Literature DB >> 33404848

Application of Model Informed Precision Dosing to Address the Impact of Pregnancy Stage and CYP2D6 Phenotype on Foetal Morphine Exposure.

Sarah Badaoui1, Ashley M Hopkins1, A David Rodrigues2, John O Miners1, Michael J Sorich1, Andrew Rowland3.   

Abstract

Guidance regarding the effect of codeine and its metabolites on foetal development is limited by small studies and inconsistent findings. The primary objective was to use physiologically based pharmacokinetic modelling to investigate the impact of gestational stage and maternal CYP2D6 phenotype on foetal morphine exposure following codeine administration. Full body physiologically based pharmacokinetic models were developed and verified for codeine and morphine using Simcyp (version 19.1). The impact of gestational age and maternal CYP2D6 phenotype on foetal and maternal morphine and codeine exposure following oral codeine administration was modelled in a cohort of 250 pregnant females and foetuses at gestational weeks 0 (mothers only), 6, 12, 24 and 36. Consistent with the known effect on codeine metabolism, a clinically meaningful (> 1.65-fold) increase in foetal morphine AUC was observed in the CYP2D6 UM phenotype cohort compared to the CYP2D6 EM and PM phenotype cohorts. The mean (95% CI) foetal morphine AUC in the CYP2D6 UM cohort of 0.988 (0.902 to 1.073) ng/mL.h was 1.8-fold higher than the CYP2D6 EM cohort of 0.546 (0.492 to 0.600) ng/mL.h (p < 0.001). Despite a 2.8-fold increase in maternal CYP2D6 protein abundance between gestational weeks 6 and 36, the mean foetal morphine AUC in the CYP2D6 EM and UM phenotype cohorts reduced by 1.55- and 1.75-fold, respectively, over this period. Maternal CYP2D6 phenotype is a significant determinant of foetal morphine AUC. Simulations suggest that the greatest risk with respect to foetal morphine exposure is during the first trimester of pregnancy, particularly in CYP2D6 UM phenotype mothers.

Entities:  

Keywords:  codeine; model informed precision dosing; morphine; physiologically based pharmacokinetics

Year:  2021        PMID: 33404848     DOI: 10.1208/s12248-020-00541-1

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  44 in total

1.  Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review.

Authors:  P Zhao; L Zhang; J A Grillo; Q Liu; J M Bullock; Y J Moon; P Song; S S Brar; R Madabushi; T C Wu; B P Booth; N A Rahman; K S Reynolds; E Gil Berglund; L J Lesko; S-M Huang
Journal:  Clin Pharmacol Ther       Date:  2010-12-29       Impact factor: 6.875

2.  Application of physiologically based pharmacokinetic modeling to predict drug disposition in pregnant populations.

Authors:  Vamshi Krishna Jogiraju; Suvarchala Avvari; Rakesh Gollen; David R Taft
Journal:  Biopharm Drug Dispos       Date:  2017-07-13       Impact factor: 1.627

3.  In vitro-in vivo extrapolation predicts drug-drug interactions arising from inhibition of codeine glucuronidation by dextropropoxyphene, fluconazole, ketoconazole, and methadone in humans.

Authors:  Pritsana Raungrut; Verawan Uchaipichat; David J Elliot; Benjamas Janchawee; Andrew A Somogyi; John O Miners
Journal:  J Pharmacol Exp Ther       Date:  2010-05-18       Impact factor: 4.030

4.  Physiologically Based Pharmacokinetic Modeling in Pregnancy: A Systematic Review of Published Models.

Authors:  André Dallmann; Marc Pfister; John van den Anker; Thomas Eissing
Journal:  Clin Pharmacol Ther       Date:  2018-05-06       Impact factor: 6.875

5.  Physiologically Based Pharmacokinetic Modelling to Identify Pharmacokinetic Parameters Driving Drug Exposure Changes in the Elderly.

Authors:  Felix Stader; Hannah Kinvig; Melissa A Penny; Manuel Battegay; Marco Siccardi; Catia Marzolini
Journal:  Clin Pharmacokinet       Date:  2020-03       Impact factor: 6.447

6.  Prediction of olanzapine exposure in individual patients using physiologically based pharmacokinetic modelling and simulation.

Authors:  Thomas M Polasek; Geoffrey T Tucker; Michael J Sorich; Michael D Wiese; Titus Mohan; Amin Rostami-Hodjegan; Porntipa Korprasertthaworn; Vidya Perera; Andrew Rowland
Journal:  Br J Clin Pharmacol       Date:  2018-01-11       Impact factor: 4.335

Review 7.  Why has model-informed precision dosing not yet become common clinical reality? lessons from the past and a roadmap for the future.

Authors:  A S Darwich; K Ogungbenro; A A Vinks; J R Powell; J-L Reny; N Marsousi; Y Daali; D Fairman; J Cook; L J Lesko; J S McCune; Caj Knibbe; S N de Wildt; J S Leeder; M Neely; A F Zuppa; P Vicini; L Aarons; T N Johnson; J Boiani; A Rostami-Hodjegan
Journal:  Clin Pharmacol Ther       Date:  2017-04-04       Impact factor: 6.875

8.  The simcyp population based simulator: architecture, implementation, and quality assurance.

Authors:  Masoud Jamei; Steve Marciniak; Duncan Edwards; Kris Wragg; Kairui Feng; Adrian Barnett; Amin Rostami-Hodjegan
Journal:  In Silico Pharmacol       Date:  2013-06-03

9.  Optimized Cocktail Phenotyping Study Protocol Using Physiological Based Pharmacokinetic Modeling and In silico Assessment of Metabolic Drug-Drug Interactions Involving Modafinil.

Authors:  Angela Rowland; Arduino A Mangoni; Ashley Hopkins; Michael J Sorich; Andrew Rowland
Journal:  Front Pharmacol       Date:  2016-12-27       Impact factor: 5.810

10.  Model-Informed Precision Dosing at the Bedside: Scientific Challenges and Opportunities.

Authors:  Ron J Keizer; Rob Ter Heine; Adam Frymoyer; Lawrence J Lesko; Ranvir Mangat; Srijib Goswami
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2018-10-16
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