Skip to main content

Advertisement

SpringerLink
Log in

Methods to Predict Volume of Distribution

  • Molecular Drug Disposition (B Joshi, Section Editor)
  • Published:
Current Pharmacology Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Prior to human studies, knowledge of drug disposition in the body is useful to inform decisions on drug safety and efficacy, first in human dosing, and dosing regimen design. It is therefore of interest to develop predictive models for primary pharmacokinetic parameters, clearance, and volume of distribution. The volume of distribution of a drug is determined by the physiological properties of the body and physiochemical properties of the drug, and is used to determine secondary parameters, including the half-life. The purpose of this review is to provide an overview of current methods for the prediction of volume of distribution of drugs, discuss a comparison between the methods, and identify deficiencies in current predictive methods for future improvement.

Recent Findings

Several volumes of distribution prediction methods are discussed, including preclinical extrapolation, physiological methods, tissue composition-based models to predict tissue:plasma partition coefficients, and quantitative structure-activity relationships. Key factors that impact the prediction of volume of distribution, such as permeability, transport, and accuracy of experimental inputs, are discussed. A comparison of current methods indicates that in general, all methods predict drug volume of distribution with an absolute average fold error of 2-fold. Currently, the use of composition-based PBPK models is preferred to models requiring in vivo input.

Summary

Composition-based models perfusion-limited PBPK models are commonly used at present for prediction of tissue:plasma partition coefficients and volume of distribution, respectively. A better mechanistic understanding of important drug distribution processes will result in improvements in all modeling approaches.

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

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Rowland M, Tozer T. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications. Fourth ed. 2011.

  2. Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis: Concepts and Applications. Fourth ed. 2010.

  3. 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(1):46–58.

    CAS  PubMed  Google Scholar 

  4. Rodgers T, Leahy D, Rowland M. Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases. J Pharm Sci. 2005;94(6):1259–76. https://doi.org/10.1002/jps.20322.

    Article  CAS  PubMed  Google Scholar 

  5. Rodgers T, Rowland M. Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci. 2006;95(6):1238–57. https://doi.org/10.1002/jps.20502.

    Article  CAS  PubMed  Google Scholar 

  6. Hardman JG, Limbird LE. Goodman and Gilman's the pharmacological basis of therapeutics 10th edition. New York: McGraw-Hill; 2001.

    Google Scholar 

  7. Peters SA. Physiologically-based pharmacokinetic modeling and simulations. Hoboken, NJ: Wiley; 2012.

    Book  Google Scholar 

  8. Cole S, Bagal S, El-Kattan A, Fenner K, Hay T, Kempshall S, et al. Full efficacy with no CNS side-effects: unachievable panacea or reality? DMPK considerations in design of drugs with limited brain penetration. Xenobiotica. 2012;42(1):11–27. https://doi.org/10.3109/00498254.2011.617847.

    Article  CAS  PubMed  Google Scholar 

  9. 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. https://doi.org/10.1002/bdd.1823.

    Article  CAS  PubMed  Google Scholar 

  10. Boxenbaum H. Interspecies scaling, allometry, physiological time, and the ground plan of pharmacokinetics. J Pharmacokinet Biopharm. 1982;10(2):201–27. https://doi.org/10.1007/BF01062336.

    Article  CAS  PubMed  Google Scholar 

  11. Freitas AA, Limbu K, Ghafourian T. Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients. J Cheminformatics. 2015;7:17. https://doi.org/10.1186/s13321-015-0054-x.

    Article  CAS  Google Scholar 

  12. Mahmood I. Theoretical versus empirical allometry: facts behind theories and application to pharmacokinetics. J Pharm Sci. 2010;99(7):2927–33. https://doi.org/10.1002/jps.22073.

    Article  CAS  PubMed  Google Scholar 

  13. Colclough N, Ruston L, Wood JM, MacFaul PA. Species differences in drug plasma protein binding. Med Chem Commun. 2014;5:963–7.

    Article  CAS  Google Scholar 

  14. Sugita O, Sawada Y, Sugiyama Y, Hanano M, Iga T. Effect of sulfaphenazole on tolbutamide distribution in rabbits - analysis of interspecies differences in tissue distribution of tolbutamide. J Pharm Sci. 1984;73(5):631–4. https://doi.org/10.1002/jps.2600730513.

    Article  CAS  PubMed  Google Scholar 

  15. Sawada Y, Hanano M, Sugiyama Y, Harashima H, Iga T. Prediction of the volumes of distribution of basic drugs in humans based on data from animals. J Pharmacokinet Biopharm. 1984;12(6):587–96. https://doi.org/10.1007/bf01059554.

