis 2005 Aug; 46(2): 233–41PubMedCrossRefGoogle Scholar
  93. 93.
    Weber MA, Neutel JM, Smith DH. Contrasting clinical properties and exercise responses in obese and lean hypertensive patients. J Am Coll Cardiol 2001 Jan; 37(1): 169–74PubMedCrossRefGoogle Scholar
  94. 94.
    Wang Z, Hall SD, Maya JF, et al. Diabetes mellitus increases the in vivo activity of cytochrome P450 2E1 in humans. Br J Clin Pharmacol 2003 Jan; 55(1): 77–85PubMedCrossRefGoogle Scholar
  95. 95.
    Caraco Y, Zylber-Katz E, Berry EM, et al. Caffeine pharmacokinetics in obesity and following significant weight reduction. Int J Obes Relat Metab Disord 1995 Apr; 19(4): 234–9PubMedGoogle Scholar
  96. 96.
    Abernethy DR, Todd EL, Schwartz JB. Caffeine disposition in obesity. Br J Clin Pharmacol 1985 Jul; 20(1): 61–6PubMedCrossRefGoogle Scholar
  97. 97.
    Gal P, Jusko WJ, Yurchak AM, et al. Theophylline disposition in obesity. Clin Pharmacol Ther 1978 Apr; 23(4): 438–44PubMedGoogle Scholar
  98. 98.
    Abernethy DR, Greenblatt DJ. Phenytoin disposition in obesity: determination of loading dose. Arch Neurol 1985 May; 42(5): 468–71PubMedCrossRefGoogle Scholar
  99. 99.
    Aitchison J, Brown JAC. The lognormal distribution. Cambridge: University Press, 1966Google Scholar
  100. 100.
    Armitage P, Matthews JNS, Berry G. Statistical methods in medical research. 4th ed. New York: Wiley Blackwell, 2001Google Scholar
  101. 101.
    Guest EJ, Aarons L, Houston JB, et al. Critique of the two-fold measure of prediction success for ratios: application for the assessment of drug-drug interactions. Drug Metab Dispos 2011 Feb; 39(2): 170–3PubMedCrossRefGoogle Scholar
  102. 102.
    Busetto L, Tregnaghi A, De Marchi F, et al. Liver volume and visceral obesity in women with hepatic steatosis undergoing gastric banding. Obes Res 2002 May; 10(5): 408–11PubMedCrossRefGoogle Scholar
  103. 103.
    Lewis MC, Phillips ML, Slavotinek JP, et al. Change in liver size and fat content after treatment with Optifast very low calorie diet. Obes Surg 2006 Jun; 16(6): 697–701PubMedCrossRefGoogle Scholar
  104. 104.
    Vauthey JN, Abdalla EK, Doherty DA, et al. Body surface area and body weight predict total liver volume in Western adults. Liver Transpl 2002 Mar; 8(3): 233–40PubMedCrossRefGoogle Scholar
  105. 105.
    Khemawoot P, Yokogawa K, Shimada T, et al. Obesity-induced increase of CYP2E1 activity and its effect on disposition kinetics of chlorzoxazone in Zucker rats. Biochem Pharmacol 2007 Jan 1; 73(1): 155–62PubMedCrossRefGoogle Scholar
  106. 106.
    Cubitt HE, Yeo KR, Howgate EM, et al. Sources of interindividual variability in IVIVE of clearance: an investigation into the prediction of benzodiazepine clearance using a mechanistic population-based pharmacokinetic model. Xenobiotica 2011 Aug; 41(8); 623–38PubMedCrossRefGoogle Scholar
  107. 107.
    Einolf HJ. Comparison of different approaches to predict metabolic drug-drug interactions. Xenobiotica 2007 Oct–Nov; 37(10–11): 1257–94PubMedGoogle Scholar
  108. 108.
    Polasek TM, Polak S, Doogue MP, et al. Assessment of inter-individual variability in predicted phenytoin clearance. Eur J Clin Pharmacol 2009; 65(12): 1203–10PubMedCrossRefGoogle Scholar
  109. 109.
    Duffull SB, Dooley MJ, Green B, et al. A standard weight descriptor for dose adjustment in the obese patient. Clin Pharmacokinet 2004; 43(15): 1167–78PubMedCrossRefGoogle Scholar
  110. 110.
    Alexander JK, Dennis EW, Smith WG, et al. Blood volume, cardiac output, and distribution of systemic blood flow in extreme obesity. Cardiovasc Res Cent Bull 1962 Winter; 1: 39–44PubMedGoogle Scholar
  111. 111.
    Bucolo RJ. Obesity, hyperinsulinemia, and portal blood flow: a theoretic study of the effects of increased portal blood flow upon fasting peripheral insulin concentrations. Diabetes 1978 Aug; 27(8): 840–8PubMedCrossRefGoogle Scholar
  112. 112.
    Burton M, Shaw L, Schentag JJ, et al. Applied pharmacokinetics and pharmacodynamics: principles of therapeutic drug monitoring. 4th ed. Baltimore (MD): Lippincott Williams and Wilkins, 2005Google Scholar
  113. 113.
    Ferreira I, Snijder MB, Twisk JW, et al. Central fat mass versus peripheral fat and lean mass: opposite (adverse versus favorable) associations with arterial stiffness? The Amsterdam Growth and Health Longitudinal Study. J Clin Endocrinol Metab 2004 Jun; 89(6): 2632–9PubMedCrossRefGoogle Scholar
  114. 114.
    Pinto-Sietsma SJ, Navis G, Janssen WM, et al. A central body fat distribution is related to renal function impairment, even in lean subjects. Am J Kidney Dis 2003 Apr; 41(4): 733–41PubMedCrossRefGoogle Scholar
  115. 115.
    Committee for Medicinal Products for Human Use, European Medicines Agency. Guideline on the evaluation of the pharmacokinetics of medicinal products in patients with impaired hepatic function [online]. Available from URL: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/09/WC500003122.pdf [Accessed 2011 Sep 20]

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© Adis Data Information BV 2011

Authors and Affiliations

  • Cyrus Ghobadi
    • 1
  • Trevor N. Johnson
    • 1
  • Mohsen Aarabi
    • 1
  • Lisa M. Almond
    • 1
  • Aurel Constant Allabi
    • 1
  • Karen Rowland-Yeo
    • 1
  • Masoud Jamei
    • 1
  • Amin Rostami-Hodjegan
    • 1
    • 2
  1. 1.Simcyp LimitedSheffieldUK
  2. 2.School of Pharmacy and Pharmaceutical SciencesUniversity of ManchesterManchesterUK

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