Simulation and prediction of in vivo drug metabolism in human populations from in vitro data

Nature Reviews Drug Discovery

Volumn 6, Issue 2, 2007, Pages 140-148

  Rostami Hodjegan, Amin ^a,b   Tucker, Geoffrey T ^a,b  

^a ROYAL HALLAMSHIRE HOSPITAL   (United Kingdom)

^b CERTARA UK LIMITED   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

3,4 METHYLENEDIOXYMETHAMPHETAMINE; ALPRAZOLAM; CAFFEINE; CARBAMAZEPINE; CHLORZOXAZONE; CISAPRIDE; CYCLOSPORIN; CYTOCHROME P450; DEXTROMETHORPHAN; DICLOFENAC; KETOCONAZOLE; MEPHENYTOIN; MIDAZOLAM; OMEPRAZOLE; PHENYTOIN; RIFAMPICIN; SILDENAFIL; THEOPHYLLINE; TOLBUTAMIDE; TOLTERODINE; TRIAZOLAM; WARFARIN; ZOLPIDEM; DRUG;

CLINICAL TRIAL; COMPUTER PROGRAM; COMPUTER SIMULATION; COVARIANCE; DEMOGRAPHY; DISEASE COURSE; DRUG CLEARANCE; DRUG DISPOSITION; DRUG DISTRIBUTION; DRUG EFFICACY; DRUG METABOLISM; DRUG PROTEIN BINDING; DRUG RESPONSE; DRUG SAFETY; ENZYME ACTIVITY; ENZYME INDUCTION; ENZYME INHIBITION; ETHNIC DIFFERENCE; GENETIC DIFFERENCE; GENETIC POLYMORPHISM; GENOMICS; HUMAN; IMAGE DISPLAY; IN VITRO STUDY; IN VIVO STUDY; LIVER BLOOD FLOW; LIVER CLEARANCE; LIVER WEIGHT; MONTE CARLO METHOD; POPULATION DISTRIBUTION; PREDICTION; PRIORITY JOURNAL; PROTEIN EXPRESSION; REVIEW; ANIMAL; BIOLOGICAL MODEL; LIVER; METABOLISM;

ANIMALS; COMPUTER SIMULATION; HUMANS; LIVER; MODELS, BIOLOGICAL; PHARMACEUTICAL PREPARATIONS;

EID: 33947587274     PISSN: 14741776     EISSN: 14741784     Source Type: Journal    
DOI: 10.1038/nrd2173     Document Type: Review

References (64)