    Article  CAS  PubMed  Google Scholar 

  16. Jones R, Jones HM, Rowland M, Gibson CR, Yates JWT, Chien JY, et al. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: comparative assessment of prediction methods of human volume of distribution. J Pharm Sci. 2011;100(10):4074–89. https://doi.org/10.1002/jps.22553.

    Article  CAS  PubMed  Google Scholar 

  17. Gillette JR. Factors affecting drug metabolism. Ann N Y Acad Sci. 1971;179:43–66.

    Article  CAS  PubMed  Google Scholar 

  18. Gibaldi M, McNamara PJ. Apparent volumes of distribution and drug binding to plasma proteins and tissues. Eur J Clin Pharmacol. 1978;13(5):373–80.

    Article  CAS  PubMed  Google Scholar 

  19. Wilkinson GR, Shand DG. Commentary: a physiological approach to hepatic drug clearance. Clin Pharmacol Ther. 1975;18(4):377–90.

    Article  CAS  PubMed  Google Scholar 

  20. Oie S, Tozer TN. Effect of altered plasma-protein binding on apparent volume of distribution. J Pharm Sci. 1979;68(9):1203–5. https://doi.org/10.1002/jps.2600680948.

    Article  CAS  PubMed  Google Scholar 

  21. Lombardo F, Obach RS, Shalaeva MY, Gao F. Prediction of volume of distribution values in humans for neutral and basic drugs using physicochemical measurements and plasma protein binding data. J Med Chem. 2002;45(13):2867–76. https://doi.org/10.1021/jm0200409.

    Article  CAS  PubMed  Google Scholar 

  22. Lombardo F, Obach RS, Shalaeva MY, Gao F. Prediction of human volume of distribution values for neutral and basic drugs. 2. Extended data set and leave-class-out statistics. J Med Chem. 2004;47(5):1242–50. https://doi.org/10.1021/jm030408h.

    Article  CAS  PubMed  Google Scholar 

  23. •• Korzekwa K, Nagar S. Drug Distribution Part 2. Predicting volume of distribution from plasma protein binding and membrane partitioning. Pharm Res. 2017;34(3):544–51. https://doi.org/10.1007/s11095-016-2086-y This article describes a new method for the prediction of the Vss ,which utilizes partitioning into microsomes to represent phospholipid partitioning in a physiological-based Vss equation. This study also looked at other tissue interactions which may be important for describing the distribution of a drug.

    Article  CAS  PubMed  Google Scholar 

  24. Arundel P. A multi-compartmental model generally applicable to physiologically-based pharmacokinetics. IFAC Proceedings Volumes. 1997;30(2):129–33. https://doi.org/10.1016/S1474-6670(17)44557-5.

    Article  Google Scholar 

  25. Jansson R, Bredberg U, Ashton M. Prediction of drug tissue to plasma concentration ratios using a measured volume of distribution in combination with lipophilicity. J Pharm Sci. 2008;97(6):2324–39. https://doi.org/10.1002/jps.21130.

    Article  CAS  PubMed  Google Scholar 

  26. Bjorkman S. Prediction of the volume of distribution of a drug: which tissue-plasma partition coefficients are needed? J Pharm Pharmacol. 2002;54(9):1237–45. https://doi.org/10.1211/002235702320402080.

    Article  CAS  PubMed  Google Scholar 

  27. Poulin P, Theil F-P. A priori prediction of tissue:plasma partition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery. J Pharm Sci. 2000;89(1):16–35. https://doi.org/10.1002/(sici)1520-6017(200001)89:1<16::aid-jps3>3.0.co;2-e.

    Article  CAS  PubMed  Google Scholar 

  28. Poulin P, Schoenlein K, Theil FP. Prediction of adipose tissue: plasma partition coefficients for structurally unrelated drugs. J Pharm Sci. 2001;90(4):436–47. https://doi.org/10.1002/1520-6017(200104)90:4<436::aid-jps1002>3.0.co;2-p.

    Article  CAS  PubMed  Google Scholar 

  29. Poulin P, Krishnan K. A biologically-based algorithm for predicting human tissue-blood partition coefficients of organic chemicals. Hum Exp Toxicol. 1995;14(3):273–80. https://doi.org/10.1177/096032719501400307.

    Article  CAS  PubMed  Google Scholar 

  30. Poulin P, Theil F-P. 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. 2009;98(12):4941–61. https://doi.org/10.1002/jps.21759.

