Beyond Michaelis-Menten: A modified enzyme kinetics equation improves drug metabolism prediction in bottom-Up PBPK modeling

Abstract

Physiologically based pharmacokinetic (PBPK) models are widely used methodology that dynamically integrates diverse biological parameters to predict pharmacokinetic profiles of drugs and their metabolites. Traditionally, PBPK models rely on the Michaelis-Menten (MM) equation to describe enzymatic rate processes such as transport, metabolism, and secretion in terms of intrinsic clearance. However, the MM equation assumes that enzyme concentrations ($E_T$) are substantially lower than the MM constant ($K_M$). This condition is often violated in vivo, resulting in inaccuracies in predicting clearance and drug-drug interactions. To address these inaccuracies, current PBPK approaches employ parameter optimization using phase I clinical trial data. However, this conflicts with the bottom-up paradigm of predicting human pharmacokinetics from preclinical data prior to human trials. Here, we resolve this conflict by implementing a modified metabolic rate equation within the PBPK modeling framework. Unlike the MM equation, the modified equation remains valid even when $E_T$ is comparable to $K_M$, thereby improving prediction accuracy in static models. Our results demonstrate that using the modified equation outperforms the conventional MM-based method even in dynamic PBPK modeling, particularly in scenarios where the MM equation's assumptions are invalid. Based on these findings, we propose expanded guidelines to broaden the applicability of PBPK modeling using the modified equation. This advancement offers a significant contribution to pharmacokinetic research and enhances the utility of PBPK models in drug development.