Korean J Physiol Pharmacol.  2017 Jan;21(1):107-115. 10.4196/kjpp.2017.21.1.107.

Prediction of pharmacokinetics and drug-drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach: A case study of caffeine and ciprofloxacin

• Park MH
• Shin SH
• Byeon JJ
• Lee GH
• Yu BY
• Shin YG

• KMID: 2371074
• DOI: http://doi.org/10.4196/kjpp.2017.21.1.107

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.

Keyword

Caffeine; Ciprofloxacin; Drug-drug interaction; Physiologically based pharmacokinetics

MeSH Terms

Area Under Curve
Caffeine*
Ciprofloxacin*
Computer Simulation
Drug Discovery
Humans
In Vitro Techniques
Permeability
Pharmacokinetics*
Solubility
Tissue Distribution
Caffeine
Ciprofloxacin

Figure

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  Fig. 1 Proposed workflow for PBPK modeling.

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  Fig. 2 Comparison of observed values and predicted values for caffeine.(A) Predicted and observed concentration-time profile of caffeine in Daniel et al.'s paper, (B) Comparison of observed PK values and predicted PK values*. *The PK values of comparison are Cmax, Tmax and AUClast.

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  Fig. 3 PK profile of ciprofloxacin after 500 mg PO dose in human.(A) PK profile predicted by only predicted input value, (B) PK profile predicted after changing the solubility of ciprofloxacin, (C) PK profile predicted after optimizing the permeability, Kp of liver and CL of ciprofloxacin.

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  Fig. 4 Parameter sensitivity analysis of permeability, CL and Kp of liver on (A) Cmax (B) Tmax and (C) AUClast for Ciprofloxacin.

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  Fig. 5 Comparison of observed and predicted PK values* for ciprofloxacin.*The PK values of comparison are Cmax, Tmax and AUClast.

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