    Article  CAS  PubMed  Google Scholar 

  31. Graham H, Walker M, Jones O, Yates J, Galetin A, Aarons L. Comparison of in-vivo and in-silico methods used for prediction of tissue: plasma partition coefficients in rat. J Pharm Pharmacol. 2012;64(3):383–96. https://doi.org/10.1111/j.2042-7158.2011.01429.x.

    Article  CAS  PubMed  Google Scholar 

  32. Berezhkovskiy LM. Volume of distribution at steady state for a linear pharmacokinetic system with peripheral elimination. J Pharm Sci. 2004;93(6):1628–40. https://doi.org/10.1002/jps.20073.

    Article  CAS  PubMed  Google Scholar 

  33. Berry LM, Roberts J, Be X, Zhao Z, Lin MHJ. Prediction of Vss from in vitro tissue-binding studies. Drug Metab Dispos. 2010;38(1):115–21. https://doi.org/10.1124/dmd.109.029629.

    Article  CAS  PubMed  Google Scholar 

  34. Clausen J, Bickel MH. Prediction of drug distribution in distribution dialysis and in vivo from binding to tissues and blood. J Pharm Sci. 1993;82(4):345–9. https://doi.org/10.1002/jps.2600820402.

    Article  CAS  PubMed  Google Scholar 

  35. Poulin P, Ekins S, Theil F-P. A hybrid approach to advancing quantitative prediction of tissue distribution of basic drugs in human. Toxicol Appl Pharmacol. 2011;250(2):194–212. https://doi.org/10.1016/j.taap.2010.10.014.

    Article  CAS  PubMed  Google Scholar 

  36. Yun YE, Edginton AN. Correlation-based prediction of tissue-to-plasma partition coefficients using readily available input parameters. Xenobiotica. 2013;43(10):839–52. https://doi.org/10.3109/00498254.2013.770182.

    Article  CAS  PubMed  Google Scholar 

  37. Yun YE, Cotton CA, Edginton AN. Development of a decision tree to classify the most accurate tissue-specific tissue to plasma partition coefficient algorithm for a given compound. J Pharmacokinet Pharmacodyn. 2014;41(1):1–14. https://doi.org/10.1007/s10928-013-9342-0.

    Article  CAS  PubMed  Google Scholar 

  38. Schmitt W. General approach for the calculation of tissue to plasma partition coefficients. Toxicol In Vitro. 2008;22(2):457–67. https://doi.org/10.1016/j.tiv.2007.09.010.

    Article  CAS  PubMed  Google Scholar 

  39. Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. J Pharm Sci. 2002;91(1):129–56. https://doi.org/10.1002/jps.10005.

    Article  CAS  PubMed  Google Scholar 

  40. •• Korzekwa K, Nagar S. On the nature of physiologically-based pharmacokinetic models –a priori or a posteriori? Mechanistic or empirical? Pharm Res. 2017;34(3):529–34. https://doi.org/10.1007/s11095-016-2089-8 This article provides a commentary on the current assumptions and methods used in physiologically-based pharmacokinetic models.

    Article  CAS  PubMed  Google Scholar 

  41. Hinderling PH. Red blood cells: a neglected compartment in pharmacokinetics and pharmacodynamics. Pharmacol Rev. 1997;49(3):279–95.

    CAS  PubMed  Google Scholar 

  42. Ye M, Nagar S, Korzekwa K. A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding. Biopharm Drug Dispos. 2016;37(3):123–41. https://doi.org/10.1002/bdd.1996.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Ghafourian T, Barzegar-Jalali M, Hakimiha N, Cronin MTD. Quantitative structure-pharmacokinetic relationship modelling: apparent volume of distribution. J Pharm Pharmacol. 2004;56(3):339–50. https://doi.org/10.1211/0022357022890.

    Article  CAS  PubMed  Google Scholar 

  44. Lombardo F, Obach RS, DiCapua FM, Bakken GA, Lu J, Potter DM, et al. 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):2262–7. https://doi.org/10.1021/jm050200r.

    Article  CAS  PubMed  Google Scholar 

  45. Zhivkova Z, Doytchinova I. Prediction of steady-state volume of distribution of acidic drugs by quantitative structure-pharmacokinetics relationships. J Pharm Sci. 2012;101(3):1253–66. https://doi.org/10.1002/jps.22819.

    Article  CAS  PubMed  Google Scholar 

  46. Korzekwa KR, Nagar S, Tucker J, Weiskircher EA, Bhoopathy S, Hidalgo IJ. Models to predict unbound intracellular drug concentrations in the presence of transporters. Drug Metab Dispos. 2012;40(5):865–76. https://doi.org/10.1124/dmd.111.044289.

