Abstract
This study aimed to derive quantitative abundance values for key hepatic transporters suitable for in vitro–in vivo extrapolation within a physiologically based pharmacokinetic modeling framework. A meta-analysis was performed whereby data on abundance measurements, sample preparation methods, and donor demography were collated from the literature. To define values for a healthy Caucasian population, a subdatabase was created whereby exclusion criteria were applied to remove samples from non-Caucasian individuals, those with underlying disease, or those with subcellular fractions other than crude membrane. Where a clinically relevant active genotype was known, only samples from individuals with an extensive transporter phenotype were included. Authors were contacted directly when additional information was required. After removing duplicated samples, the weighted mean, geometric mean, standard deviation, coefficient of variation, and between-study homogeneity of transporter abundances were determined. From the complete database containing 24 transporters, suitable abundance data were available for 11 hepatic transporters from nine studies after exclusion criteria were applied. Organic anion transporting polypeptides OATP1B1 and OATP1B3 showed the highest population abundance in healthy adult Caucasians. For several transporters, the variability in abundance was reduced significantly once the exclusion criteria were applied. The highest variability was observed for OATP1B3 > OATP1B1 > multidrug resistance protein 2 > multidrug resistance gene 1. No relationship was found between transporter expression and donor age. To our knowledge, this study provides the first in-depth analysis of current quantitative abundance data for a wide range of hepatic transporters, with the aim of using these data for in vitro–in vivo extrapolation, and highlights the significance of investigating the background of tissue(s) used in quantitative transporter proteomic studies. Similar studies are now warranted for other ethnicities.
Highlights
Based pharmacokinetic (PBPK) models are able to use in vitro data from recombinant expression systems to predict drug disposition via in vitro–in vivo extrapolation (IVIVE)
An increased awareness of the role of transporters in the uptake and efflux of clinically relevant compounds has led to a growing interest in the development of Physiologically based pharmacokinetic (PBPK) models to investigate their influence on pharmacokinetics (Li et al, 2014; Rose et al, 2014; Posada et al, 2015; Snoeys et al, 2016)
The lack of quantitative expression data for transporters in in vitro systems and human tissue has been a key limitation, meaning such PBPK models cannot be developed entirely from the “bottom up” and are typically built using both in vitro and clinical data in a “middle-out” approach. If an approach such as that described by eq 1 is used to scale in vitro intrinsic clearance (CLint),j values to CLint,liver, the difficulty in obtaining a measured value of relative expression factor (REF)/relative activity factor (RAF) means that often this value can only be estimated by fitting clinical pharmacokinetic data
Summary
Based pharmacokinetic (PBPK) models are able to use in vitro data from recombinant expression systems to predict drug disposition via in vitro–in vivo extrapolation (IVIVE). The availability of recombinant P450 standards has facilitated the quantification of a wide range of P450 isoenzyme abundances in human liver samples, typically by immunoblotting techniques (Shimada et al, 1994; Rowland-Yeo et al, 2004). Until recently, such data have been lacking for non–P450metabolizing enzymes, including UDP glucuronosyltransferases, esterases, and flavin-containing monooxygenases, as well as drug transporters. The scalars currently used for transporters have been based
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