Literature DB >> 32283068

The Novel In Vitro Method to Calculate Tissue-to-Plasma Partition Coefficient in Humans for Predicting Pharmacokinetic Profiles by Physiologically-Based Pharmacokinetic Model With High Predictability.

Kei Mayumi1, Miho Tachibana2, Mei Yoshida3, Shuichi Ohnishi4, Takushi Kanazu4, Hiroshi Hasegawa4.   

Abstract

Proper prediction of human pharmacokinetic (PK) profiles can accelerate the compound selection in drug discovery. Recently, we reported a robust bottom-up physiologically-based pharmacokinetic (PBPK) approach (J Pharm Sci. 2019 Aug; 108(8):2718-2727), which uses the in vivo rat distribution volume at the steady state (Vss) to determine human tissue-to-plasma partition coefficients (Kptissue). Here, we report on a bottom-up PBPK approach that can simulate the PK profile with both high-throughput and high-predictive accuracy only using in vitro data. In this study, as an alternative parameter of in vivo rat Vss which was used for the correction of human Kptissue, Vss, in vitro was obtained from protein binding data in rats, and the values of Vss, in vitro for 31 reference compounds showed good correlation with the observed rat Vss (R2 = 0.859). Next, rat and human PK profiles of reference compounds were predicted by the bottom-up PBPK approach using Kptissue corrected by rat Vss, in vitro. As a result, the absolute average fold errors for pharmacokinetic parameters were almost less than 2, showing that these PK profiles could be accurately predicted using in vitro data. This method enables the screening of promising compounds with good PK profiles in humans at an early stage of drug discovery.
Copyright © 2020 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Absorption, distribution, metabolism, and excretion (ADME); Pharmacokinetics; Physiologically based pharmacokinetic modeling; Protein binding; Tissue distribution

Mesh:

Year:  2020        PMID: 32283068     DOI: 10.1016/j.xphs.2020.04.002

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  1 in total

1.  Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds.

Authors:  Neha Murad; Kishore K Pasikanti; Benjamin D Madej; Amanda Minnich; Juliet M McComas; Sabrinia Crouch; Joseph W Polli; Andrew D Weber
Journal:  Drug Metab Dispos       Date:  2020-11-25       Impact factor: 3.922

  1 in total

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