Skip to main content
SpringerLink
Log in

Albumin-Mediated Uptake Improves Human Clearance Prediction for Hepatic Uptake Transporter Substrates Aiding a Mechanistic In Vitro-In Vivo Extrapolation (IVIVE) Strategy in Discovery Research

  • Research Article
  • Published:
The AAPS Journal Aims and scope Submit manuscript

Abstract

This study focused on exploring various in vitro to in vivo extrapolation (IVIVE) approaches with the primary goal of improving human hepatic clearance (CL) prediction for OATP substrates. To that effect, the impact of albumin-mediated uptake in human hepatocytes was investigated. In vitro hepatic uptake assay using suspended human hepatocytes was performed with 16 selected OATP substrates to determine the uptake CL in the absence and presence of 4% BSA and unbound hepatocyte to media partition coefficient (Kpuu). Substantial enhancement of the unbound uptake CL (PSu,inf) was observed in the presence of 4% BSA, demonstrating “albumin-mediated” uptake. Prediction of human hepatic CL was performed using two non-traditional IVIVE approaches: initial uptake CL (PSu,inf) and intrinsic metabolic CL (CLint,met) corrected by Kpuu based on extended clearance concept. Compared to traditional IVIVE using CLint,met only, the two tested IVIVE approaches significantly improved the prediction of human hepatic CL. Particularly, direct extrapolation from PSu,inf (+BSA) showed the most robust correlation with in vivo human hepatic CL for all 16 compounds with bias of 1.9–2.0 for two lots of human hepatocytes, respectively. In addition, PSu,inf (+BSA) and Kpuu were also determined in suspended cynomolgus monkey hepatocytes. Prediction of monkey hepatic CL was improved by both approaches, although with more bias compared to human. These results suggested supplementing 4% BSA in human hepatocyte uptake assay provides a useful tool to characterize hepatic uptake CL for OATP substrates, enabling more accurate human CL prediction without any empirical scaling factor (ESF).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

AAFE:

Absolute average fold error

BSA:

Bovine serum albumin

CL:

Clearance

CLint,all :

Hepatic intrinsic clearance

CLint,met :

Intrinsic hepatic metabolic clearances

CYP:

Cytochrome P450

DDI:

Drug-drug interaction

ECCS:

Extended clearance classification system

ESF:

Empirical scaling factor

fu,p :

The unbound fraction in plasma

fu,hep :

The unbound fraction in hepatocyte suspension incubation

fu,liver tissue :

The unbound fraction in liver tissue

fu,KHB w 4%,BSA :

Unbound fraction in KHB buffer containing 4% BSA

IVIVE:

In vitro to in vivo extrapolation

KHB:

Krebs-Henseleit buffer

Kpuu,ss :

Unbound hepatocytes to media concentration ratio at steady state

LC-MS/MS:

Liquid chromatography-tandem mass spectrometry

OATP:

Organic anion-transporting polypeptide

PSinf :

Hepatic uptake clearance for total drug

PSu,inf :

Unbound hepatic uptake clearance

PK:

Pharmacokinetics

RMSLE:

Root mean squared logarithmic error

References

  1. Patilea-Vrana G, Unadkat JD. Transport vs. metabolism: what determines the pharmacokinetics and pharmacodynamics of drugs? Insights from the extended clearance model. Clin Pharmacol Ther. 2016;100(5):413–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Kusuhara H, Sugiyama Y. In vitro-in vivo extrapolation of transporter-mediated clearance in the liver and kidney. Drug Metab Pharmacokinet. 2009;24(1):37–52.

    CAS  PubMed  Google Scholar 

  3. Shitara Y, Horie T, Sugiyama Y. Transporters as a determinant of drug clearance and tissue distribution. Eur J Pharm Sci. 2006;27(5):425–46.

    CAS  PubMed  Google Scholar 

  4. Varma MV, Steyn SJ, Allerton C, El-Kattan AF. Predicting clearance mechanism in drug discovery: extended clearance classification system (ECCS). Pharm Res. 2015;32(12):3785–802.

    CAS  PubMed  Google Scholar 

  5. Di L, Keefer C, Scott DO, Strelevitz TJ, Chang G, Bi YA, et al. Mechanistic insights from comparing intrinsic clearance values between human liver microsomes and hepatocytes to guide drug design. Eur J Med Chem. 2012;57:441–8.

    CAS  PubMed  Google Scholar 

  6. Hallifax D, Foster JA, Houston JB. Prediction of human metabolic clearance from in vitro systems: retrospective analysis and prospective view. Pharm Res. 2010;27(10):2150–61.

    CAS  PubMed  Google Scholar 

  7. Kilford PJ, Stringer R, Sohal B, Houston JB, Galetin A. Prediction of drug clearance by glucuronidation from in vitro data: use of combined cytochrome P450 and UDP-glucuronosyltransferase cofactors in alamethicin-activated human liver microsomes. Drug Metab Dispos. 2009;37(1):82–9.

