Comparing Mechanistic and Preclinical Predictions of Volume of Distribution on a Large Set of Drugs
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Volume of distribution at steady state (Vdss) is a fundamental pharmacokinetic (PK) parameter driven predominantly by passive processes and physicochemical properties of the compound. Human Vdss can be estimated using in silico mechanistic methods or empirically scaled from Vdss values obtained from preclinical species. In this study the accuracy and the complementarity of these two approaches are analyzed leveraging a large data set (over 150 marketed drugs).
For all the drugs analyzed in this study experimental in vitro measurements of LogP, plasma protein binding and pKa are used as input for the mechanistic in silico model to predict human Vdss. The software used for predicting human tissue partition coefficients and Vdss based on the method described by Rodgers and Rowland is made available as supporting information.
This assessment indicates that overall the in silico mechanistic model presented by Rodgers and Rowland is comparably accurate or superior to empirical approaches based on the extrapolation of in vivo data from preclinical species.
These results illustrate the great potential of mechanistic in silico models to accurately predict Vdss in humans. This in silico method does not rely on in vivo data and is, consequently, significantly time and resource sparing. The success of this in silico model further suggests that reasonable predictability of Vdss in preclinical species could be obtained by a similar process.
Key wordsmechanistic in silico model PBPK physicochemical properties preclinical volume of distribution (Vdss)
Absorption, distribution, metabolism and elimination or excretion
Blood-to-plasma concentration ratio
Fraction of compound unbound in plasma
Liter per kilogram
The octanol–water partition coefficient
Poulin and Theil
Physiologically-based pharmacokinetic modeling
The negative base 10 logarithm of the acid dissociation constant
Rodgers and Rowland
Volume of distribution at steady state
Acknowledgments and Disclosures
Rosa Chan was supported by the American Association of Pharmaceutical Scientists Graduate Fellowship, American Foundation for Pharmaceutical Education Pre-Doctoral Fellowship, North American Graduate Fellowship from the American College of Toxicology, and NIGMS grant R25 GM56847. All authors declare no conflict of interest.
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