Predicting human pharmacokinetics from preclinical data: volume of distribution

This tutorial introduces background and methods to predict the human volume of distribution (V ) of drugs using and animal pharmacokinetic (PK) parameters. The physiologically based PK (PBPK) method is based on the familiar equation: = + ∑ ( × ). In this equation, V (plasma volume) and V (tissue vol...

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Published inTranslational and clinical pharmacology Vol. 28; no. 4; pp. 169 - 174
Main Authors Yim, Dong-Seok, Choi, Suein
Format Journal Article
LanguageEnglish
Published Korea (South) Korean Society for Clinical Pharmacology and Therapeutics 01.12.2020
대한임상약리학회
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Summary:This tutorial introduces background and methods to predict the human volume of distribution (V ) of drugs using and animal pharmacokinetic (PK) parameters. The physiologically based PK (PBPK) method is based on the familiar equation: = + ∑ ( × ). In this equation, V (plasma volume) and V (tissue volume) are known physiological values, and k (tissue plasma partition coefficient) is experimentally measured. Here, the k may be predicted by PBPK models because it is known to be correlated with the physicochemical property of drugs and tissue composition (fraction of lipid and water). Thus, PBPK models' evolution to predict human V has been the efforts to find a better function giving a more accurate k . When animal PK parameters estimated using i.v. PK data in ≥ 3 species are available, allometric methods can also be used to predict human V . Unlike the PBPK method, many different models may be compared to find the best-fitting one in the allometry, a kind of empirical approach. Also, compartmental V parameters (e.g., V , V , and Q) can be predicted in the allometry. Although PBPK and allometric methods have long been used to predict V , there is no consensus on method choice. When the discrepancy between PBPK-predicted V and allometry-predicted V is huge, physiological plausibility of all input and output data (e.g., r -value of the allometric curve) may be reviewed for careful decision making.
ISSN:2289-0882
2383-5427
DOI:10.12793/tcp.2020.28.e19