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Review

Modeling and predicting drug pharmacokinetics in patients with renal impairment

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Pages 261-274 | Published online: 10 Jan 2014

References

  • Gabardi S, Abramson S. Drug dosing in chronic kidney disease. Med. Clin. North Am.89, 649–687 (2005).
  • Balant LP, Dayer P, Fabre J. Consequences of renal insufficiency on the hepatic clearance of some drugs. Int. J. Clin. Pharmacol. Res.3, 459–474 (1983).
  • Pichette V, Leblond FA. Drug metabolism in chronic renal failure. Curr. Drug Metab.4, 91–103 (2003).
  • Dreisbach AW, Lertora JJ. The effect of chronic renal failure on hepatic drug metabolism and drug disposition. Semin. Dial.16, 45–50 (2003).
  • Korashy HM, Elbekai RH, El-Kadi AO. Effects of renal diseases on the regulation and expression of renal and hepatic drug-metabolizing enzymes: a review. Xenobiotica34, 1–29 (2004).
  • Nolin TD, Frye RF, Matzke GR. Hepatic drug metabolism and transport in patients with kidney disease. Am. J. Kidney Dis.42, 906–925 (2003).
  • Sun H, Frassetto L, Benet LZ. Effects of renal failure on drug transport and metabolism. Pharmacol. Ther.109, 1–11 (2006).
  • Nolin TD, Naud J, Leblond FA, Pichette V. Emerging evidence of the impact of kidney disease on drug metabolism and transport. Clin. Pharmacol. Ther.83, 898–903 (2008).
  • Zhang Y, Zhang L, Abraham S et al. Assessment of the impact of renal impairment on systemic exposure of new molecular entities: evaluation of recent new drug applications. Clin. Pharmacol. Ther.85, 305–311 (2009).
  • Lalonde RL, Wagner JA. Drug development perspective on pharmacokinetic studies of new drugs in patients with renal impairment. Clin. Pharmacol. Ther.86, 557–561 (2009).
  • Ribbing J, Jonsson EN. Power, selection bias and predictive performance of the population pharmacokinetic covariate model. J. Pharmacokinet. Pharmacodyn.31, 109–134 (2004).
  • 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, 140–148 (2007).
  • Gibson GG, Rostami-Hodjegan A. Modeling and simulation in prediction of human xenobiotic absorption, distribution, metabolism and excretion (ADME): in vitro–in vivo extrapolations (IVIVE). Xenobiotica37, 1013–1014 (2007).
  • Foley RN. Clinical epidemiology of cardiovascular disease in chronic kidney disease. J. Ren. Care36(Suppl. 1), 4–8 (2010).
  • Hamer RA, El Nahas AM. The burden of chronic kidney disease. Br. Med. J.332, 563–564 (2006).
  • Coresh J, Selvin E, Stevens LA et al. Prevalence of chronic kidney disease in the United States. JAMA298, 2038–2047 (2007).
  • Atkins RC. The epidemiology of chronic kidney disease. Kidney Int. Suppl.94, S14–S18 (2005).
  • Talbert RL. Drug dosing in renal insufficiency. J. Clin. Pharmacol.34, 99–110 (1994).
  • Lam YW, Banerji S, Hatfield C, Talbert RL. Principles of drug administration in renal insufficiency. Clin. Pharmacokinet.32, 30–57 (1997).
  • Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron16, 31–41 (1976).
  • Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann. Intern. Med.130, 461–470 (1999).
  • Stevens LA, Coresh J, Greene T, Levey AS. Assessing kidney function–measured and estimated glomerular filtration rate. N. Engl. J. Med.354, 2473–2483 (2006).
  • Rule AD, Gussak HM, Pond GR et al. Measured and estimated GFR in healthy potential kidney donors. Am. J. Kidney Dis.43(1), 112–119 (2004).
  • Poggio ED, Wang X, Greene T, Van Lente F, Hall PM. Performance of the modification of diet in renal disease and Cockcroft–Gault equations in the estimation of GFR in health and in chronic kidney disease. J. Am. Soc. Nephrol.16(2), 459–466 (2005).
