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Prediction of Volume of Distribution Values in Humans for Neutral and Basic Drugs Using Physicochemical Measurements and Plasma Protein Binding Data

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Molecular Properties Group, Pharmacokinetics, Dynamics, and Metabolism, and Nonclinical Statistics Group, Pfizer Global Research and Development, Groton Laboratories, Groton, Connecticut 06340
Cite this: J. Med. Chem. 2002, 45, 13, 2867–2876
Publication Date (Web):May 21, 2002
https://doi.org/10.1021/jm0200409
Copyright © 2002 American Chemical Society

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    Abstract

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    We present a method for the prediction of volume of distribution in humans, for neutral and basic compounds. It is based on two experimentally determined physicochemical parameters, ElogD(7.4) and fi(7.4), the latter being the fraction of compound ionized at pH 7.4 and on the fraction of free drug in plasma (fu). The fraction unbound in tissues (fut), determined via a regression analysis from 64 compounds using the parameters described, is then used to predict VDss via the Oie−Tozer equation. Accuracy of this method was determined using a test set of 14 compounds, and it was demonstrated that human VDss values could be predicted, on average, within or very close to 2-fold of the actual value. The present method is as accurate as reported methods based on animal pharmacokinetic data, using a similar set of compounds, and ranges between 1.62 and 2.20 as mean-fold error. This method has the advantage of being amenable to automation, and therefore fast throughput, it is compound and resources sparing, and it offers a rationale for the reduction of the use of animals in pharmacokinetic studies. A discussion of the potential errors that may be encountered, including errors in the determination of fu, is offered, and the caveats about the use of computed vs experimentally determined logD and pKa values are addressed.

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    *

     To whom correspondence should be addressed. Tel.:  (860)441-6982. Fax:  (860)715-3345. E-mail:  [email protected].

     Molecular Properties Group.

     Pharmacokinetics, Dynamics, and Metabolism.

    §

     Nonclinical Statistics Group.

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