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Comparing Mechanistic and Preclinical Predictions of Volume of Distribution on a Large Set of Drugs

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Abstract

Purpose

Volume of distribution at steady state (Vdss) is a fundamental pharmacokinetic (PK) parameter driven predominantly by passive processes and physicochemical properties of the compound. Human Vdss can be estimated using in silico mechanistic methods or empirically scaled from Vdss values obtained from preclinical species. In this study the accuracy and the complementarity of these two approaches are analyzed leveraging a large data set (over 150 marketed drugs).

Methods

For all the drugs analyzed in this study experimental in vitro measurements of LogP, plasma protein binding and pKa are used as input for the mechanistic in silico model to predict human Vdss. The software used for predicting human tissue partition coefficients and Vdss based on the method described by Rodgers and Rowland is made available as supporting information.

Results

This assessment indicates that overall the in silico mechanistic model presented by Rodgers and Rowland is comparably accurate or superior to empirical approaches based on the extrapolation of in vivo data from preclinical species.

Conclusions

These results illustrate the great potential of mechanistic in silico models to accurately predict Vdss in humans. This in silico method does not rely on in vivo data and is, consequently, significantly time and resource sparing. The success of this in silico model further suggests that reasonable predictability of Vdss in preclinical species could be obtained by a similar process.

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Abbreviations

ADME:

Absorption, distribution, metabolism and elimination or excretion

BP:

Blood-to-plasma concentration ratio

cyno:

Cynomologus monkey

fu:

Fraction of compound unbound in plasma

L/kg:

Liter per kilogram

LogP:

The octanol–water partition coefficient

P&T:

Poulin and Theil

PBPK:

Physiologically-based pharmacokinetic modeling

pKa:

The negative base 10 logarithm of the acid dissociation constant

R&R:

Rodgers and Rowland

Vdss :

Volume of distribution at steady state

References

  1. Wagner J. Biopharmaceutics and relevant pharmacokinetics. Hamilton: Drug Intelligence Publications; 1971.

    Google Scholar 

  2. Gibaldi M, Perrier D. Pharmacokinetics. In: Swarbrick J, editor. New York: Marcel Dekker; 1975. p. 175–87.

  3. Riegelman S, Loo J, Rowland M. Concept of a volume of distribution and possible errors in evaluation of this parameter. J Pharm Sci Elsevier Masson SAS. 1968;57:128–33.

    CAS  Google Scholar 

  4. Lombardo F, Obach RS, DiCapua FM, Bakken GA, Lu J, Potter DM, et al. A hybrid mixture discriminant analysis-random forest computational model for the prediction of volume of distribution of drugs in human. J Med Chem. 2006;49(7):2262.

    Article  CAS  PubMed  Google Scholar 

  5. Grover A, Benet LZ. Effects of drug transporters on volume of distribution. AAPS J. 2009;11:250–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Shugarts S, Benet LZ. The role of transporters in the pharmacokinetics of orally administered drugs. Pharm Res. 2009;26:2039–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    CAS  PubMed  Google Scholar 

  8. Lombardo F, Waters NJ, Argikar UA, Dennehy MK, Zhan J, Gunduz M, et al. Comprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 1: volume of distribution at steady state. J Clin Pharmacol. 2013;53:167–77.

    Article  CAS  PubMed  Google Scholar 

  9. Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. J Pharm Sci Elsevier Masson SAS. 2002;91:129–56.

    CAS  Google Scholar 

  10. Berezhkovskiy LM. Volume of distribution at steady state for a linear pharmacokinetic system with peripheral elimination. J Pharm Sci Elsevier Masson SAS. 2004;93:1628–40.

    CAS  Google Scholar 

  11. Rodgers T, Leahy D, Rowland M. Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases. J Pharm Sci Elsevier Masson SAS. 2005;94:1259–76.

    CAS  Google Scholar 

  12. Rodgers T, Rowland M. Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci Elsevier Masson SAS. 2006;95:1238–57.

    CAS  Google Scholar 

  13. Rodgers T, Rowland M. Mechanistic approaches to volume of distribution predictions: understanding the processes. Pharm Res. 2007;24:918–33.