1. Uehling, M.D. Model Patient. Bio-IT World web site [online], (2003).
2. Lin, J. H. Sense and nonsense in the prediction of drug-drug interactions. Curr. Drug Metab. 1, 305-331 (2000).
3. Clarke, S. E. & Jeffrey, P. Utility of metabolic stability screening: comparison of in vitro and in vivo clearance. Xenobiotica 31, 591-598 (2001).
4. Andersson, T. B., Bredberg, E., Ericsson, H. & Sjoberg, H. An evaluation of the in vitro metabolism data for predicting the clearance and drug-drug interaction potential of CYP2C9 substrates. Drug Metab. Dispos. 32, 715-721 (2004).
5. Rostami-Hodjegan, A. & Tucker, G. T. 'In silico' simulations to assess the 'in vivo'consequences of 'in vitro' metabolic drug - drug interactions. Drug Discov. Today Technol: 1, 441-448 (2004).
6. Maxwell, C., Domenet, J. G. & Joyce, C. R. Instant experience in clinical trials: A novel aid to teaching by simulation. J. Clin. Pharmacol. New Drugs 11, 323-331 (1971).
7. Holford. N. H., Kimko, H. C., Monteleone, J. P. & Peck, C. C. Simulation of clinical trials. Annu. Rev. Pharmacol. Toxicol. 40, 209-234 (2000).
8. Marsh, B. T. The planning of a clinical trials workshop. Br. J. Clin. Pharmacol. 2, 455-461 (1975).
9. Bland, J. M. Computer simulation of a clinical trial as an aid to teaching the concept of statistical significance. Stat. Med. 5 193-197 (1986).
10. Madsen, B. W. et al. Clinical trial experience by simulation: A workshop report. BMJ 2, 1333-1335 (1978).
11. Jackson, P. R., Tucker, G. T., Lennard, M. S. & Woods. H. F. Polymorphic drug oxidation: Pharmacokinetic basis and comparison of experimental indices. Br. J. Clin. Pharmacol. 22, 541-550 (1986).
12. Jackson, P. R. & Tucker, G. T. Pharmacokinetic-pharmacogenetic modelling in the detection of polymorphisms in xenobiotic metabolism. Ann. Occup. Hyg. 34, 653-662 (1990).
13. Jackson, P. R., Tucker, G. T. & Woods, H. F. Backtracking booze with Bayes - the retrospective interpretation of blood alcohol data. Br. J. Clin. Pharmacol. 31, 55-63 (1991).
14. Rostami-Hodjegan, A., Jackson, P. R. & Tucker, G. T. Sensitivity of indirect metrics for assessing 'rate' in bioequivalence studies - moving the 'goalposts' or changing the 'game'. J. Pharm. Sci. 83, 1554-1557 (1994).
15. Rostami-Hodjegan, A., Nurminen, S., Jackson, P. R. & Tucker, G. T. Caffeine urinary metabolite ratios as markers of enzyme activity: A theoretical assessment. Pharmacogenetics 6, 121-149 (1996).
16. Bogaards, J. J. et al. Prediction of interindividual variation in drug plasma levels in vivo from individual enzyme kinetic data and physiologically based pharmacokinetic modeling. Eur. J. Pharm. Sci. 12, 117-124 (2000).
17. Jonsson, F. & Johanson, G. The Bayesian population approach to physiological toxicokinetic-toxicodynamic models - an example using the MCSim software. Toxicol. Lett. 138, 143-150 (2003).
18. MacDonald, A. J., Rostami-Hodjegan, A., Tucker, G. T. & Linkens, D. A. in Proc. 1999 Health Sci. Simul. Conf. 103-108 (eds Andersen, J. G. & Katzper, M.) (Society Comp. Simul. Int., San Francisco, 1999).
19. MacDonald, A. J., Rostami-Hodjegan, A.. Tucker, G. T. & Linkens, D. A. Analysis of solvent central nervous system toxicity and ethanol interactions using a human population physiologically based kinetic and dynamic model. Regul. Toxicol. Pharmacol. 35, 165-176 (2002).
20. Nestorov, I., Gueorguieva, I., Jones, H. M., Houston, B. & Rowland, M. Incorporating measures of variability and uncertainty into the prediction of in vivo hepatic clearance from in vitro data. Drug Metab. Dispos. 30, 276-282 (2002).
21. Yang, J., Rostami-Hodjegan, A. & Tucker, G. T. Prediction of ritonavir interaction with sildenafil (Viagra): Incorporating population variability. Br. J. Clin. Pharmacol. 53, 438P (2002).