  • Froissart M, Rossert J, Jacquot C, Paillard M, Houillier P. Predictive performance of the modification of diet in renal disease and Cockcroft–Gault equations for estimating renal function. J. Am. Soc. Nephrol.16(3), 763–773 (2005).
  • Cantu TG, Ellerbeck EF, Yun SW, Castine SD, Kornhauser DM. Drug prescribing for patients with changing renal function. Am. J. Hosp. Pharm.49, 2944–2948 (1992).
  • Dressman JB, Amidon GL, Fleisher D. Absorption potential: estimating the fraction absorbed for orally administered compounds. J. Pharm. Sci.74, 588–589 (1985).
  • Artursson P, Karlsson J. Correlation between oral drug absorption in humans and apparent drug permeability coefficients in human intestinal epithelial (Caco-2) cells. Biochem. Biophys. Res. Commun.175, 880–885 (1991).
  • Yu LX, Amidon GL. A compartmental absorption and transit model for estimating oral drug absorption. Int. J. Pharm.186, 119–125 (1999).
  • Agoram B, Woltosz WS, Bolger MB. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv. Drug Deliv. Rev.50(Suppl. 1), S41–S67 (2001).
  • Jamei M, Turner D, Yang J et al. Population-based mechanistic prediction of oral drug absorption. AAPS J.11, 225–237 (2009).
  • Wright RA, Clemente R, Wathen R. Gastric emptying in patients with chronic renal failure receiving hemodialysis. Arch. Intern. Med.144, 495–496 (1984).
  • McNamee PT, Moore GW, McGeown MG, Doherty CC, Collins BJ. Gastric emptying in chronic renal failure. Br. Med. J.291, 310–311 (1985).
  • St Peter WL, Redic-Kill KA, Halstenson CE. Clinical pharmacokinetics of antibiotics in patients with impaired renal function. Clin. Pharmacokinet.22, 169–210 (1992).
  • 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.24, 53–75 (2009).
  • Sawada Y, Hanano M, Sugiyama Y, Harashima H, Iga T. Prediction of the volumes of distribution of basic drugs in humans based on data from animals. J. Pharmacokinet. Biopharm.12, 587–596 (1984).
  • Berezhkovskiy LM. Volume of distribution at steady state for a linear pharmacokinetic system with peripheral elimination. J. Pharm. Sci.93, 1628–1640 (2004).
  • Poulin P, Theil FP. A priori prediction of tissue:plasma partition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery. J. Pharm. Sci.89, 16–35 (2000).
  • Poulin P, Schoenlein K, Theil FP. Prediction of adipose tissue: plasma partition coefficients for structurally unrelated drugs. J. Pharm. Sci.90, 436–447 (2001).
  • Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. J. Pharm. Sci.91, 129–156 (2002).
  • Rodgers T, Leahy D, Rowland M. Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases. J. Pharm. Sci.94, 1259–1276 (2005).
  • Rodgers T, Rowland M. Physiologically based pharmacokinetic modeling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J. Pharm. Sci.95, 1238–1257 (2006).
  • Hsu CY, McCulloch CE, Curhan GC. Iron status and hemoglobin level in chronic renal insufficiency. J. Am. Soc. Nephrol.13(11), 2783–2786 (2002).
  • Vanholder R, De Smet R, Ringoir S. Factors influencing drug protein binding in patients with end stage renal failure. Eur. J. Clin. Pharmacol.44(Suppl. 1), S17–S21 (1993).
  • Vanholder R, Van Landschoot N, De Smet R, Schoots A, Ringoir S. Drug protein binding in chronic renal failure: evaluation of nine drugs. Kidney Int.33, 996–1004 (1988).
  • McNamara PJ, Alcorn J. Protein binding predictions in infants. AAPS PharmSci.4(1), E4 (2002).
  • Aranoff G, Berns J, Brier M et al.Drug Prescribing in Renal Failure: Dosing Guidelines for Adults (Volume 4). American College of Physicians, Philadelphia, PA, USA (1999).
  • Wilkinson GR. Clearance approaches in pharmacology. Pharmacol. Rev.39, 1–47 (1987).
  • Rane A, Wilkinson GR, Shand DG. Prediction of hepatic extraction ratio from in vitro measurement of intrinsic clearance. J. Pharmacol. Exp. Ther.200, 420–424 (1977).
