Abstract
Among the basic hepatic clearance models, the dispersion model (DM) is the most physiologically sound compared with the well-stirred model and the parallel tube model. However, its application in physiologically-based pharmacokinetic (PBPK) modeling has been limited due to computational complexities. The series compartment models (SCM) of hepatic elimination that treats the liver as a cascade of well-stirred compartments connected by hepatic blood flow exhibits some mathematical similarities to the DM but is easier to operate. This work assesses the quantitative correlation between the SCM and DM and demonstrates the operation of the SCM in PBPK with the published single-dose blood and liver concentration-time data of six flow-limited compounds. The predicted liver concentrations and the estimated intrinsic clearance (CL(int) ) and PBPK-operative tissue-to-plasma partition coefficient (K(p) ) values were shown to depend on the number of liver sub-compartments (n) and hepatic enzyme zonation in the SCM. The CL(int) and K(p) decreased with increasing n, with more remarkable differences for drugs with higher hepatic extraction ratios. Given the same total CL(int) , the SCM yields a higher K(p) when the liver perivenous region exhibits a lower CL(int) as compared with a high CL(int) at this region. Overall, the SCM nicely approximates the DM in characterizing hepatic elimination and offers an alternative flexible approach as well as providing some insights regarding sequential drug concentrations in the liver. SIGNIFICANCE STATEMENT: The SCM nicely approximates the DM when applied in PBPK for characterizing hepatic elimination. The number of liver sub-compartments and hepatic enzyme zonation are influencing factors for the SCM resulting in model-dependent predictions of total/internal liver concentrations and estimates of CL(int) and the PBPK-operative K(p) . Such model-dependency may have an impact when the SCM is used for in vitro-to-in vivo extrapolation (IVIVE) and may also be relevant for PK/PD/toxicological effects when it is the driving force for such responses.