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Prediction of Human Drug Clearance from in Vitro and Preclinical Data Using Physiologically Based and Empirical Approaches

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Purpose.

The aim of this study is to compare the accuracy of five methods for predicting in vivo intrinsic clearance (CLint) and seven for predicting hepatic clearance (CLh) in humans using in vitro microsomal data and/or preclinical animal data.

Methods.

The human CLint was predicted for 33 drugs by five methods that used either in vitro data with a physiologic scaling factor (SF), with an empirical SF, with the physiologic and drug-specific (the ratio of in vivo and in vitro CLint in rats) SFs, or rat CLint directly and with allometric scaling. Using the estimated CLint, the CLh in humans was calculated according to the well-stirred liver model. The CLh was also predicted using additional two methods: using direct allometric scaling or drug-specific SF and allometry.

Results.

Using in vitro human microsomal data with a physiologic SF resulted in consistent underestimation of both CLint and CLh . This bias was reduced by using either an empirical SF, a drug-specific SF, or allometry. However, for allometry, there was a substantial decrease in precision. For drug-specific SF, bias was less reduced, precision was similar to an empirical SF. Both CLint and CLh were best predicted using in vitro human microsomal data with empirical SF. Use of larger data set of 52 drugs with the well-stirred liver model resulted in a best-fit empirical SF that is 9-fold increase on the physiologic SF.

Conclusions.

Overall, the empirical SF method and the drug-specific SF method appear to be the best methods; they show lower bias than the physiologic SF and better precision than allometric approaches. The use of in vitro human microsomal data with an empirical SF may be preferable, as it does not require extra information from a preclinical study.

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Abbreviations

afe:

average fold-error

Bh:

mean body weights of humans

Br:

mean body weights of rats

CLh,h:

hepatic clearance in humans

CLh,r:

hepatic clearance in rats

CLint:

intrinsic clearance

CLh:

hepatic clearance

CLint,h,in vitro:

in vitro intrinsic clearance in humans

CLint,h,in vivo:

in vivo intrinsic clearance in humans

CLint,r,in vitro:

in vitro intrinsic clearance in rats

CLint,r,in vivo:

in vivo intrinsic clearance in rats

fu,p:

plasma unbound fraction

fu,m:

unbound fraction in microsomes

Ka:

affinity constant for the protein

Ka,m:

affinity constant for drug binding in microsomes

Ka,p:

affinity constant for drug binding in plasma

mse :

mean squared prediction error

P:

protein concentration

PB-SF:

physiologically based SF

Qh:

hepatic blood flow

RB:

blood-to-plasma concentration ratio

rmse :

root mean squared prediction error

SF:

scaling factor

References

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

    Google Scholar 

  2. 2. J. B. Houston and D. J. Carlile. Prediction of hepatic clearance from microsomes, hepatocytes, and liver slices. Drug Metab. Rev. 29:891–922 (1997).

    Google Scholar 

  3. 3. T. Iwatsubo, N. Hirota, T. Ooie, H. Suzuki, N. Shimada, K. Chiba, T. Ishizaki, C. E. Green, C. A. Tyson, and Y. Sugiyama. Prediction of in vivo drug metabolism in the human liver from in vitro metabolism data. Pharmacol. Ther. 73:147–171 (1997).

    Google Scholar 

  4. 4. H. Boxenbaum. Interspecies scaling, allometry, physiological time, and the ground plan of pharmacokinetics. J. Pharmacokin. Biopharm. 10:201–227 (1982).

    Google Scholar 

  5. 5. F. Gaspari and M. Bonati. Interspecies metabolism and pharmacokinetic scaling of theophylline disposition. Drug Metab. Rev. 22:179–207 (1990).

    Google Scholar 

  6. 6. I. Mahmood and J. D. Balian. Interspecies scaling: predicting clearance of drugs in humans. Three different approaches. Xenobiotica 9:887–895 (1996).

    Google Scholar 

  7. 7. R. S. Obach, J. G. Baxter, T. E. Liston, B. M. Silber, B. C. Jones, F. MacIntyre, D. J. Rance, and P. Wastall. The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J. Pharmacol. Exp. Ther. 283:46–58 (1997).

    Google Scholar 

  8. 8. W. L. Chiou, G. Robbie, S. M. Chung, T.-C. Wu, and C. Ma. Correlation of plasma clearance of 54 extensively metabolized drugs between humans and rats: Mean allometric coefficient of 0.66. Pharm. Res. 15:1474–1479 (1998).