    Article  CAS  PubMed  Google Scholar 

  14. Jones RDO, Jones HM, Rowland M, Gibson CR, Yates JWT, Chien Y, et al. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2 : comparative assessment of prediction methods of human volume of distribution. 2011;100:4074–89.

  15. Manallack DT. The pKa distribution of drugs: application to drug discovery. Perspect Medicin Chem. 2007;1:25–38.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. 2006;34:668–72.

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Uchimira T, Motohiro K, Tomohisa S, Kinoshita H. Prediction of human blood-to-plasma drug concentration ratio. Biopharm Drug Dispos. 2010:286–97.

  19. Poulin P, Theil F. Development of a novel method for predicting human volume of distribution at steady-state of basic drugs and comparative assessment with existing methods. J Pharm Sci Elsevier Masson SAS. 2009;98:4941–61.

    CAS  Google Scholar 

  20. R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria. 2015. Available from: http://www.r-project.org/.

  21. RStudio I. shiny: Easy web applications in R [Internet]. 2017. Available from: http://shiny.rstudio.com/.

  22. Abduljalil K, Edwards D, Barnett A, Rose RH, Cain T, Jamei M. A tutorial on pharmacodynamic scripting facility in Simcyp. CPT Pharmacometrics Syst Pharmacol. 2016;5:455–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. EC K, Moss AM, Ishisa K, Govindarajan R, Unadkat JD. The role of the equilibrative nucleoside transporter 1 on tissue and fetal distribution of ribavirin in the mouse. Biopharm Drug Dispos. 2016:336–4.

  24. Roberts MS, Magnusson BM, Burczynski FJ, Weiss M. Enterohepatic circulation: physiological, pharmacokinetic and clinical implications. Clin Pharmacokinet. 2002;41:751–90.

    Article  CAS  PubMed  Google Scholar 

  25. Manallack DT, Prankerd RJ, Yuriev E, Oprea TI, David K. The significance of acid/base properties in drug discovery. Chem Soc Rev. 2014;42:485–96.

    Article  Google Scholar 

  26. Berry LM, Li C, Zhao Z, Al BET. Species differences in distribution and prediction of human Vss from preclinical data. Drug Metab Dispos. 2011;39:2103–6.

    Article  CAS  PubMed  Google Scholar 

  27. Haddad S, Poulin P, Krishnan K. Relative lipid content as the sole mechanistic determinant of the adipose tissue: blood partition coefficients of highly lipophilic organic chemicals. Chemosphere. 2000;40:839–43.

    Article  CAS  PubMed  Google Scholar 

  28. Poulin P, Haddad S. Advancing prediction of tissue distribution and volume of distribution of highly lipophilic compounds from a simplified tissue-composition-based model as a mechanistic animal alternative method. J Pharm Sci. Elsevier Masson SAS. 2012;101:2250–61.

    CAS  Google Scholar 

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Acknowledgments and Disclosures

Rosa Chan was supported by the American Association of Pharmaceutical Scientists Graduate Fellowship, American Foundation for Pharmaceutical Education Pre-Doctoral Fellowship, North American Graduate Fellowship from the American College of Toxicology, and NIGMS grant R25 GM56847. All authors declare no conflict of interest.

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Authors and Affiliations

  1. Department of Bioengineering and Therapeutic Sciences, School of Pharmacy and Medicine, University of California San Francisco, San Francisco, California, USA

    Rosa Chan

  2. Drug Metabolism & Pharmacokinetics Genentech, Inc, 1 DNA Way, South San Francisco, California, 94080-4990, USA

    Tom De Bruyn, Matthew Wright & Fabio Broccatelli

Authors
  1. Rosa Chan
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  2. Tom De Bruyn
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  3. Matthew Wright
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  4. Fabio Broccatelli
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Corresponding author

Correspondence to Fabio Broccatelli.

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Chan, R., De Bruyn, T., Wright, M. et al. Comparing Mechanistic and Preclinical Predictions of Volume of Distribution on a Large Set of Drugs. Pharm Res 35, 87 (2018). https://doi.org/10.1007/s11095-018-2360-2

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  • DOI: https://doi.org/10.1007/s11095-018-2360-2

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