    CAS  PubMed  Google Scholar 

  8. 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(11):1731–5.

    CAS  PubMed  Google Scholar 

  9. 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(11):1178–88.

    CAS  PubMed  Google Scholar 

  10. Watanabe T, Kusuhara H, Watanabe T, Debori Y, Maeda K, Kondo T, et al. Prediction of the overall renal tubular secretion and hepatic clearance of anionic drugs and a renal drug-drug interaction involving organic anion transporter 3 in humans by in vitro uptake experiments. Drug Metab Dispos. 2011;39(6):1031–8.

    CAS  PubMed  Google Scholar 

  11. Jones HM, Barton HA, Lai Y, Bi YA, Kimoto E, Kempshall S, et al. Mechanistic pharmacokinetic modeling for the prediction of transporter-mediated disposition in humans from sandwich culture human hepatocyte data. Drug Metab Dispos. 2012;40(5):1007–17.

    CAS  PubMed  Google Scholar 

  12. Poulin P, Burczynski FJ, Haddad S. The role of extracellular binding proteins in the cellular uptake of drugs: impact on quantitative in vitro-to-in vivo extrapolations of toxicity and efficacy in physiologically based pharmacokinetic-pharmacodynamic research. J Pharm Sci. 2016;105(2):497–508.

    CAS  PubMed  Google Scholar 

  13. Poulin P, Haddad S. Extrapolation of the hepatic clearance of drugs in the absence of albumin in vitro to that in the presence of albumin in vivo: comparative assessment of 2 extrapolation models based on the albumin-mediated hepatic uptake theory and limitations and mechanistic insights. J Pharm Sci. 2018;107(7):1791–7.

    CAS  PubMed  Google Scholar 

  14. Poulin P. Prediction of total hepatic clearance by combining metabolism, transport, and permeability data in the in vitro-in vivo extrapolation methods: emphasis on an apparent fraction unbound in liver for drugs. J Pharm Sci. 2013;102(7):2085–95.

    CAS  PubMed  Google Scholar 

  15. Kim SJ, Lee KR, Miyauchi S, Sugiyama Y. Extrapolation of in vivo hepatic clearance from in vitro uptake clearance by suspended human hepatocytes for anionic drugs with high binding to human albumin: improvement of in vitro-to-in vivo extrapolation by considering the “albumin-mediated” hepatic uptake mechanism on the basis of the “facilitated-dissociation model”. Drug Metab Dispos. 2019;47(2):94–103.

    CAS  PubMed  Google Scholar 

  16. Miyauchi S, Masuda M, Kim SJ, Tanaka Y, Lee KR, Iwakado S, et al. The phenomenon of albumin-mediated hepatic uptake of organic anion transport polypeptide substrates: prediction of the in vivo uptake clearance from the in vitro uptake by isolated hepatocytes using a facilitated-dissociation model. Drug Metab Dispos. 2018;46(3):259–67.

    CAS  PubMed  Google Scholar 

  17. 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(4):358–63.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Li N, Badrinarayanan A, Li X, Roberts J, Hayashi M, Virk M, et al. Comparison of in vitro to in vivo extrapolation approaches for predicting transporter-mediated hepatic uptake clearance using suspended rat hepatocytes. Drug Metab Dispos. 2020.

  19. Chu X, Korzekwa K, Elsby R, Fenner K, Galetin A, Lai Y, et al. Intracellular drug concentrations and transporters: measurement, modeling, and implications for the liver. Clin Pharmacol Ther. 2013;94(1):126–41.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Matsunaga N, Ufuk A, Morse BL, Bedwell DW, Bao J, Mohutsky MA, et al. Hepatic organic anion transporting polypeptide-mediated clearance in the beagle dog: assessing in vitro-in vivo relationships and applying cross-species empirical scaling factors to improve prediction of human clearance. Drug Metab Dispos. 2019;47(3):215–26.

    CAS  PubMed  Google Scholar 

  21. Yabe Y, Galetin A, Houston JB. Kinetic characterization of rat hepatic uptake of 16 actively transported drugs. Drug Metab Dispos. 2011;39(10):1808–14.

    CAS  PubMed  Google Scholar 

  22. Watanabe T, Kusuhara H, Maeda K, Kanamaru H, Saito Y, Hu Z, et al. Investigation of the rate-determining process in the hepatic elimination of HMG-CoA reductase inhibitors in rats and humans. Drug Metab Dispos. 2010;38(2):215–22.

    CAS  PubMed  Google Scholar 

  23. De Bruyn T, Ufuk A, Cantrill C, Kosa RE, Bi YA, Niosi M, et al. Predicting human clearance of organic anion transporting polypeptide substrates using cynomolgus monkey: in vitro-in vivo scaling of hepatic uptake clearance. Drug Metab Dispos. 2018;46(7):989–1000.