22. Jonsson, E. N. & Sheiner, L. B. More efficient clinical trials through use of scientific model-based statistical tests. Clin. Pharmacol. Ther. 72, 603-614 (2002).
23. Ribbing, J. & Jonsson, E. N. Power, selection bias and predictive performance of the Population Pharmacokinetic Covariate model. J. Pharmacokinet. Pharmacodyn. 31, 109-134 (2004).
24. Lave, T., Coassolo, P. & Reigner, B. Prediction of hepatic metabolic clearance based on interspecies allometric scaling techniques and in vitro-in vivo correlations. Clin. Pharmacokinet. 36, 211-231 (1999).
25. Zuegge, J., Schneider, G., Coassolo, P. & Lave, T. Prediction of hepatic metabolic clearance: Comparison and assessment of prediction models. Clin. Pharmocokinet. 40, 553-563 (2001).
26. Pang, K. S. & Chiba, M. in Handbook of Experimental Pharmacology: Pharmacokinetics of Drugs Vol 110 (eds Welling, P. G. & Balant, L. P.) 101-187 (Springer, Berlin, 1994).
27. Wilkinson, G. R. & Shand, D. G. Commentary: A physiological approach to hepatic drug clearance. Clin. Pharmacol. Ther. 18, 377-390 (1975).
28. Proctor, N. J., Tucker, G. T. & Rostami-Hodjegan, A. Predicting drug clearance from recombinantly expressed CYPs: Intersystem extrapolation factors. Xenobiotica 34, 151-178 (2004).
29. Yang, J., Jamei, M., Yeo, K. R., Tucker, G. T. & Rostami-Hodjegan, A. Kinetic values for mechanism-based enzyme inhibition: Assessing the bias introduced by the conventional experimental protocol. Eur. J. Pharm. Sci. 26, 334-340 (2005).
30. Johnson, T. N., Rostami-Hodjegan, A. & Tucker. G. T. Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children. Clin. Pharmacokinet. 45, 931-956 (2006).
31. Rowland-Yeo, K., Rostami-Hodjegan, A. & Tucker, G. T. Abundance of cytochromes P450 in human liver: A meta-analysis. Br. J. Clin. Pharmacol. 57, 687-688 (2004).
32. Inoue, S. et al. Prediction of in vivo drug clearance from in vitro data. II. Potential inter-ethnic differences. Xenobiotica 36, 499-513 (2006).
33. Houston, J. B. & Carlile, D. J. Prediction of hepatic clearance from microsomes, hepatocytes, and liver slices. Drug Metab. Rev. 29 891-922 (1997).
34. Barter, Z. E. 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, 35-45 (2007).
35. Hakooz, N. et al. determination of a human hepatic microsomal scaling factor for predicting in vivo drug clearance. Pharm. Res. 23, 533-539 (2006).
36. Johnson, T. N., Tucker, G. T., Tanner, M. S. & Rostami-Hodjegan, A. Changes in liver volume from birth to adulthood: A meta-analysis. Liver Transpl. 11, 1481-1493 (2005).
37. Guyton, A. C. & Hall J. E. in Textbook of Medical Physiology 9th edn (eds Guyton, A. C. & Hall, J. E.) (WB Saunders Co., Philadelphia, 1996).
38. Mathias, C. J. Effect of food intake on cardiovascular control in patients with impaired autonomic function. J. Neurosci. Methods 34, 193-200 (1990).
39. Matheson, P. J., Wilson, M. A. & Garrison, R. N. Regulation of intestinal blood flow. J. Surg. Res. 93, 182-196 (2000).
40. Bernardi, M. et al. The hemodynamic status of preascitic cirrhosis: An evaluation under steady-state conditions and after postural change. Hepatology 16, 341-346 (1992).
41. Edell, E. S., Cortese, D. A., Krowka, M. J. & Rehder, K. Severe hypoxemia and liver disease. Am. Rev. Respir. Dis. 140, 1631-1635 (1989).
42. Ruckert, K. H. & Juchems, R. [Hemodynamics in posture changes]. Z. Kreislaufforsch. 59, 685-698 (1970) (in German).
43. Wong, F., Liu, P., Allidina, Y. & Blendis, L. The effect of posture on central blood volume in patients with preascitic cirrhosis on a sodium-restricted diet. Hepatology 23, 1141-1147 (1996).
44. Lobell, M. & Sivarajah, V. In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values. Mol. Divers. 7, 69-87 (2003).
