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The Yin–Yang of CYP3A4: a Bayesian meta-analysis to quantify inhibition and induction of CYP3A4 metabolism in humans and refine uncertainty factors for mixture risk assessment

  • Nadia Quignot
  • Witold Wiecek
  • Billy Amzal
  • Jean-Lou Dorne
Toxicogenomics
  • 110 Downloads

Abstract

Quantifying differences in pharmacokinetics (PK) and toxicokinetics (TK) provides a science-based approach to refine uncertainty factors (UFs) for chemical risk assessment. Cytochrome P450 (CYP) 3A4—the major hepatic and intestinal human CYP—and the P-glycoprotein (Pgp) transporter share a vast range of common substrates for which PK may be modulated through inhibition or induction in the presence of grapefruit juice (GFJ) or St. John’s wort (SJW), respectively. Here, an extensive literature search was performed on PK interactions for CYP3A4 and Pgp substrates after oral co-exposure to GFJ and SJW. Relevant data from 109 publications, extracted for both markers of acute (Cmax) and chronic [clearance and area under the plasma concentration–time curve (AUC)] exposure, were computed into a Bayesian hierarchical meta-analysis model. Bioavailability (F) and substrate fraction metabolised by CYP3A4 (Fm) were identified as the variables exhibiting the highest impact on the magnitude of interaction. The Bayesian meta-regression model developed provided good predictions for magnitudes of inhibition (maximum 5.3-fold with GFJ) and induction (maximum 2.3-fold with SJW). Integration of CYP3A4 variability, F, Fm and magnitude of interaction provided the basis to derive a range of CYP3A4 and Pgp-related UFs. Such CYP3A4 and Pgp-related UFs can be derived in the absence of human data using in vitro TK evidence for CYP3A4/Pgp inhibition or induction as conservative in silico options. The future development of quantitative in vitro–in vivo extrapolation models for mixture risk assessment is discussed with particular attention to integrating human in vitro and in vivo P/TK data on interactions with pathway-related variability.

Keywords

CYP3A4 Interindividual variability Kinetic interactions Mixtures Risk assessment Uncertainty factors 

Notes

Acknowledgements

This work has been financed by the European Food Safety Authority (EFSA) under contract CFT/EFSA/EMRISK/2012/01 and Analytica LASER. The authors would like to thank Katarzyna Miernik, Iwona Kuter, Mateusz Nikodem, Agnieszka Zyla, Camille Béchaux, Sonia Halhol, Laure Perreau and Céline Dubuquoy from Analytica LASER for data collection and analysis.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

204_2018_2325_MOESM1_ESM.pdf (87 kb)
S1: Compounds characteristics (PDF 86 KB)
204_2018_2325_MOESM2_ESM.pdf (493 kb)
S2: Pharmacokinetic interaction data following co-exposure with grapefruit juice or St John’s Wort (PDF 492 KB)
204_2018_2325_MOESM3_ESM.pdf (69 kb)
S3: Summary statistics of the parameter prior and posterior distributions after Bayesian calibration of the meta-regression model (PDF 69 KB)
204_2018_2325_MOESM4_ESM.pdf (79 kb)
S4: Prediction of magnitudes of interaction for CYP3A4-Pgp substrates after grapefruit juice (inhibitor) and St John’s wort (inducer) exposure according to substrate characteristics (PDF 78 KB)
204_2018_2325_MOESM5_ESM.pdf (49 kb)
S5: Derivation of CYP3A4-related uncertainty factors for inhibition and induction (PDF 48 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Analytica LASERParisFrance
  2. 2.Analytica LASERLondonUK
  3. 3.European Food Safety AuthorityParmaItaly

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