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Interlaboratory Variability in Human Hepatocyte Intrinsic Clearance Values and Trends with Physicochemical Properties

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Abstract

Purpose

To examine the interlaboratory variability in CLint values generated with human hepatocytes and determine trends in variability and clearance prediction accuracy using physicochemical and pharmacokinetic parameters.

Methods

Data for 50 compounds from 14 papers were compiled with physicochemical and pharmacokinetic parameter values taken from various sources.

Results

Coefficients of variation were as high as 99.8% for individual compounds and variation was not dependent on the number of prediction values included in the analysis. When examining median values, it appeared that compounds with a lower number of rotatable bonds had more variability. When examining prediction uniformity, those compounds with uniform in vivo underpredictions had higher CLint, in vivo values, while those with non-uniform predictions typically had lower CLint, in vivo values. Of the compounds with uniform predictions, only a small number were uniformly predicted accurately. Based on this limited dataset, less lipophilic, lower intrinsic clearance, and lower protein binding compounds yield more accurate clearance predictions.

Conclusions

Caution should be taken when compiling in vitro CLint values from different laboratories as variations in experimental procedures (such as extent of shaking during incubation) may yield different predictions for the same compound. The majority of compounds with uniform in vitro values had predictions that were inaccurate, emphasizing the need for a better mechanistic understanding of IVIVE. The non-uniform predictions, often with low turnover compounds, reaffirmed the experimental challenges for drugs in this clearance range. Separating new chemical entities by lipophilicity, intrinsic clearance, and protein binding may help instill more confidence in IVIVE predictions.

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Abbreviations

BDDCS :

Biopharmaceutics Drug Disposition Classification System

CL int :

Intrinsic clearance

CL H :

Hepatic clearance

CV :

Coefficient of variation

fu :

Fraction unbound

HBA :

Number of hydrogen bond acceptors

HBD :

Number of hydrogen bond donors

IVIVE :

In vitro to in vivo extrapolation

MRT :

Mean residence time

MW :

Molecular weight

PSA :

Polar surface area

VD ss :

Steady state volume of distribution

References

  1. Bowman CM, Benet LZ. Hepatic clearance predictions from in vitro-in vivo extrapolation and the biopharmaceuitcs drug disposition classification system. Drug Metab Dispos. 2016;44:1731–5.

    Article  CAS  Google Scholar 

  2. 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 

  3. Sohlenius-Sternbeck AK, Jones C, Ferguson D, Middleton BJ, Projean D, Floby E, et al. Practical use of the regression offset approach for the prediction of in vivo intrinsic clearance from hepatocytes. Xenobiotica. 2012;42:841–53.

    Article  CAS  Google Scholar 

  4. Nagilla R, Frank KA, Jolivette LJ, Ward KW. Investigation of the utility of published in vitro intrinsic clearance data for prediction of in vivo clearance. J Pharmacol Toxicol Methods. 2006;53:106–16.

    Article  CAS  Google Scholar 

  5. Stringer R, Nicklin PL, Houston JB. Reliability of human cryopreserved hepatocytes and liver microsomes as in vitro systems to predict metabolic clearance. Xenobiotica. 2008;38:1313–29.

    Article  CAS  Google Scholar 

  6. Hakooz N, Ito K, Rawden H, Gill H, Lemmers L, Boobis AR, et al. Determination of a human hepatic microsomal scaling factor for predicting in vivo drug clearance. Pharm Res. 2006;23:533–9.

    Article  CAS  Google Scholar 

  7. Barter ZE, Bayliss MK, Beaune PH, Boobis AR, Carlile DJ, Edwards RJ, 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 liver. Curr Drug Metab. 2007;8:33–45.

    Article  CAS  Google Scholar 

  8. Hallifax D, Galetin A, Houston JB. Prediction of metabolic clearance using fresh human hepatocytes: comparison with cryopreserved hepatocytes and hepatic microsomes for five benzodiazepines. Xenobiotica. 2008;38:353–67.

    Article  CAS  Google Scholar 

  9. Floby E, Johansson J, Hoogstraate J, Hewitt NJ, Hill J, Sohlenius-Sternbeck A-K. Comparison of intrinsic metabolic clearance in fresh and cryopreserved human hepatocytes. Xenobiotica. 2009;39:656–62.

    Article  CAS  Google Scholar 

  10. Akabane T, Gerst N, Naritomi Y, Masters JN, Tamura K. A practical and direct comparison of intrinsic metabolic clearance of several non-CYP enzyme substrates in freshly isolated and cryopreserved hepatocytes. Drug Metab Pharmacokinet. 2012;27:181–91.

    Article  CAS  Google Scholar 

  11. Blanchard N, Alexandre E, Abadie C, Lavé T, Heyd B, Mantion G, et al. Comparison of clearance predictions using primary cultures and suspensions of human hepatocytes. Xenobiotica. 2005;35:1–15.

    Article  CAS  Google Scholar 

  12. Hallifax D, Rawden HC, Hakooz N, Houston JB. Prediction of metabolic clearance using cryopreserved human hepatocytes: kinetic characteristics for five benzodiazepines. Drug Metab Dispos. 2005;33:1852–8.