45. Ekins, S. & Wrighton, S. A. Application of in silico approaches to predicting drug-drug interactions. J. Pharmacol. Toxicol. Methods 45, 65-69 (2001).
46. Lewis, D. F. Structural characteristics of human P450s involved in drug metabolism: OSARs and lipophilicity profiles. Toxicology 144 197-203 (2000).
47. McNamara, P. J. & Alcorn, J. Protein binding predictions in infants. AAPS PharmSci. 4, e4 (2002).
48. Howgate, E. M., Rowland-Yeo, K., Proctor, N. J., Tucker, G. T. & Rostami-Hodjegan, A. Prediction of in vivo drug clearance from in vitro data. I. Impact of inter-individual variability. Xenobiotica 36, 473-497 (2006).
49. Shiran, M. R. et al. Prediction of metabolic drug clearance in humans: in vitro-in vivo extrapolation vs allometric scaling. Xenobiotica 36, 567-580 (2006).
50. Bjornsson, T. D. et al. A review and assessment of potential sources ofethnic differences in drug responsiveness. J. Clin. Pharmacol. 43, 943-967 (2003).
51. Imaoka, S. et al. Multiple forms of human P450 expressed in Saccharomyces cerevisiae. Systematic characterization and comparison with those of the rat. Biochem. Pharmacol. 51, 1041-1050 (1996).
52. Nakasa, H. et al. Characterization of human liver microsomal cytochrome P450 involved in the reductive metabolism of zonisamide. Mol. Pharmacol. 44, 216-221 (1993).
53. Shu, Y. et al. Interindividual variations in levels and activities of cytochrome P-450 in liver microsomes of Chinese subjects. Acta Pharmacol. Sin. 22, 283-288 (2001).
54. Tateishi, T. et al. A comparison of hepatic cytochrome P450 protein expression between infancy and postinfancy. Life Sci. 61, 2567-2574 (1997).
55. Tateishi, T. et al. No ethnic difference between Caucasian and Japanese hepatic samples in the expression frequency of CYP3A5 and CYP3A7 proteins. Biochem. Pharmacol. 57, 935-939 (1999).
56. Yasumori, T., Murayama, N., Yamazoe, Y. & Kato, R. Polymorphism in hydroxylation of mephenytoin and hexobarbital stereoisomers in relation to hepatic P-450 human-2. Clin. Pharmacol. Ther. 47, 313-322 (1990).
57. Yasumori, T. et al. Cytochrome P450 mediated metabolism of diazepam in human and rat: Involvement of human CYP2C in N demethylation in the substrate concentration-dependent manner. Pharmacogenetics 3, 291-301 (1993).
58. Dickinson, G. L., Lennard, M. S.. Tucker, G. T. & Rostami-Hodjegan, A. The use of mechanistic DM-PK-PD modelling to assess the power of pharmacogenetic studies - CYP2C9 and warfarin as an example. Br. J. Clin. Pharmacol. (in the press).
59. Ito, K., Brown, H. S. & Houston, J. B. Database analyses for the prediction of in vivo drug-drug interactions from in vitro data. Br. J. Clin. Pharmacol. 57, 473-486 (2004).
60. Ito, K., Hallifax, D., Obach, R. S. & Houston, J. B. Impact of parallel pathways of drug elimination and multiple cytochrome P450 involvement on drug-drug interactions: CYP2D6 paradigm. Drug Metab. Dispos. 33 837-844 (2005).
61. Tucker, G. T. The rational selection of drug interaction studies: implications of recent advances in drug metabolism. Int. J. Clin. Pharmacol. Ther. 30, 550-553 (1992).
62. Food and Drug Administration (FDA). Guidance for industry: Drug interaction studies - study design, data analysis, and implications for closing and labeling. FDA web site [online]. (2006).
63. Yang, J. et al. Implications of mechanism-based inhibition of CYP2D6 for the pharmacokinetics and toxicity of MDMA. J. Psychopharmacol. 20, 842-849 (2006).
64. Howgate, E. M.. Rowland Yeo, K., Proctor, N. J., Tucker, G. T. & Rostami Hodjegan, A. Prediction of in vivo drug clearance from in vitro data. I: Impact of inter-individual variability. Xenobiotica. 36, 473-497 (2006).