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)
Part of the following topical collections:
  1. Topical Collection on Molecular Drug Disposition


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.


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.


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


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.


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

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

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