Net Influx Rather Than Directional Rates: Re-evaluating Transporter Characterization In Vivo and In Vitro for Renal...

Here we challenge the assumption that hepatic uptake can be rate-limited solely by transporter-mediated influx clearance, as commonly concluded in interpretations based on the Extended Clearance Concept (ECC). We initially review the derivation of renal and hepatic clearance independent of differential equations based on adaptation of Kirchhoff's Laws from physics, incorporating clinically relevant aspects such as transporter activity, organ blood flow, and clearance from the site of drug delivery into systemic circulation. In doing so, we highlight the limitations of the ECC framework, which does not adequately capture all aspects of these mechanistic elements and note that no experimental data or PBPK analyses definitively support its validity. In contrast, we show that all hepatic clearance data can be adequately explained based on our derivations, which provide a more robust and mechanistically consistent framework for interpreting renal and hepatic clearance. The derived equations define the net difference between influx and efflux clearances as the key determinant of transporter involvement in hepatic drug disposition, rather than considering influx alone as in the ECC. This approach is analogous to the treatment of secretion and reabsorption clearances as opposing processes in renal clearance, where their difference determines the net transport. We also question the mechanistic accuracy of determining hepatic influx and efflux clearances in vitro using initial rates of membrane passage, as is typically done for passive processes. Such measurements inherently reflect the net intramembrane difference between influx and efflux clearances, and do not allow for independent quantification of directional transport, due to the inability to measure drug concentrations within the membrane. Finally, while we acknowledge the utility of ECC and PBPK analyses for predicting changes in pharmacokinetic exposure due to DDIs (or other variables such as disease state, pharmacogenomics, etc.), we caution that model-based data fitting, however useful, does not constitute mechanistic validation.