    PubMed  Google Scholar 

  24. Shen H, Liu T, Jiang H, Titsch C, Taylor K, Kandoussi H, et al. Cynomolgus monkey as a clinically relevant model to study transport involving renal organic Cation transporters: in vitro and in vivo evaluation. Drug Metab Dispos. 2016;44(2):238–49.

    PubMed  Google Scholar 

  25. Shen H, Yang Z, Mintier G, Han YH, Chen C, Balimane P, et al. Cynomolgus monkey as a potential model to assess drug interactions involving hepatic organic anion transporting polypeptides: in vitro, in vivo, and in vitro-to-in vivo extrapolation. J Pharmacol Exp Ther. 2013;344(3):673–85.

    CAS  PubMed  Google Scholar 

  26. Takahashi T, Ohtsuka T, Yoshikawa T, Tatekawa I, Uno Y, Utoh M, et al. Pitavastatin as an in vivo probe for studying hepatic organic anion transporting polypeptide-mediated drug-drug interactions in cynomolgus monkeys. Drug Metab Dispos. 2013;41(10):1875–82.

    CAS  PubMed  Google Scholar 

  27. Wang L, Prasad B, Salphati L, Chu X, Gupta A, Hop CE, et al. Interspecies variability in expression of hepatobiliary transporters across human, dog, monkey, and rat as determined by quantitative proteomics. Drug Metab Dispos. 2015;43(3):367–74.

    PubMed  Google Scholar 

  28. Varma MV, El-Kattan AF, Feng B, Steyn SJ, Maurer TS, Scott DO, et al. Extended clearance classification system (ECCS) informed approach for evaluating investigational drugs as substrates of drug transporters. Clin Pharmacol Ther. 2017;102(1):33–6.

    CAS  PubMed  Google Scholar 

  29. Nakai D, Kumamoto K, Sakikawa C, Kosaka T, Tokui T. Evaluation of the protein binding ratio of drugs by a micro-scale ultracentrifugation method. J Pharm Sci. 2004;93(4):847–54.

    CAS  PubMed  Google Scholar 

  30. Riccardi K, Ryu S, Lin J, Yates P, Tess D, Li R, et al. Comparison of species and cell-type differences in fraction unbound of liver tissues, hepatocytes, and cell lines. Drug Metab Dispos. 2018;46(4):415–21.

    CAS  PubMed  Google Scholar 

  31. Kalvass JC, Maurer TS, Pollack GM. Use of plasma and brain unbound fractions to assess the extent of brain distribution of 34 drugs: comparison of unbound concentration ratios to in vivo p-glycoprotein efflux ratios. Drug Metab Dispos. 2007;35(4):660–6.

    CAS  PubMed  Google Scholar 

  32. Shitara Y, Maeda K, Ikejiri K, Yoshida K, Horie T, Sugiyama Y. Clinical significance of organic anion transporting polypeptides (OATPs) in drug disposition: their roles in hepatic clearance and intestinal absorption. Biopharm Drug Dispos. 2013;34(1):45–78.

    CAS  PubMed  Google Scholar 

  33. Riccardi K, Lin J, Li Z, Niosi M, Ryu S, Hua W, et al. Novel method to predict in vivo liver-to-plasma Kpuu for OATP substrates using suspension hepatocytes. Drug Metab Dispos. 2017;45(5):576–80.

    CAS  PubMed  Google Scholar 

  34. Izumi S, Nozaki Y, Komori T, Takenaka O, Maeda K, Kusuhara H, et al. Comparison of the predictability of human hepatic clearance for organic anion transporting polypeptide substrate drugs between different in vitro-in vivo extrapolation approaches. J Pharm Sci. 2017;106(9):2678–87.

    CAS  PubMed  Google Scholar 

  35. Riccardi KA, Tess DA, Lin J, Patel R, Ryu S, Atkinson K, et al. A novel unified approach to predict human hepatic clearance for both enzyme- and transporter-mediated mechanisms using suspended human hepatocytes. Drug Metab Dispos. 2019;47(5):484–92.

    CAS  PubMed  Google Scholar 

  36. Jones HM, Butt RP, Webster RW, Gurrell I, Dzygiel P, Flanagan N, et al. Clinical micro-dose studies to explore the human pharmacokinetics of four selective inhibitors of human Nav1.7 voltage-dependent sodium channels. Clin Pharmacokinet. 2016;55(7):875–87.

    CAS  PubMed  Google Scholar 

  37. Chang JH, Chen YC, Cheong J, Jones RS, Pang J. Investigating the impact of albumin on the liver uptake of pitavastatin and warfarin in Nagase analbuminemic rats. Drug Metab Dispos. 2019;47(11):1307–13.