    Google Scholar 

  9. 9. Y. Naritomi, S. Terashita, S. Kimura, A. Suzuki, A. Kagayama, and Y. Sugiyama. Prediction of human hepatic clearance from in vivo animal experiments and in vitro metabolic studies with liver microsomes from animals and humans. Drug Metab. Dispos. 29: 1316–1324 (2001).

    Google Scholar 

  10. 10. T. Lave, S. Dupin, C. Schmitt, R. C. Chou, D. Jaeck, and P. Coassolo. Integration of in vitro data into allometric scaling to predict hepatic metabolic clearance in man: Application to 10 extensively metabolized drugs. J. Pharm. Sci. 86:584–590 (1997).

    Google Scholar 

  11. 11. J. Zuegge, G. Schneider, P. Coassolo, and T. Lave. Prediction of hepatic metabolic clearance: Comparison and assessment of prediction models. Clin. Pharmacokin. 40:553–563 (2001).

    Google Scholar 

  12. 12. D. J. Carlile, N. Hakooz, M. K. Bayliss, and J. B. Houston. Microsomal prediction of in vivo clearance of CYP2C9 substrate in human. Br. J. Clin. Pharmacol. 47:625–635 (1999).

    Google Scholar 

  13. 13. A. J. Stevens, S. W. Martin, B. S. Brennan, A. McLachlan, L. A. Gifford, M. Rowland, and J. B. Houston. Regional drug delivery II: relationship between drug targeting index and pharmacokinetic parameters for three non-steroidal anti-inflammatory drugs using the rat air pouch model of inflammation. Pharm. Res. 12: 1987–1996 (1995).

    Google Scholar 

  14. 14. R. S. Obach. 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).

    Google Scholar 

  15. 15. T. Iwatsubo, H. Suzuki, and Y. Sugiyama. 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).

    Google Scholar 

  16. 16. M. Chiba, M. Hensleigh, and J. H. Lin. Hepatic and intestinal metabolism of indinavir, an HIV protease inhibitor, in rat and human microsomes. Major role of CYP3A. Biochem. Pharmacol. 53:1187–1195 (1997).

    Google Scholar 

  17. 17. S. K. Balani, E. J. Woolf, V. L. Hoagland, M. G. Sturgill, P. J. Deutsch, K. C. Yeh, and J. H. Lin. Disposition of indinavir, a potent HIV-1 protease inhibitor, after an oral dose in humans. Drug Metab. Dispos. 24:1389–1394 (1996).

    Google Scholar 

  18. 18. J. H. Lin, M. Chiba, S. K. Balani, I.-W. Chen, G. Y.-S. Kwei, K. J. Vastag, and J. A. Nishime. Species differences in the pharmacokinetics and metabolism of indinavir, a potent human immunodeficiency virus protease inhibitor. Drug Metab. Dispos. 24: 1111–1120 (1996).

    Google Scholar 

  19. 19. R. J. Parker, J. M. Collins, and J. M. Strong. Identification of 2,6-xylidine as a major lidocaine metabolite in human liver slices. Drug Metab. Dispos. 24:1167–1173 (1996).

    Google Scholar 

  20. 20. C. M. Dixon, P. V. Colthup, C. J. Serabjit-Singh, B. M. Kerr, C. C. Boehlert, G. R. Park, and M. H. Tarbit. Multiple forms of cytochrome P450 are involved in the metabolism of ondansetron in humans. Drug Metab. Dispos. 23:1225–1230 (1995).

    Google Scholar 

  21. 21. G. Engel, U. Hofmann, H. Heidemann, J. Cosme, and M. Eichelbaum. Antipyrine as a probe for human oxidative drug metabolism: identification of the cytochrome P450 enzymes catalyzing 4-hydroxyantipyrine, 3-hydroxymethylantipyrine, and norantipyrine formation. Clin. Pharmacol. Ther. 59:613–623 (1996).

    Google Scholar 

  22. 22. J. C. Bloomer, S. E. Clarke, and R. J. Chenery. Determination of P4501A2 activity in human liver microsomes using [3-14C-methyl] caffeine. Xenobiotica 25:917–927 (1995).

    Google Scholar 

  23. 23. U. G. Eriksson, J. Lundahl, C. Baarnhielm, and C. G. Regardh. Stereoselective metabolism of felodipine in liver microsomes from rat, dog, and human. Drug Metab. Dispos. 19:889–894 (1991).