    Article  CAS  PubMed  Google Scholar 

  47. • Kovacsics D, Patik I, Özvegy-Laczka C. The role of organic anion transporting polypeptides in drug absorption, distribution, excretion and drug-drug interactions. Expert Opin Drug Metab Toxicol. 2017;13(4):409–24. https://doi.org/10.1080/17425255.2017.1253679 This article is a current review discussing the OATP family of transporters and the importance of OATPs in the absorption and distribution of drugs, as well as their role in drug-drug interactions.

    Article  CAS  PubMed  Google Scholar 

  48. Maeda K. Organic anion transporting polypeptide (OATP)1B1 and OATP1B3 as important regulators of the pharmacokinetics of substrate drugs. Biol Pharm Bull. 2015;38(2):155–68. https://doi.org/10.1248/bpb.b14-00767.

    Article  CAS  PubMed  Google Scholar 

  49. • Kulkarni P, Korzekwa K, Nagar S. Intracellular unbound atorvastatin concentrations in the presence of metabolism and transport. J Pharmacol Exp Ther. 2016;359(1):26–36. https://doi.org/10.1124/jpet.116.235689 This article used a 5-compartmental model for the prediction of intracellular concentrations of atorvastation, to understand the influence of transporters on the intracellular concentration.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. •• Di L, Breen C, Chambers R, Eckley ST, Fricke R, Ghosh A, et al. Industry perspective on contemporary protein-binding methodologies: considerations for regulatory drug-drug interaction and related guidelines on highly bound drugs. J Pharm Sci. 2017;106(12):3442–52. https://doi.org/10.1016/j.xphs.2017.09.005 This article offers an industry perspective on the current methods used to determine the plasma protein binding of a drug, as well as factors which should be considered in current methodology.

    Article  CAS  PubMed  Google Scholar 

  51. Kochansky CJ, McMasters DR, Lu P, Koeplinger KA, Kerr HH, Shou M, et al. Impact of pH on plasma protein binding in equilibrium dialysis. Mol Pharm. 2008;5(3):438–48. https://doi.org/10.1021/mp800004s.

    Article  CAS  PubMed  Google Scholar 

  52. •• Chan R, De Bruyn T, Wright M, Broccatelli F. Comparing mechanistic and preclinical predictions of volume of distribution on a large set of drugs. Pharm Res. 2018;35(4):11. https://doi.org/10.1007/s11095-018-2360-2 This article compared the use of composition-based tissue: plasma partition coefficient prediction models, as well as preclinical extrapolation for the prediction of the Vss for a set of 152 drugs.

    Article  CAS  Google Scholar 

  53. Zou P, Zheng N, Yang YS, Yu LX, Sun DX. Prediction of volume of distribution at steady state in humans: comparison of different approaches. Expert Opin Drug Metab Toxicol. 2012;8(7):855–72. https://doi.org/10.1517/17425255.2012.682569.

    Article  CAS  PubMed  Google Scholar 

  54. Sui XF, Sun J, Li HY, Wang YJ, Liu JF, Liu XH, et al. Prediction of volume of distribution values in human using immobilized artificial membrane partitioning coefficients, the fraction of compound ionized and plasma protein binding data. Eur J Med Chem. 2009;44(11):4455–60. https://doi.org/10.1016/j.ejmech.2009.06.004.

    Article  CAS  PubMed  Google Scholar 

  55. De Buck SS, Sinha VK, Fenu LA, Gilissen RA, Mackie CE, Nijsen MJ. The prediction of drug metabolism, tissue distribution, and bioavailability of 50 structurally diverse compounds in rat using mechanism-based absorption, distribution, and metabolism prediction tools. Drug Metab Dispos. 2007;35(4):649–59. https://doi.org/10.1124/dmd.106.014027.

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

The authors acknowledge funding from the National Institutes of Health grants (R01GM104178 and R01GM114369).

Author information

Author notes

  1. Kimberly Holt and Swati Nagar contributed equally to this work.

Authors and Affiliations

  1. Department of Pharmaceutical Sciences, Temple University School of Pharmacy, 3307 N. Broad Street, Philadelphia, PA, 19140, USA

    Kimberly Holt, Swati Nagar & Ken Korzekwa

Authors
  1. Kimberly Holt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Swati Nagar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ken Korzekwa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ken Korzekwa.

Ethics declarations

Conflict of Interest

The authors have no conflicts of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

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

This article is part of the Topical Collection on Molecular Drug Disposition

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Holt, K., Nagar, S. & Korzekwa, K. Methods to Predict Volume of Distribution. Curr Pharmacol Rep 5, 391–399 (2019). https://doi.org/10.1007/s40495-019-00186-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40495-019-00186-5

Keywords

Search

Navigation