INTERPLAY OF DRUG TRANSPORTERS AND ENZYMES ON HEPATIC DRUG PROCESSING - Drug Transporters: Molecular Characterization and Role in Drug Disposition - page 737

The trends predicted for the biliary, metabolic, and total clearance of E 2 17G with
varying CL int , sec or CL int , sult are similar to those for enalapril and digoxin, in which
futile cycling is absent. Patterns for the PBPK and ZPBPK models are also similar.
An increase in the CL int of one pathway would decrease the clearance of the alter-
native pathway but increase the total clearance, whereas a decrease in the CL int of
one pathway would increase the clearance of the alternative pathway but decrease
the total clearance (Table 23.8). The existence of futile cycling further empowers the
metabolite, E 2 3S17G, to exert considerable influence on the rate and extent of hepatic
removal of the parent drug via desulfation intrinsic clearance (CL int , desult { M } ) and

the secretory intrinsic clearance of the metabolite, CL int , sec { , by altering the intra-

cellular concentration of E 2 17G. According to both the PBPK and ZPBPK models,
an increase in desulfation clearance of E 2 3S17G (CL int , desult {

M

}

) decreases the net
metabolic clearance and increases the apparent biliary clearance of E 2 17G (Table
23.8). Biliary excretion of the metabolite (CL int , sec {

M

}

), which removes the metabo-
lite irreversibly and prevents futile cycling, effectively increases the net sulfation
clearance and results in attenuated biliary excretion of the parent drug (Table 23.8).
These patterns are unique for E 2 17G and E 2 3S17G since the parent drug E 2 17G fails
to reenter the circulation at the basolateral membrane, and the metabolite E 2 3S17G
does not cross the basolateral membrane at all.

M

}

23.6. CONCLUSIONS

The predictive power of the simple and zonal physiologically based pharmacokinetic
liver models has been showcased in the chapter. The drug examples above reveal that
PBPK liver model has exquisite properties in presenting integrative views on hepatic
drug extraction. The ZPBPK liver model, which is based on the acinar distribution
of transporters and enzymes, is superior, since it is able to describe the attendant
heterogeneities of transporters and enzymes. To use this integrated approach, in vitro
hepatocyte transport and metabolic data and information on the zonal distribution of
transporters and enzymes are building blocks of the ZPBPK model. Enalapril and
digoxin have been chosen as the examples because of the richness of in vitro data on
transport and metabolism and ex vivo liver perfusion data in our laboratory. The data
and the simulations aptly demonstrate the interplay of transporters and enzymes. The
concepts developed are pertinent to all drugs and allow us to explain quantitatively the
effects of blood flow, vascular (plasma and RBC) binding, transport, and metabolism
on hepatic drug disposition. The models are able to predict changes in the biliary,
metabolic, and total hepatic clearances upon alteration of one or multiple parameters.
More important, the PBPK and ZPBPK models share common views on the interplay
of enzymes and transporters (Table 23.4). These concepts have been based solidly on
mass transfer principles and will be unyielding in terms of the predictions.
However, the scenario is altered with futile cycling, as exemplified by estradiol

17 -D-glucuronide that is sulfated to its 3-sulfate conjugate, which in turn is desulfated

back to the parent glucuronide. Due to the absence of E 2 17G basolateral efflux and
the complication due to futile cycling, the interplay between transporter and enzyme