    Google Scholar 

  24. 24. S. V. Otton, E. M. Gillam, M. S. Lennard, G. T. Tucker, and H. F. Woods. Propranolol oxidation by human liver microsomes-the use of cumene hydroperoxide to probe isoenzyme specificity and regio- and stereoselectivity. Br. J. Clin. Pharmacol. 30:751–760 (1990).

    Google Scholar 

  25. 25. R. Ishida, S. Obara, Y. Masubuchi, S. Narimatsu, S. Fujita, and T. Suziki. Induction of propranolol metabolism by the azo dye sudan III in rats. Biochem. Pharmacol. 43:2489–2492 (1992).

    Google Scholar 

  26. 26. D. B. Jones, M. S. Ching, R. A. Smallwood, and D. J. Morgan. A carrier-protein receptor is not a prerequisite for avid hepatic elimination of highly bound compounds: A study of propranolol elimination by the isolated perfused rat liver. Hepatology 5:590–593 (1985).

    Google Scholar 

  27. 27. K. S. Pang and M. Rowland. 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. Pharmacokin. Biopharm. 5:625–653 (1977).

    Google Scholar 

  28. 28. R. L. Dedrick. Animal scale-up. J. Pharmacokinet. Biopharm. 1:435–461 (1973).

    Google Scholar 

  29. 29. K. B. Bischoff, R. L. Dedrick, D. S. Zaharko, and J. A. Longstreth. Methotrexate pharmacokinetics. J. Pharm. Sci. 60:1128–1133 (1971).

    Google Scholar 

  30. 30. G. M. Pollack, K. L. R. Brouwer, K. B. Demby, and J. A. Jones. Determination of hepatic blood flow in the rat using sequential infusions of indocyanine green or galactose. Drug Metab. Dispos. 18:197–202 (1999).

    Google Scholar 

  31. 31. K. S. Pang and J. R. Gillette. Complications in the estimation of hepatic blood flow in vivo by pharmacokinetic parameters. Drug Metab. Dispos. 6:567–576 (1978).

    Google Scholar 

  32. 32. L. B. Sheiner and S. L. Beal. Some suggestions for measuring predictive performance J. Pharmacokin. Biopharm. 9:503–512 (1981).

    Google Scholar 

  33. 33. M. G. Soars, B. Burchell, and R. J. Riley. In vitro analysis of human drug glucuronidation and prediction of in vivo metabolic clearance. J. Pharmacol. Exp. Ther. 301:382–390 (2002).

    Google Scholar 

  34. 34. K. Ito and J. B. Houston. 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 (2004).

    Google Scholar 

  35. 35. R. J. Riley. The potential pharmacological and toxicological impact of P450 screening. Curr. Opin. Drug Discov. Devel. 4:45–54 (2001).

    Google Scholar 

  36. 36. P. Poulin. and F-P Theil. Prediction of pharmacokinetics prior to in vivo studies. II. Generic physiologically based pharmacokinetic models for drug disposition. J. Pharm. Sci. 91:1358–1370 (2002).

    Google Scholar 

  37. 37. J. B. Houston and K. E. Kenworthy. In vitro-in vivo scaling of CYP kinetic data not consistent with the classical Michaelis-Menten model. Drug Metab. Dispos. 28:246–254 (2000).

    Google Scholar 

  38. 38. M. F. Paine, M. Khalighi, J. M. Fisher, D. D. Shen, K. L. Kunze, C. L. Marsh, J. D. Perkins, and K. E. Thummel. Characterization of interintestinal and intraintestinal variations in human CYP3A-dependent metabolism. J. Pharmacol. Exp. Ther. 283:1552–1562 (1997).

    Google Scholar 

  39. 39. Q. Y. Zhang, D. Dunbar, A. Ostrowska, S. Zeisloft, J. Yang, and L. S. Kaminsky. Characterization of human small intestinal cytochromes P-450. Drug Metab. Dispos. 27:804–809 (1999).

    Google Scholar 

  40. 40. H. J. Lin, M. Chiba, and T. A. Baillie. Is the role of the small intestine in first-pass metabolism overemphasized? Pharmacol. Rev. 51:135–157 (1999).

    Google Scholar 

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Correspondence to J. Brian Houston.

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Ito, K., Houston, J. Prediction of Human Drug Clearance from in Vitro and Preclinical Data Using Physiologically Based and Empirical Approaches. Pharm Res 22, 103–112 (2005). https://doi.org/10.1007/s11095-004-9015-1

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