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Investigating the Theoretical Basis for In Vitro–In Vivo Extrapolation (IVIVE) in Predicting Drug Metabolic Clearance and Proposing Future Experimental Pathways

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Abstract

Extensive studies have been conducted to predict in vivo metabolic clearance from in vitro human liver metabolism parameters (i.e., in vitro–in vivo extrapolation (IVIVE)) with little success. Here, deriving IVIVE from first principles, we show that the product of fraction unbound in the blood and the predicted in vivo intrinsic clearance determined from hepatocyte or microsomal incubations is the lower boundary condition for in vivo hepatic clearance and the prerequisite for IVIVE predictions to be valid, regardless of extraction ratio. For 60–80% of drugs evaluated here, this product is markedly less than the in vivo measured clearance, a result that violates the lower boundary of the predictive relationship. This can only be explained by (a) suboptimal in vitro metabolic stability assay conditions, (b) significant error in the assumption that in vitro intrinsic clearance determinations will predict in vivo intrinsic clearance simply by scaling-up the amount of enzyme (in vitro incubation to in vivo liver), and/or (c) the methods of determining fraction unbound are incorrect. We further suggest that widely employed organ blood flow values underpredict the effective blood flow within the organ by approximately 2.5-fold, thus impacting IVIVE of high clearance compounds. We propose future pathways that should be investigated in terms of the relationship to experimentally measured clearance values, rather than model-dependent intrinsic clearance. IVIVE outcome can be improved by estimating the ratio of unbound drug concentration in the liver tissue to the liver plasma, examining the assumption of the free drug theory (i.e., there are no transporter effects at the blood cell membrane) and the finding that the upper limit of organ clearance may be greater than blood flow entering the organ.

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Abbreviations

ADME:

Absorption, distribution, metabolism, excretion

AUC:

Area under the concentration–time curve

BDDCS:

Biopharmaceutics Drug Disposition Classification System

\( \frac{B}{P} \) :

Blood to plasma partitioning ratio

CL:

Clearance

CLint :

Intrinsic clearance

CYP3A:

Cytochrome P450 3A

D :

Dose

ECCS:

Extended Clearance Classification System

F :

Bioavailability

f u,B :

Fraction of unbound drug in the blood

f u, inc :

Fraction of unbound drug in an in vitro incubation

f u,P :

Fraction of unbound drug in the plasma

IVIVE:

In vitro–in vivo extrapolation

NME:

New molecular entity

Q H :

Hepatic blood flow

References

  1. Page KM. Validation of early human dose prediction: a key metric for compound progression in drug discovery. Mol Pharm. 2015;13:609–20.

    Article  Google Scholar 

  2. Krauss M, Hofmann U, Schafmayer C, Igel S, Schlender J, Mueller C, et al. Translational learning from clinical studies predicts drug pharmacokinetics across patient populations. NPJ Syst Biol Appl. 2017;3:11.

  3. Rané A, Wilkinson GR, Shand DG. Prediction of hepatic clearance ratio from in vitro measurement of intrinsic clearance. J Pharmacol Exp Ther. 1977;200:420–4.

    PubMed  Google Scholar 

  4. Houston JB. Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance. Biochem Pharmacol. 1994;47:1469–79.

    Article  CAS  Google Scholar 

  5. Houston JB. Prediction of hepatic clearance from microsomes, hepatocyte and liver slices. Drug Metab Rev. 1997;29:891–922.

    Article  CAS  Google Scholar 

  6. Iwatsubo T, Hirota N, Ooie T, Suzuki H, Shimada N, Chiba K, et al. Prediction of in vivo drug metabolism in the human liver from in vitro metabolism data. Pharmacol Ther. 1997;73:147–71.

  7. Obach RS, Baxter JG, Liston TE, Silber BM, Jones BC, MacIntyre F, et al. The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J Pharmacol Exp Ther. 1997;283:46–58.

  8. 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. 1999;27:1350–9.

    CAS  PubMed  Google Scholar 

  9. Bowman CM, Benet LZ. Hepatic clearance predictions from in vitro-in vivo extrapolation and the Biopharmaceutics Drug Disposition Classification System. Drug Metab Dispos. 2016;44:1731–5.

    Article  CAS  Google Scholar 

  10. Wood FL, Houston JB, Hallifax D. Clearance prediction methodology needs fundamental improvement: trends common to rat and human hepatocytes/microsomes and implications for experimental methodology. Drug Metab Dispos. 2017;45:1178–88.

    Article  CAS  Google Scholar 

  11. Takano J, Maeda K, Bolger MB, Sugiyama Y. The prediction of the relative importance of CYP3A/P-gp to the nonlinear intestinal absorption of drugs by advanced compartmental absorption and transit model. Drug Metab Dispos. 2016;44:1808–18.

    Article  CAS  Google Scholar 

  12. Chiba M, Ishii Y, Sugiyama Y. Prediction of hepatic clearance in human from in vitro data for successful drug development. AAPS J. 2009;2:262–76.

    Article  Google Scholar 

  13. Wood FL, Houston JB, Hallifax D. Importance of the unstirred water layer and hepatocyte membrane integrity in vitro for quantification of intrinsic metabolic clearance. Drug Metab Dispos. 2018;46:268–78.

