Compatibility Study between Physiologically Based Pharmacokinetic (PBPK) and Compartmental PK Model Using Lumping Method: Application to the Voriconazole Case
10.24304/kjcp.2021.31.2.125
- Author:
Hyo-jeong RYU
1
;
Won-ho KANG
;
Jung-woo CHAE
;
Hwi-yeol YUN
Author Information
1. College of Pharmacy, Chungnam National University, Daejeon 34134, Republic of Korea
- Publication Type:Original Article
- From:Korean Journal of Clinical Pharmacy
2021;31(2):125-135
- CountryRepublic of Korea
- Language:English
-
Abstract:
Background:Generally, pharmacokinetics (PK) models could be stratified into two models. The compartment PK model uses the concept of simple compartmentalization to describe complex bodies, and the physiologically based pharmacokinetic (PBPK) model describes the body using multi-compartment networking. Notwithstanding sharing a theoretical background in both models, there was still a lack of knowledge to enhance compatibility in both models.
Objective:This study aimed to evaluate the compatibility among PBPK, lumping model and compartment PK model with voriconazole PK case study.
Methods:The number of compartments and blood flow on each tissue in the PBPK model were modified using the lumping method, considering physiological similarities. The concentration-time profiles and area under the concentration-time curve (AUC) parameters were simulated at each model, assuming taken voriconazole oral 400 mg single dose. After that, those mentioned PK parameters were compared.
Results:The PK profiles and parameters of voriconazole in the three models were similar that proves their compatibility. The AUC of central compartment in the PBPK and lumping model was within a 2-fold range compared to those in the 2-compartment model. The AUC of non-eliminating tissues compartment in the PBPK model was similar to those in the lumping model.
Conclusion:Regarding the compatibility of the three PK models, the utilization of the lumping method was confirmed by suggesting its reliable PK parameters with PBPK and compartment PK models. Further case studies are recommended to confirm our findings.
