Literature DB >> 28254950

Assessing the Risk of Drug-Induced Cholestasis Using Unbound Intrahepatic Concentrations.

Julia Riede1, Birk Poller1, Jörg Huwyler1, Gian Camenisch2.   

Abstract

Inhibition of the bile salt export pump (BSEP) has been recognized as a key factor in the development of drug-induced cholestasis (DIC). The risk of DIC in humans has been previously assessed using in vitro BSEP inhibition data (IC50) and unbound systemic drug exposure under assumption of the "free drug hypothesis." This concept, however, is unlikely valid, as unbound intrahepatic drug concentrations are affected by active transport and metabolism. To investigate this hypothesis, we experimentally determined the in vitro liver-to-blood partition coefficients (Kpuu) for 18 drug compounds using the hepatic extended clearance model (ECM). In vitro-in vivo translatability of Kpuu values was verified for a subset of compounds in rat. Consequently, unbound intrahepatic concentrations were calculated from clinical exposure (systemic and hepatic inlet) and measured Kpuu data. Using these values, corresponding safety margins against BSEP IC50 values were determined and compared with the clinical incidence of DIC. Depending on the ECM class of a drug, in vitro Kpuu values deviated up to 14-fold from unity, and unbound intrahepatic concentrations were affected accordingly. The use of in vitro Kpuu-based safety margins allowed separation of clinical cholestasis frequency into three classes (no cholestasis, cholestasis in ≤2%, and cholestasis in >2% of subjects) for 17 out of 18 compounds. This assessment was significantly superior compared with using unbound extracellular concentrations as a surrogate for intrahepatic concentrations. Furthermore, the assessment of Kpuu according to ECM provides useful guidance for the quantitative evaluation of genetic and physiologic risk factors for the development of cholestasis.
Copyright © 2017 by The American Society for Pharmacology and Experimental Therapeutics.

Entities:  

Mesh:

Substances:

Year:  2017        PMID: 28254950     DOI: 10.1124/dmd.116.074179

Source DB:  PubMed          Journal:  Drug Metab Dispos        ISSN: 0090-9556            Impact factor:   3.922


  5 in total

1.  When Does the Rate-Determining Step in the Hepatic Clearance of a Drug Switch from Sinusoidal Uptake to All Hepatobiliary Clearances? Implications for Predicting Drug-Drug Interactions.

Authors:  Gabriela I Patilea-Vrana; Jashvant D Unadkat
Journal:  Drug Metab Dispos       Date:  2018-08-16       Impact factor: 3.922

2.  Advancing Predictions of Tissue and Intracellular Drug Concentrations Using In Vitro, Imaging and Physiologically Based Pharmacokinetic Modeling Approaches.

Authors:  Yingying Guo; Xiaoyan Chu; Neil J Parrott; Kim L R Brouwer; Vicky Hsu; Swati Nagar; Pär Matsson; Pradeep Sharma; Jan Snoeys; Yuichi Sugiyama; Daniel Tatosian; Jashvant D Unadkat; Shiew-Mei Huang; Aleksandra Galetin
Journal:  Clin Pharmacol Ther       Date:  2018-09-12       Impact factor: 6.875

3.  Challenging the Relevance of Unbound Tissue-to-Blood Partition Coefficient (Kpuu) on Prediction of Drug-Drug Interactions.

Authors:  Jasleen K Sodhi; Shuaibing Liu; Leslie Z Benet
Journal:  Pharm Res       Date:  2020-03-25       Impact factor: 4.200

4.  Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology.

Authors:  Robert Ietswaart; Seda Arat; Amanda X Chen; Saman Farahmand; Bumjun Kim; William DuMouchel; Duncan Armstrong; Alexander Fekete; Jeffrey J Sutherland; Laszlo Urban
Journal:  EBioMedicine       Date:  2020-06-18       Impact factor: 8.143

5.  Robustness testing and optimization of an adverse outcome pathway on cholestatic liver injury.

Authors:  Lindsey Devisscher; Mathieu Vinken; Eva Gijbels; Vânia Vilas-Boas; Pieter Annaert; Tamara Vanhaecke
Journal:  Arch Toxicol       Date:  2020-03-10       Impact factor: 5.153

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.