    Article  Google Scholar 

  14. Hallifax D, Houston JB. Evaluation of hepatic clearance prediction using in vitro data: emphasis on fraction unbound in plasma and drug ionization using a database of 107 drugs. J Pharm Sci. 2012;101:2645–52.

    Article  CAS  Google Scholar 

  15. Riccardi KA, Tess DA, Lin J, Patel R, Ryu R, Atkinson K, et al. A novel unified approach to predict human hepatic clearance from both enzyme- and transporter-mediated mechanisms using suspended human hepatocytes. Drug Metab Dispos. 2019;47:484–92.

  16. Rowland M, Benet LZ, Graham GG. Clearance concepts in pharmacokinetics. J Pharmacokinet Biopharm. 1973;1:123–35.

    Article  CAS  Google Scholar 

  17. Wilkinson GR, Shand DG. Commentary: a physiologic approach to hepatic drug clearance. Clin Pharmacol Ther. 1975;18:377–90.

    Article  CAS  Google Scholar 

  18. Rowland M, Tozer TN. Clinical pharmacokinetics and pharmacodynamics: concepts and applications. 4th ed. Philadelphia: Lippincott Williams & Wilkens; 2010.

    Google Scholar 

  19. Klotz U, Avant GR, Hoyumpa A, Schenker S, Wilkinson GR. The effects of age and liver disease on the disposition and elimination of diazepam in adult man. J Clin Invest. 1975;55:347–59.

    Article  CAS  Google Scholar 

  20. Mangoni AA, Jackson SHD. Age-related changes in pharmacokinetics and pharmacodynamics: basic principles and practical applications. Br J Clin Pharmacol. 2004;57:6–14.

    Article  CAS  Google Scholar 

  21. Wu C-Y, Benet LZ. Predicting drug disposition via application of BCS: transport/absorption/elimination interplay and development of a Biopharmaceutics Drug Disposition Classification System. Pharm Res. 2005;22:11–23.

    Article  CAS  Google Scholar 

  22. Hallifax D, Houston JP. Use of segregated hepatocyte scaling factors and cross-species relationships to resolve clearance dependence in the prediction of human hepatic clearance. Drug Metab Dispos. 2019;47:320–7.

    Article  CAS  Google Scholar 

  23. Bowman CM, Benet LZ. Interlaboratory variability in human hepatocyte intrinsic clearance values and trends with physicochemical properties. Pharm Res. 2019;36:113.

  24. Bowman CM, Benet LZ. In vitro-in vivo extrapolation and hepatic clearance-dependent underprediction. J Pharm Sci. 2019;108:2500–4.

    Article  CAS  Google Scholar 

  25. Bowman CM, Benet LZ. In vitro-in vivo inaccuracy: the CYP3A4 anomaly. Drug Metab Dispos. 2019;47:1368–71.

    Article  CAS  Google Scholar 

  26. Bowman CM, Benet LZ. An examination of protein binding and protein-facilitated uptake relating to in vitro-in vivo extrapolation. Eur J Pharm Sci. 2018;123:502–14.

    Article  CAS  Google Scholar 

  27. Bowman CM, Okochi H, Benet LZ. The presence of a transporter-induced protein binding shift: a new explanation for protein-facilitated uptake and improvement for in vitro-in vivo extrapolation. Drug Metab Dispos. 2019;47:358–63.

    Article  CAS  Google Scholar 

  28. Benet LZ, Liu S, Wolfe AR. The universally unrecognized assumption in predicting drug clearance and organ extraction ratio. Clin Pharmacol Ther. 2018;103:521–5.

    Article  CAS  Google Scholar 

  29. Lombardo F, Berellini G, Obach RS. Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 1352 drug compounds. Drug Metab Dispos. 2018;46:1466–77.

    Article  CAS  Google Scholar 

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Acknowledgments

The authors thank members of our laboratory Dr. Jin Dong (present address: Simulations Plus), Alan R. Wolfe, Shufang Liu (present address: SUNY Buffalo), and Dr. Giovani Bocci (present address: University of New Mexico) for the very helpful discussions.

Funding

This work was supported in part by a Mary Anne Koda-Kimble Seed Award for Innovation. Dr. Benet is a member of the UCSF Liver Center supported by NIH Grant P30 DK026743. Ms. Sodhi was supported in part by an American Foundation for Pharmaceutical Education Pre-Doctoral Fellowship, NIGMS Grant R25 GM56847 and a Louis Zeh Fellowship.

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  1. Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, 513 Parnassus Ave Rm HSE 1164, UCSF Box 0912, San Francisco, California, 94143, USA

    Leslie Z. Benet & Jasleen K. Sodhi

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  1. Leslie Z. Benet
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  2. Jasleen K. Sodhi
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Correspondence to Leslie Z. Benet.

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Benet, L.Z., Sodhi, J.K. Investigating the Theoretical Basis for In Vitro–In Vivo Extrapolation (IVIVE) in Predicting Drug Metabolic Clearance and Proposing Future Experimental Pathways. AAPS J 22, 120 (2020). https://doi.org/10.1208/s12248-020-00501-9

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