PMID- 33460193
OWN - NLM
STAT- MEDLINE
DCOM- 20211220
LR  - 20211220
IS  - 1552-4604 (Electronic)
IS  - 0091-2700 (Linking)
VI  - 61
IP  - 6
DP  - 2021 Jun
TI  - Usage of In Vitro Metabolism Data for Drug-Drug Interaction in Physiologically 
      Based Pharmacokinetic Analysis Submissions to the US Food and Drug 
      Administration.
PG  - 782-788
LID - 10.1002/jcph.1819 [doi]
AB  - The key parameters necessary to predict drug-drug interactions (DDIs) are 
      intrinsic clearance (CL(int) ) and fractional contribution of the metabolizing 
      enzyme toward total metabolism (f(m) ). Herein, we summarize the accumulated 
      knowledge from 53 approved new drug applications submitted to the Office of 
      Clinical Pharmacology, US Food and Drug Administration, from 2016 to 2018 that 
      contained physiologically based pharmacokinetic (PBPK) models to understand how 
      in vitro data are used in PBPK models to assess drug metabolism and predict DDIs. 
      For evaluation of CL(int) and f(m) , 29 and 20 new drug applications were 
      included for evaluation, respectively. For CL(int) , 86.2% of the PBPK models 
      used modified values based on in vivo data with modifications ranging from -82.5% 
      to 2752.5%. For f(m) , 45.0% of the models used modified values with 
      modifications ranging from -28% to 178.6%. When values for CL(int) were used from 
      in vitro testing without modification, the model resulted in up to a 14.3-fold 
      overprediction of the area under the concentration-time curve of the substrate. 
      When values for f(m) from in vitro testing were used directly, the model resulted 
      in up to a 2.9-fold underprediction of its DDI magnitude with an inducer, and up 
      to a 1.7-fold overprediction of its DDI magnitude with an inhibitor. Our analyses 
      suggested that the in vitro system usually provides a reasonable estimation of 
      f(m) when the drug metabolism by a given CYP pathway is more than 70% of the 
      total clearance. In vitro experiments provide important information about basic 
      PK properties of new drugs and can serve as a starting point for building a PBPK 
      model. However, key PBPK parameters such as CL(int) and f(m) still need to be 
      optimized based on in vivo data.
CI  - Published 2021. This article is a U.S. Government work and is in the public 
      domain in the USA.
FAU - Lee, Jieon
AU  - Lee J
AD  - Office of Clinical Pharmacology, Office of Translational Sciences, Center for 
      Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, 
      Maryland, USA.
FAU - Yang, Yuching
AU  - Yang Y
AD  - Office of Clinical Pharmacology, Office of Translational Sciences, Center for 
      Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, 
      Maryland, USA.
FAU - Zhang, Xinyuan
AU  - Zhang X
AD  - Office of Clinical Pharmacology, Office of Translational Sciences, Center for 
      Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, 
      Maryland, USA.
FAU - Fan, Jianghong
AU  - Fan J
AD  - Office of Clinical Pharmacology, Office of Translational Sciences, Center for 
      Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, 
      Maryland, USA.
FAU - Grimstein, Manuela
AU  - Grimstein M
AD  - Office of Clinical Pharmacology, Office of Translational Sciences, Center for 
      Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, 
      Maryland, USA.
FAU - Zhu, Hao
AU  - Zhu H
AD  - Office of Clinical Pharmacology, Office of Translational Sciences, Center for 
      Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, 
      Maryland, USA.
FAU - Wang, Yaning
AU  - Wang Y
AD  - Office of Clinical Pharmacology, Office of Translational Sciences, Center for 
      Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, 
      Maryland, USA.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
PT  - Research Support, U.S. Gov't, P.H.S.
DEP - 20210209
PL  - England
TA  - J Clin Pharmacol
JT  - Journal of clinical pharmacology
JID - 0366372
SB  - IM
MH  - Area Under Curve
MH  - Computer Simulation
MH  - Drug Approval/statistics & numerical data
MH  - Drug Interactions/*physiology
MH  - Humans
MH  - In Vitro Techniques/standards/*statistics & numerical data
MH  - Metabolic Clearance Rate
MH  - *Models, Biological
MH  - United States
MH  - United States Food and Drug Administration/*statistics & numerical data
OTO - NOTNLM
OT  - drug-drug interactions
OT  - new drug application
OT  - physiologically based pharmacokinetic (PBPK) model
OT  - regulatory
EDAT- 2021/01/19 06:00
MHDA- 2021/12/21 06:00
CRDT- 2021/01/18 12:20
PHST- 2020/09/24 00:00 [received]
PHST- 2021/01/13 00:00 [accepted]
PHST- 2021/01/19 06:00 [pubmed]
PHST- 2021/12/21 06:00 [medline]
PHST- 2021/01/18 12:20 [entrez]
AID - 10.1002/jcph.1819 [doi]
PST - ppublish
SO  - J Clin Pharmacol. 2021 Jun;61(6):782-788. doi: 10.1002/jcph.1819. Epub 2021 Feb 
      9.