  • Houston JB. Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance. Biochem. Pharmacol.47, 1469–1479 (1994).
  • 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, 462–469 (1997).
  • 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, 1350–1359 (1999).
  • Galetin A, Brown C, Hallifax D, Ito K, Houston JB. Utility of recombinant enzyme kinetics in prediction of human clearance: impact of variability, CYP3A5, and CYP2C19 on CYP3A4 probe substrates. Drug Metab. Dispos.32, 1411–1420 (2004).
  • 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, 1304–1311 (2005).
  • 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. Xenobiotica36, 473–497 (2006).
  • 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, 33–45 (2007).
  • Rostami-Hodjegan A, Tucker GT. ‘In silico’ simulations to assess the ‘in vivo’ consequences of ‘in vitro’ metabolic drug–drug interactions. Drug Discov. Today Tech.1(4), 441–448 (2004).
  • Rowland Yeo K, Rostami-Hodjegan A, Tucker GT. Abundance of cytochromes P450 in human liver: a meta analysis. Br. J. Clin. Pharmacol.57(5), 687 (2004).
  • Proctor NJ, Tucker GT, Rostami-Hodjegan A. Predicting drug clearance from recombinantly expressed CYPs: intersystem extrapolation factors. Xenobiotica34, 151–178 (2004).
  • Haneda M. [Mechanisms for the development and progression of diabetic nephropathy]. Nippon Rinsho64(Suppl. 2), 427–432 (2006).
  • Michaud J, Naud J, Chouinard J et al. Role of parathyroid hormone in the downregulation of liver cytochrome P450 in chronic renal failure. J. Am. Soc. Nephrol.17, 3041–3048 (2006).
  • Taburet AM, Singlas E. Drug interactions with antiviral drugs. Clin. Pharmacokinet.30, 385–401 (1996).
  • Bauer LA, Bauer SP, Blouin RA. The effect of acute and chronic renal failure on theophylline clearance. J. Clin. Pharmacol.22(1), 65–68 (1982).
  • Chapelsky MC, Thompson-Culkin K, Miller AK, Sack M, Blum R, Freed MI. Pharmacokinetics of rosiglitazone in patients with varying degrees of renal insufficiency. J. Clin. Pharmacol.43(3), 252–259 (2003).
  • Dingemanse J, van Giersbergen PL. Influence of severe renal dysfunction on the pharmacokinetics and metabolism of bosentan, a dual endothelin receptor antagonist. Int. J. Clin. Pharmacol. Ther.40(7), 310–316 (2002).
  • Naesdal J, Andersson T, Bodemar G et al. Pharmacokinetics of [14C]omeprazole in patients with impaired renal function. Clin. Pharmacol. Ther.40(3), 344–351 (1986).
  • Balant L, Francis RJ, Tozer TN, Marmy A, Tschopp JM, Fabre J. Influence of renal failure on the hepatic clearance of bufuralol in man. J. Pharmacokinet. Biopharm.8(5), 421–438 (1980).
  • Vinik HR, Reves JG, Greenblatt DJ, Abernethy DR, Smith LR. The pharmacokinetics of midazolam in chronic renal failure patients. Anesthesiology59(5), 390–394 (1983).
  • Pang KS. Modeling of intestinal drug absorption: roles of transporters and metabolic enzymes (for the Gillette Review Series). Drug Metab. Dispos.31, 1507–1519 (2003).
  • Paine MF, Hart HL, Ludington SS, Haining RL, Rettie AE, Zeldin DC. The human intestinal cytochrome P450 “pie”. Drug Metab. Dispos.34, 880–886 (2006).
  • Paine MF, Khalighi M, Fisher JM et al. Characterization of interintestinal and intraintestinal variations in human CYP3A-dependent metabolism. J. Pharmacol. Exp. Ther.283, 1552–1562 (1997).
  • Yang J, Tucker GT, Rostami-Hodjegan A. Cytochrome P450 3A expression and activity in the human small intestine. Clin. Pharmacol. Ther.76, 391 (2004).