    CAS  PubMed  Google Scholar 

  38. Bteich M, Poulin P, Piette S, Haddad S. Impact of extensive plasma protein binding on the in situ hepatic uptake and clearance of perampanel and fluoxetine in Sprague Dawley rats. J Pharm Sci. 2020;109(10):3190–205.

    CAS  PubMed  Google Scholar 

  39. Bowman CM, Chen E, Chen L, Chen YC, Liang X, Wright M, et al. Changes in organic anion transporting polypeptide uptake in HEK293 overexpressing cells in the presence and absence of human plasma. Drug Metab Dispos. 2020;48(1):18–24.

    CAS  PubMed  Google Scholar 

  40. Bteich M, Poulin P, Haddad S. The potential protein-mediated hepatic uptake: discussion on the molecular interactions between albumin and the hepatocyte cell surface and their implications for the in vitro-to-in vivo extrapolations of hepatic clearance of drugs. Expert Opin Drug Metab Toxicol. 2019;15(8):633–58.

    CAS  PubMed  Google Scholar 

  41. Chu X, Shih SJ, Shaw R, Hentze H, Chan GH, Owens K, et al. Evaluation of cynomolgus monkeys for the identification of endogenous biomarkers for hepatic transporter inhibition and as a translatable model to predict pharmacokinetic interactions with statins in humans. Drug Metab Dispos. 2015;43(6):851–63.

    CAS  PubMed  Google Scholar 

  42. Iwasaki K, Uno Y. Cynomolgus monkey CYPs: a comparison with human CYPs. Xenobiotica. 2009;39(8):578–81.

    CAS  PubMed  Google Scholar 

  43. Kimoto E, Bi YA, Kosa RE, Tremaine LM, Varma MVS. Hepatobiliary clearance prediction: species scaling from monkey, dog, and rat, and in vitro-in vivo extrapolation of Sandwich-cultured human hepatocytes using 17 drugs. J Pharm Sci. 2017;106(9):2795–804.

    CAS  PubMed  Google Scholar 

  44. Tse FL, Smith HT, Ballard FH, Nicoletti J. Disposition of fluvastatin, an inhibitor of HMG-COA reductase, in mouse, rat, dog, and monkey. Biopharm Drug Dispos. 1990;11(6):519–31.

    CAS  PubMed  Google Scholar 

  45. Mosure KW, Knipe JO, Browning M, Arora V, Shu YZ, Phillip T, et al. Preclinical pharmacokinetics and in vitro metabolism of Asunaprevir (BMS-650032), a potent hepatitis C virus NS3 protease inhibitor. J Pharm Sci. 2015;104(9):2813–23.

    CAS  PubMed  Google Scholar 

  46. Francon D, Riff C, Blin O, Cohen M, Guilhaumou R. Prolonged continuous wound infiltration with a local anaesthetic after total mastectomy: pharmacokinetics and preliminary results on postoperative pain. Anaesth Crit Care Pain Med. 2019;38(4):385–6.

    PubMed  Google Scholar 

Download references

Acknowledgments

Authors would like to thank Dan Rock, Jan Wahlstrom, and Dean Hickman for their support of this work.

Author information

Authors and Affiliations

  1. Department of Pharmacokinetics and Drug Metabolism, Amgen Research, Amgen Inc, 360 Binney St, Cambridge, Massachusetts, 02142, USA

    Na Li, Akshay Badrinarayanan, Kazuya Ishida, Xingwen Li, John Roberts, Shuai Wang, Mike Hayashi & Anshul Gupta

Authors
  1. Na Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Akshay Badrinarayanan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kazuya Ishida
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xingwen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. John Roberts
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shuai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mike Hayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Anshul Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Participated in research design: Na Li and Anshul Gupta.

Conducted experiments: Na Li, Akshay Badrinarayanan, Xingwen Li, John Roberts, Shuai Wang, and Mike Hayashi.

Performed data analysis: Na Li and Kazuya Ishida.

Wrote or contributed to the writing of the manuscript: Na Li, Akshay Badrinarayanan, John Roberts, Mike Hayashi, and Anshul Gupta.

Corresponding authors

Correspondence to Na Li or Anshul Gupta.

Ethics declarations

The Institutional Animal Care and Use Committee (IACUC) at Charles River laboratories approved the animal experiments.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 387 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, N., Badrinarayanan, A., Ishida, K. et al. Albumin-Mediated Uptake Improves Human Clearance Prediction for Hepatic Uptake Transporter Substrates Aiding a Mechanistic In Vitro-In Vivo Extrapolation (IVIVE) Strategy in Discovery Research. AAPS J 23, 1 (2021). https://doi.org/10.1208/s12248-020-00528-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1208/s12248-020-00528-y

KEY WORDS

Search

Navigation