    CAS  PubMed  Google Scholar 

  13. Jacobson L, Middleton B, Holmgren J, Eirefelt S, Fröjd M, Blomgren A, et al. An optimized automated assay for determination of metabolic stability using hepatocytes: assay validation, variance component analysis, and in vivo relevance. Assay Drug Dev Technol. 2007;5:403–15.

    Article  CAS  Google Scholar 

  14. Lau YY, Sapidou E, Cui X, White RE, Cheng K-C. Development of a novel in vitro model to predict hepatic clearance using fresh, cryopreserved, and sandwich-cultured hepatocytes. Drug Metab Dispos. 2002;30:1446–54.

    Article  CAS  Google Scholar 

  15. Lu C, Li P, Gallegos R, Uttamsingh V, Xia CQ, Miwa GT, et al. Comparison of intrinsic clearance in liver microsomes and hepatocytes from rats and humans: evaluation of free fraction and uptake in hepatocytes. Drug Metab Dispos. 2006;34:1600–5.

    Article  CAS  Google Scholar 

  16. McGinnity DF, Soars MG, Urbanowicz RA, Riley RJ. Evaluation of fresh and cryopreserved hepatocytes as in vitro drug metabolism tools for the prediction of metabolic clearance. Drug Metab Dispos. 2004;32:1247–53.

    Article  CAS  Google Scholar 

  17. Naritomi Y, Terashita S, Kagayama A, Sugiyama Y. Utility of hepatocytes in predicting drug metabolism: comparison of hepatic intrinsic clearance in rats and humans in vivo and in vitro. Drug Metab Dispos. 2003;31:580–8.

    Article  CAS  Google Scholar 

  18. 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. 2005;33:1304–11.

    Article  CAS  Google Scholar 

  19. Soars MG, Burchell B, Riley RJ. In vitro analysis of human drug glucuronidation and prediction of in vivo metabolic clearance. J Pharmacol Exp Ther. 2002;301:382–90.

    Article  CAS  Google Scholar 

  20. Sohlenius-Sternbeck A-K, Afzelius L, Prusis P, Neelissen J, Hoogstraate J, Johansson J, et al. Evaluation of the human prediction of clearance from hepatocyte and microsome intrinsic clearance for 52 drug compounds. Xenobiotica. 2010;40:637–49.

    Article  CAS  Google Scholar 

  21. Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, et al. The ChEMBL database in 2017. Nucleic Acids Res. 2017;45:D945–54.

    Article  CAS  Google Scholar 

  22. Obach RS, Lombardo F, Waters NJ. Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metab Dispos. 2008;36:1385–405.

    Article  CAS  Google Scholar 

  23. Benet LZ, Broccatelli F, Oprea TI. BDDCS applied to over 900 drugs. AAPS J. 2011;13:519–47.

    Article  CAS  Google Scholar 

  24. Hosey CM, Chan R, Benet LZ. BDDCS predictions, self-correcting aspects of BDDCS assignments, BDDCS assignment corrections, and classification for more than 175 additional drugs. AAPS J. 2016;18:251–60.

    Article  CAS  Google Scholar 

  25. El-Kattan AF, Varma MV, Steyn SJ, Scott DO, Maurer TS, Bergman A. Projecting ADME behavior and drug-drug interactions in early discovery and development: application of the extended clearance classification system. Pharm Res. 2016;33:3021–33.

    Article  CAS  Google Scholar 

  26. Houston JB, Carlile DJ. Prediction of hepatic clearance from microsomes, hepatocytes, and liver slices. Drug Metab Rev. 1997;29:891–922.

    Article  CAS  Google Scholar 

  27. 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.

    CAS  PubMed  Google Scholar 

  28. Verber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45:2615–23.

  29. 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 

  30. Clark DE. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 1. Prediction of intestinal absorption. J Pharm Sci. 1999;88:807–14.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  32. Foster JA, Houston JB, Hallifax D. Comparison of intrinsic clearances in human liver microsomes and suspended hepatocytes from the same donor livers: clearance-dependent relationship and implications for prediction of in vivo clearance. Xenobiotica. 2011;41:124–36.

    Article  CAS  Google Scholar 

  33. Di L, Obach RS. Addressing the challenges of low clearance in drug research. AAPS J. 2015;17:352–7.

    Article  CAS  Google Scholar 

  34. Baker M, Parton T. Kinetic determinants of hepatic clearance: plasma protein binding and hepatic uptake. Xenobiotica. 2007;37:1110–34.

    Article  CAS  Google Scholar 

  35. Soars MG, McGinnity DF, Grime K, Riley RJ. The pivotal role of hepatocytes in drug discovery. Chem Biol Interact. 2007;168:2–15.

    Article  CAS  Google Scholar 

Download references

Acknowledgments and Disclosures

CMB was supported by the National Science Foundation Graduate Research Fellowship Program [Grant 1144247] and a Pharmaceutical Research and Manufacturers of America Foundation Pre-doctoral Fellowship in Pharmaceutics; LZB is a member of the UCSF Liver Center supported by NIH Grant [P30 DK026743].

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Correspondence to Leslie Z. Benet.

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Bowman, C.M., Benet, L.Z. Interlaboratory Variability in Human Hepatocyte Intrinsic Clearance Values and Trends with Physicochemical Properties. Pharm Res 36, 113 (2019). https://doi.org/10.1007/s11095-019-2645-0

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