  • Thummel KE, O’Shea D, Paine MF et al. Oral first-pass elimination of midazolam involves both gastrointestinal and hepatic CYP3A-mediated metabolism. Clin. Pharmacol. Ther.59(5), 491–502 (1996).
  • Rostami-Hodjegan A, Tucker GT. The effects of portal shunts on intestinal cytochrome P450 3A activity. Hepatology35, 1549–1550; author reply 1550–1541 (2002).
  • Yang J, Jamei M, Yeo KR, Tucker GT, Rostami-Hodjegan A. Prediction of intestinal first-pass drug metabolism. Curr. Drug Metab.8, 676–684 (2007).
  • Leblond FA, Petrucci M, Dube P, Bernier G, Bonnardeaux A, Pichette V. Downregulation of intestinal cytochrome P450 in chronic renal failure. J. Am. Soc. Nephrol.13, 1579–1585 (2002).
  • Schinkel AH. The physiological function of drug-transporting P-glycoproteins. Semin. Cancer Biol.8, 161–170 (1997).
  • Sun H, Frassetto LA, Huang Y, Benet LZ. Hepatic clearance, but not gut availability, of erythromycin is altered in patients with end-stage renal disease. Clin. Pharmacol. Ther.87, 465–472 (2010).
  • Veau C, Leroy C, Banide H et al. Effect of chronic renal failure on the expression and function of rat intestinal P-glycoprotein in drug excretion. Nephrol. Dial. Transplant.16, 1607–1614 (2001).
  • Gibson GG, Clarke SE. Incorporation of cytochrome b5 into rat liver microsomal membranes. Impairment of cytochrome P-450-dependent mixed function oxidase activity. Biochem. Pharmacol.35, 4431–4436 (1986).
  • Kusuhara H, Sugiyama Y. In vitro–in vivo extrapolation of transporter-mediated clearance in the liver and kidney. Drug Metab. Pharmacokinet.24(1), 37–52 (2009).
  • Kusuhara H, Sugiyama Y. Pharmacokinetic modeling of the hepatobiliary transport mediated by cooperation of uptake and efflux transporters. Drug Metab. Rev.42(3), 539–550 (2010).
  • Tucker GT. Measurement of the renal clearance of drugs. Br. J. Clin. Pharmacol.12(6), 761–770 (1981).
  • Levy G. Effect of plasma protein binding on renal clearance of drugs. J. Pharm. Sci.69, 482–483 (1980).
  • Song IS, Shin HJ, Shim EJ et al. Genetic variants of the organic cation transporter 2 influence the disposition of metformin. Clin. Pharmacol. Ther.84(5), 559–562 (2008).
  • Urban TJ, Brown C, Castro RA et al. Effects of genetic variation in the novel organic cation transporter, OCTN1, on the renal clearance of gabapentin. Clin. Pharmacol. Ther.83(3), 416–421 (2008).
  • Wang ZJ, Yin OQ, Tomlinson B, Chow MS. OCT2 polymorphisms and in-vivo renal functional consequence: studies with metformin and cimetidine. Pharmacogenet. Genomics18(7), 637–645 (2008).
  • Janku I. Physiological modeling of renal drug clearance. Eur. J. Clin. Pharmacol.44, 513–519 (1993).
  • Jackson PR, Tucker GT, Lennard MS, Woods HF. Polymorphic drug oxidation: pharmacokinetic basis and comparison of experimental indices. Br. J. Clin. Pharmacol.22(5), 541–550 (1986).
  • Jackson PR, Tucker GT. Pharmacokinetic–pharmacogenetic modeling in the detection of polymorphisms in xenobiotic metabolism. Ann. Occup. Hyg.34(6), 653–662 (1990).
  • Jackson PR, Tucker GT, Woods HF. Backtracking booze with Bayes – the retrospective interpretation of blood alcohol data. Br. J. Clin. Pharmacol.31(1), 55–63 (1991).
  • Sato A. The effect of environmental factors on the pharmacokinetic behaviour of organic solvent vapours. Ann. Occup. Hyg.35(5), 525–541 (1991).
  • Clewell HJ 3rd, Andersen ME. Use of physiologically based pharmacokinetic modeling to investigate individual versus population risk. Toxicology111(1–3), 315–329 (1996).
