Kosuke Doki1,2, Adam S Darwich1, Brahim Achour1, Aleksi Tornio3, Janne T Backman3, Amin Rostami-Hodjegan1,4. 1. Centre for Applied Pharmacokinetic Research, Division of Pharmacy & Optometry, University of Manchester, Manchester, UK. 2. Department of Pharmaceutical Sciences, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan. 3. Department of Clinical Pharmacology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland. 4. Simcyp Limited (A Certara Company), Sheffield, UK.
Abstract
AIMS: Statistically significant positive correlations are reported for the abundance of hepatic drug-metabolizing enzymes. We investigate, as an example, the impact of CYP3A4-CYP2C8 intercorrelation on the predicted interindividual variabilities of clearance and drug-drug interactions (DDIs) for repaglinide using physiologically based pharmacokinetic (PBPK) modelling. METHODS: PBPK modelling and simulation were employed using Simcyp Simulator (v15.1). Virtual populations were generated assuming intercorrelations between hepatic CYP3A4-CYP2C8 abundances derived from observed values in 24 human livers. A repaglinide PBPK model was used to predict PK parameters in the presence and absence of gemfibrozil in virtual populations, and the results were compared with a clinical DDI study. RESULTS: Coefficient of variation (CV) of oral clearance was 52.5% in the absence of intercorrelation between CYP3A4-CYP2C8 abundances, which increased to 54.2% when incorporating intercorrelation. In contrast, CV for predicted DDI (as measured by AUC ratio before and after inhibition) was reduced from 46.0% in the absence of intercorrelation between enzymes to 43.8% when incorporating intercorrelation: these CVs were associated with 5th/95th percentiles (2.48-11.29 vs. 2.49-9.69). The range of predicted DDI was larger in the absence of intercorrelation (1.55-77.06) than when incorporating intercorrelation (1.79-25.15), which was closer to clinical observations (2.6-12). CONCLUSIONS: The present study demonstrates via a systematic investigation that population-based PBPK modelling incorporating intercorrelation led to more consistent estimation of extreme values than those observed in interindividual variabilities of clearance and DDI. As the intercorrelations more realistically reflect enzyme abundances, virtual population studies involving PBPK and DDI should avoid using Monte Carlo assignment of enzyme abundance.
AIMS: Statistically significant positive correlations are reported for the abundance of hepatic drug-metabolizing enzymes. We investigate, as an example, the impact of CYP3A4-CYP2C8 intercorrelation on the predicted interindividual variabilities of clearance and drug-drug interactions (DDIs) for repaglinide using physiologically based pharmacokinetic (PBPK) modelling. METHODS: PBPK modelling and simulation were employed using Simcyp Simulator (v15.1). Virtual populations were generated assuming intercorrelations between hepatic CYP3A4-CYP2C8 abundances derived from observed values in 24 human livers. A repaglinide PBPK model was used to predict PK parameters in the presence and absence of gemfibrozil in virtual populations, and the results were compared with a clinical DDI study. RESULTS: Coefficient of variation (CV) of oral clearance was 52.5% in the absence of intercorrelation between CYP3A4-CYP2C8 abundances, which increased to 54.2% when incorporating intercorrelation. In contrast, CV for predicted DDI (as measured by AUC ratio before and after inhibition) was reduced from 46.0% in the absence of intercorrelation between enzymes to 43.8% when incorporating intercorrelation: these CVs were associated with 5th/95th percentiles (2.48-11.29 vs. 2.49-9.69). The range of predicted DDI was larger in the absence of intercorrelation (1.55-77.06) than when incorporating intercorrelation (1.79-25.15), which was closer to clinical observations (2.6-12). CONCLUSIONS: The present study demonstrates via a systematic investigation that population-based PBPK modelling incorporating intercorrelation led to more consistent estimation of extreme values than those observed in interindividual variabilities of clearance and DDI. As the intercorrelations more realistically reflect enzyme abundances, virtual population studies involving PBPK and DDI should avoid using Monte Carlo assignment of enzyme abundance.
Authors: Mikko Niemi; Janne T Backman; Lauri I Kajosaari; Julian B Leathart; Mikko Neuvonen; Ann K Daly; Michel Eichelbaum; Kari T Kivistö; Pertti J Neuvonen Journal: Clin Pharmacol Ther Date: 2005-06 Impact factor: 6.875
Authors: Janne T Backman; Johanna Honkalammi; Mikko Neuvonen; Kaisa J Kurkinen; Aleksi Tornio; Mikko Niemi; Pertti J Neuvonen Journal: Drug Metab Dispos Date: 2009-09-22 Impact factor: 3.922
Authors: Brian W Ogilvie; Donglu Zhang; Wenying Li; A David Rodrigues; Amy E Gipson; Jeff Holsapple; Paul Toren; Andrew Parkinson Journal: Drug Metab Dispos Date: 2005-11-18 Impact factor: 3.922
Authors: Mikko Niemi; Julian B Leathart; Mikko Neuvonen; Janne T Backman; Ann K Daly; Pertti J Neuvonen Journal: Clin Pharmacol Ther Date: 2003-10 Impact factor: 6.875
Authors: Manthena V S Varma; Yurong Lai; Emi Kimoto; Theunis C Goosen; Ayman F El-Kattan; Vikas Kumar Journal: Pharm Res Date: 2013-01-10 Impact factor: 4.200
Authors: Bhagwat Prasad; Brahim Achour; Per Artursson; Cornelis E C A Hop; Yurong Lai; Philip C Smith; Jill Barber; Jacek R Wisniewski; Daniel Spellman; Yasuo Uchida; Michael A Zientek; Jashvant D Unadkat; Amin Rostami-Hodjegan Journal: Clin Pharmacol Ther Date: 2019-07-26 Impact factor: 6.875
Authors: Estelle Yau; Andrés Olivares-Morales; Michael Gertz; Neil Parrott; Adam S Darwich; Leon Aarons; Kayode Ogungbenro Journal: AAPS J Date: 2020-02-03 Impact factor: 4.009