Modeling First-Pass Metabolism: Advances in Predictive Algorithms for Hepatic Clearance

Abstract

First-pass metabolism, particularly hepatic clearance, plays a crucial role in determining the bioavailability and efficacy of orally administered drugs. Accurately predicting the extent of first-pass metabolism remains a critical challenge in pharmacokinetics and drug development. Traditional modeling approaches, such as compartmental and physiologically based pharmacokinetic (PBPK) models, offer valuable insights but often fall short in capturing the complexity and variability of hepatic drug metabolism. Recent advances in predictive algorithms, including machine learning and deep learning techniques, have opened new avenues for more accurate and individualized predictions of hepatic clearance. These data-driven models leverage large pharmacokinetic datasets, molecular descriptors, and omics data to improve prediction performance and generalizability. This article reviews current developments in algorithmic modeling of first-pass metabolism, discusses their integration with mechanistic approaches, and evaluates their potential applications and limitations in the drug development pipeline. The paper also highlights emerging trends such as hybrid modeling, the use of real-world data, and regulatory challenges that must be addressed for broader adoption.