  • Nestorov I. Modeling and simulation of variability and uncertainty in toxicokinetics and pharmacokinetics. Toxicol. Lett.120(1–3), 411–420 (2001).
  • Haddock RE, Jackson D, Woods FR. Paroxetine: lack of effect on hepatic drug metabolizing enzymes. Acta Psychiatr. Scand. Suppl.350, 93–94 (1989).
  • 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).
  • Sindrup SH, Brosen K, Gram LF et al. The relationship between paroxetine and the sparteine oxidation polymorphism. Clin. Pharmacol. Ther.51(3), 278–287 (1992).
  • 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).
  • Doyle GD, Laher M, Kelly JG, Byrne MM, Clarkson A, Zussman BD. The pharmacokinetics of paroxetine in renal impairment. Acta Psychiatr. Scand. Suppl.350, 89–90 (1989).
  • Pozet N, Brazier JL, Aissa AH et al. Pharmacokinetics of diltiazem in severe renal failure. Eur. J. Clin. Pharmacol.24(5), 635–638 (1983).
  • Morsell PL, Rovei V, Mitchard M, Durand A, Gomerai R, Larrinban J.Pharmacokinetics and metabolism of diltiazem in man (observations in healthy volunteers and angina pectoris patients). In: New Drug Therapy with Calcium Antagonists. Diltiazem Hakone Symposium. Bing RJ (Ed.) 152–167, Excerpta Medica, NY, USA (1978).
  • Pichard L, Fabre I, Daujat M, Domergue J, Joyeux H, Maurel P. Effect of corticosteroids on the expression of cytochromes P450 and on cyclosporin A oxidase activity in primary cultures of human hepatocytes. Mol. Pharmacol.41(6), 1047–1055 (1992).
  • Sutton D, Butler AM, Nadin L, Murray M. Role of CYP3A4 in human hepatic diltiazem N-demethylation: inhibition of CYP3A4 activity by oxidized diltiazem metabolites. J. Pharmacol. Exp. Ther.282(1), 294–300 (1997).
  • Zhao P, Lee CA, Kunze KL. Sequential metabolism is responsible for diltiazem-induced time-dependent loss of CYP3A. Drug Metab. Dispos.35(5), 704–712 (2007).
  • Backman JT, Olkkola KT, Aranko K, Himberg JJ, Neuvonen PJ. Dose of midazolam should be reduced during diltiazem and verapamil treatments. Br. J. Clin. Pharmacol.37(3), 221–225 (1994).
  • Laganiere S, Davies RF, Carignan G et al. Pharmacokinetic and pharmacodynamic interactions between diltiazem and quinidine. Clin. Pharmacol. Ther.60(3), 255–264 (1996).
  • Varhe A, Olkkola KT, Neuvonen PJ. Diltiazem enhances the effects of triazolam by inhibiting its metabolism. Clin. Pharmacol. Ther.59(4), 369–375 (1996).
  • Hoglund P, Nilsson LG. Pharmacokinetics of diltiazem and its metabolites after single and multiple dosing in healthy volunteers. Ther. Drug Monit.11(5), 558–566 (1989).
  • Montamat SC, Abernethy DR. N-monodesmethyldiltiazem is the predominant metabolite of diltiazem in the plasma of young and elderly hypertensives. Br. J. Clin. Pharmacol.24(2), 185–189 (1987).
  • Rowland Yeo K, Jamei M, Yang J, Tucker GT, Rostami-Hodjegan A. Physiologically based mechanistic modeling 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).
  • Dornhorst A. Insulinotropic meglitinide analogues. Lancet358(9294), 1709–1716 (2001).
  • 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).
  • Bidstrup TB, Bjornsdottir 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).
  • Niemi M, Backman JT, Kajosaari LI et al. Polymorphic organic anion transporting polypeptide 1B1 is a major determinant of repaglinide pharmacokinetics. Clin. Pharmacol. Ther.77(6), 468–478 (2005).
  • Marbury TC, Ruckle JL, Hatorp V et al. Pharmacokinetics of repaglinide in subjects with renal impairment. Clin. Pharmacol. Ther.67(1), 7–15 (2000).
