Prediction of pharmacokinetics and drug-drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach: A case study of caffeine and ciprofloxacin.
10.4196/kjpp.2017.21.1.107
- Author:
Min Ho PARK
1
;
Seok Ho SHIN
;
Jin Ju BYEON
;
Gwan Ho LEE
;
Byung Yong YU
;
Young G SHIN
Author Information
1. College of Pharmacy, Chungnam National University, Daejeon 34134, Korea. yshin@cnu.ac.kr
- Publication Type:Original Article
- Keywords:
Caffeine;
Ciprofloxacin;
Drug-drug interaction;
Physiologically based pharmacokinetics
- MeSH:
Area Under Curve;
Caffeine*;
Ciprofloxacin*;
Computer Simulation;
Drug Discovery;
Humans;
In Vitro Techniques;
Permeability;
Pharmacokinetics*;
Solubility;
Tissue Distribution
- From:The Korean Journal of Physiology and Pharmacology
2017;21(1):107-115
- CountryRepublic of Korea
- Language:English
-
Abstract:
Over the last decade, physiologically based pharmacokinetics (PBPK) application has been extended significantly not only to predicting preclinical/human PK but also to evaluating the drug-drug interaction (DDI) liability at the drug discovery or development stage. Herein, we describe a case study to illustrate the use of PBPK approach in predicting human PK as well as DDI using in silico, in vivo and in vitro derived parameters. This case was composed of five steps such as: simulation, verification, understanding of parameter sensitivity, optimization of the parameter and final evaluation. Caffeine and ciprofloxacin were used as tool compounds to demonstrate the “fit for purpose” application of PBPK modeling and simulation for this study. Compared to caffeine, the PBPK modeling for ciprofloxacin was challenging due to several factors including solubility, permeability, clearance and tissue distribution etc. Therefore, intensive parameter sensitivity analysis (PSA) was conducted to optimize the PBPK model for ciprofloxacin. Overall, the increase in C(max) of caffeine by ciprofloxacin was not significant. However, the increase in AUC was observed and was proportional to the administered dose of ciprofloxacin. The predicted DDI and PK results were comparable to observed clinical data published in the literatures. This approach would be helpful in identifying potential key factors that could lead to significant impact on PBPK modeling and simulation for challenging compounds.
