Advertisement

Current Pharmacology Reports

, Volume 5, Issue 5, pp 391–399 | Cite as

Methods to Predict Volume of Distribution

  • Kimberly Holt
  • Swati Nagar
  • Ken KorzekwaEmail author
Molecular Drug Disposition (B Joshi, Section Editor)
  • 43 Downloads
Part of the following topical collections:
  1. Topical Collection on Molecular Drug Disposition

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.

Keywords

Distribution Volume of distribution Tissue:plasma partition coefficients Membrane partitioning Prediction models 

Notes

Funding Information

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

Compliance with Ethical Standards

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.

References

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

  1. 1.
    Rowland M, Tozer T. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications. Fourth ed. 2011.Google Scholar
  2. 2.
    Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis: Concepts and Applications. Fourth ed. 2010.Google Scholar
  3. 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.PubMedGoogle Scholar
  4. 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.CrossRefPubMedGoogle Scholar
  5. 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.CrossRefPubMedGoogle Scholar
  6. 6.
    Hardman JG, Limbird LE. Goodman and Gilman's the pharmacological basis of therapeutics 10th edition. New York: McGraw-Hill; 2001.Google Scholar
  7. 7.
    Peters SA. Physiologically-based pharmacokinetic modeling and simulations. Hoboken, NJ: Wiley; 2012.CrossRefGoogle Scholar
  8. 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.CrossRefPubMedGoogle Scholar
  9. 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.CrossRefPubMedGoogle Scholar
  10. 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.CrossRefPubMedGoogle Scholar
  11. 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.CrossRefGoogle Scholar
  12. 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.CrossRefPubMedGoogle Scholar
  13. 13.
    Colclough N, Ruston L, Wood JM, MacFaul PA. Species differences in drug plasma protein binding. Med Chem Commun. 2014;5:963–7.CrossRefGoogle Scholar
  14. 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.CrossRefPubMedGoogle Scholar
  15. 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.CrossRefPubMedGoogle Scholar
  16. 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.CrossRefPubMedGoogle Scholar
  17. 17.
    Gillette JR. Factors affecting drug metabolism. Ann N Y Acad Sci. 1971;179:43–66.CrossRefPubMedGoogle Scholar
  18. 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.CrossRefPubMedGoogle Scholar
  19. 19.
    Wilkinson GR, Shand DG. Commentary: a physiological approach to hepatic drug clearance. Clin Pharmacol Ther. 1975;18(4):377–90.CrossRefPubMedGoogle Scholar
  20. 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.CrossRefPubMedGoogle Scholar
  21. 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.CrossRefPubMedGoogle Scholar
  22. 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.CrossRefPubMedGoogle Scholar
  23. 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.CrossRefPubMedGoogle Scholar
  24. 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.CrossRefGoogle Scholar
  25. 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.CrossRefPubMedGoogle Scholar
  26. 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.CrossRefPubMedGoogle Scholar
  27. 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.CrossRefPubMedGoogle Scholar
  28. 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.CrossRefPubMedGoogle Scholar
  29. 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.CrossRefPubMedGoogle Scholar
  30. 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.CrossRefPubMedGoogle Scholar
  31. 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.CrossRefPubMedGoogle Scholar
  32. 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.CrossRefPubMedGoogle Scholar
  33. 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.CrossRefPubMedGoogle Scholar
  34. 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.CrossRefPubMedGoogle Scholar
  35. 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.CrossRefPubMedGoogle Scholar
  36. 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.CrossRefPubMedGoogle Scholar
  37. 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.CrossRefPubMedGoogle Scholar
  38. 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.CrossRefPubMedGoogle Scholar
  39. 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.CrossRefPubMedGoogle Scholar
  40. 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.CrossRefPubMedGoogle Scholar
  41. 41.
    Hinderling PH. Red blood cells: a neglected compartment in pharmacokinetics and pharmacodynamics. Pharmacol Rev. 1997;49(3):279–95.PubMedGoogle Scholar
  42. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 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.CrossRefPubMedGoogle Scholar
  44. 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.CrossRefPubMedGoogle Scholar
  45. 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.CrossRefPubMedGoogle Scholar
  46. 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.CrossRefPubMedGoogle Scholar
  47. 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.CrossRefPubMedGoogle Scholar
  48. 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.CrossRefPubMedGoogle Scholar
  49. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 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.CrossRefPubMedGoogle Scholar
  51. 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.CrossRefPubMedGoogle Scholar
  52. 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.CrossRefGoogle Scholar
  53. 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.CrossRefPubMedGoogle Scholar
  54. 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.CrossRefPubMedGoogle Scholar
  55. 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.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Pharmaceutical SciencesTemple University School of PharmacyPhiladelphiaUSA

Personalised recommendations