  • Wang YH. Confidence assessment of the Simcyp time-based approach and a static mathematical model in predicting clinical drug–drug interactions for mechanism-based CYP3A inhibitors. Drug Metab. Dispos.38(7), 1094–1104 (2010).
  • 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 (2010).
  • Foti RS, Rock DA, Wienkers LC, Wahlstrom JL. Selection of alternative CYP3A4 probe substrates for clinical drug interaction studies using in vitro data and in vivo simulation. Drug Metab. Dispos.38(6), 981–987 (2010).
  • Polasek TM, Sadagopal JS, Elliot DJ, Miners JO. In vitro–in vivo extrapolation of zolpidem as a perpetrator of metabolic interactions involving CYP3A. Eur. J. Clin. Pharmacol.66(3), 275–283 (2010).
  • Emoto C, Murayama N, Rostami-Hodjegan A, Yamazaki H. Utilization of estimated physicochemical properties as an integrated part of predicting hepatic clearance in the early drug-discovery stage: impact of plasma and microsomal binding. Xenobiotica39(3), 227–235 (2009).
  • 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).
  • Bouzom F, Walther B. Pharmacokinetic predictions in children by using the physiologically based pharmacokinetic modeling. Fundam. Clin. Pharmacol.22(6), 579–587 (2008).
  • 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).
  • 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).
  • Rakhit A, Pantze MP, Fettner S et al. The effects of CYP3A4 inhibition on erlotinib pharmacokinetics: computer-based simulation (SimCYP) predicts in vivo metabolic inhibition. Eur. J. Clin. Pharmacol.64(1), 31–41 (2008).
  • 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).
  • Einolf HJ. Comparison of different approaches to predict metabolic drug–drug interactions. Xenobiotica37(10–11), 1257–1294 (2007).
  • Lalonde RL, Kowalski KG, Hutmacher MM et al. Model-based drug development. Clin. Pharmacol. Ther.82, 21–32 (2007).
  • Anjum S, Swan SK, Lambrecht LJ et al. Pharmacokinetics of flutamide in patients with renal insufficiency. Br. J. Clin. Pharmacol.47(1), 43–47 (1999).
  • De Martin S, Orlando R, Bertoli M, Pegoraro P, Palatini P. Differential effect of chronic renal failure on the pharmacokinetics of lidocaine in patients receiving and not receiving hemodialysis. Clin. Pharmacol. Ther.80(6), 597–606 (2006).
  • Pere P, Salonen M, Jokinen M, Rosenberg PH, Neuvonen PJ, Haasio J. Pharmacokinetics of ropivacaine in uremic and nonuremic patients after axillary brachial plexus block. Anesth. Analg.96(2), 563–569 (2003).
  • Dimmitt DC, Shah AK, Arumugham T et al. Pharmacokinetics of oral and intravenous dolasetron mesylate in patients with renal impairment. J. Clin. Pharmacol.38(9), 798–806 (1998).
  • Lehmann CR, Heironimus JD, Collins CB et al. Metoclopramide kinetics in patients with impaired renal function and clearance by hemodialysis. Clin. Pharmacol. Ther.37(3), 284–289 (1985).
  • Muirhead GJ, Wilner K, Colburn W, Haug-Pihale G, Rouviex B. The effects of age and renal and hepatic impairment on the pharmacokinetics of sildenafil. Br. J. Clin. Pharmacol.53(Suppl. 1), 21S–30S (2002).
  • Kleinbloesem CH, van Brummelen P, van Harten J, Danhof M, Breimer DD. Nifedipine: influence of renal function on pharmacokinetic/hemodynamic relationship. Clin. Pharmacol. Ther.37(5), 563–574 (1985).
  • Aparicio M, Chauveau P, De Precigout V, Bouchet JL, Lasseur C, Combe C. Nutrition and outcome on renal replacement therapy of patients with chronic renal failure treated by a supplemented very low protein diet. J. Am. Soc. Nephrol.11(4), 708–716 (2000).
  • Stenvinkel P, Heimburger O, Paultre F et al. Strong association between malnutrition, inflammation, and atherosclerosis in chronic renal failure. Kidney Int.55(5), 1899–1911 (1999).

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