Phenotyping of Human CYP450 Enzymes by Endobiotics: Current Knowledge and Methodological Approaches.

Details

Serval ID
serval:BIB_94F1E4B8E2A9
Type
Article: article from journal or magazin.
Publication sub-type
Review (review): journal as complete as possible of one specific subject, written based on exhaustive analyses from published work.
Collection
Publications
Institution
Title
Phenotyping of Human CYP450 Enzymes by Endobiotics: Current Knowledge and Methodological Approaches.
Journal
Clinical pharmacokinetics
Author(s)
Magliocco G., Thomas A., Desmeules J., Daali Y.
ISSN
1179-1926 (Electronic)
ISSN-L
0312-5963
Publication state
Published
Issued date
11/2019
Peer-reviewed
Oui
Volume
58
Number
11
Pages
1373-1391
Language
english
Notes
Publication types: Journal Article ; Review
Publication Status: ppublish
Abstract
Drug response is subject to an important within- and between-individual variability owing, mainly, to pharmacokinetic and pharmacodynamic factors. Pharmacokinetics includes metabolism by cytochrome P450 (CYP450), major enzymes of phase I reactions that are responsible for the biotransformation of around 60% of the currently approved drugs. CYP450 activity and/or expression are influenced by multiple intrinsic and extrinsic factors, such as drug-drug interactions or genetic polymorphisms. Present phenotyping strategies with xenobiotics used to assess CYP450 activity could be replaced by less invasive procedures using endogenous CYP450 biomarkers. In this work, we review existing knowledge on endobiotics and their ability to characterise variability of the CYP1A2, CYP2C19, CYP2D6 and CYP3A enzymes in humans. To date, it appears that there is a lack of clinical data for the majority of the endogenous compounds described in the literature or some important limitations to allow their use in clinical practice. Additional studies are needed to fill the gap or to identify new candidates, in particular through the use of metabolomics. The use of multivariate models is also a very promising approach to enhance prediction by combining several endogenous phenotyping metrics and other covariates.
Pubmed
Web of science
Create date
29/05/2019 14:26
Last modification date
05/01/2020 7:18
Usage data