Beyond the Michaelis-Menten: Evaluation of a tQSSA-Based IVIVE Approach for Predicting In Vivo Intrinsic Clearance From Hepatocyte Assays.

The classical Michaelis-Menten model, under the standard quasi-steady-state approximation (sQSSA), is widely used in in vitro-in vivo extrapolation (IVIVE) studies using hepatocyte or human liver microsomal (HLM) assays to predict intrinsic hepatic clearance ( Cl int , vitro $$ {\mathrm{Cl}}_{\operatorname{int},\mathrm{vitro}} $$ ). However, the approximation that enzyme concentration ( E T $$ {E}_T $$ ) is much lower than the Michaelis constant ( K M $$ {K}_M $$ ) does not always hold true, especially for low K M $$ {K}_M $$ compounds or enzyme induction scenarios, leading to inaccurate predictions. To improve the accuracy of IVIVE predictions, the total quasi-steady-state approximation (tQSSA) which accounts for enzyme saturation when E T $$ {E}_T $$ is not negligible relative to K M $$ {K}_M $$ was first applied to HLM data and confirmed that it improved clearance prediction compared with the sQSSA. Building on this, we further evaluated the performance of tQSSA using hepatocyte data. The in vivo intrinsic hepatic clearance was predicted using both the sQSSA and tQSSA with the well-stirred and parallel tube models. Predictions were evaluated across three scenarios: (1) using both the unbound fraction in blood ( f u , b $$ {f}_{u,b} $$ ) and the in vitro hepatocyte incubation system ( f u , inc $$ {f}_{u,\mathrm{inc}} $$ ), (2) using only f u , b $$ {f}_{u,b} $$ , and (3) without correction. Results showed that the sQSSA tended to overpredict clearance when E T $$ {E}_T $$ ≥ K M $$ {K}_M $$ . In the 77-compound dataset, the tQSSA yielded slightly better agreement, particularly when f u , b $$ {f}_{u,b} $$ = f u , inc $$ {f}_{u,\mathrm{inc}} $$ = 1, whereas with mechanistic binding corrections both models performed similarly. For the 11-compound subset with known K M $$ {K}_M $$ values, the proportion within 2-fold error improved by about 1.5-fold compared with sQSSA. Overall, tQSSA appears promising but requires further validation for IVIVE applications.