Development of Physiologically Based Liver Distribution Model that Incorporates Intracellular Lipid Partitioning and Binding to Fatty Acid Binding Protein 1 (FABP1)

Abstract Steady-state volume of distribution (V ss ) can be predicted using tissue-to-plasma partition coefficients (K p ) and tissue volumes. K p values are important components of physiologically based pharmacokinetic (PBPK) models, allowing for estimation of distribution kinetics and simulation of concentration-time profiles. Many in silico approaches have been developed to predict tissue K p values based on physicochemical processes that govern drug distribution. However, these methods frequently over- or under-predict tissue K p values, highlighting the need to consider additional mechanisms that can impact drug distribution kinetics. Many drugs have been shown to bind to rat and human fatty acid binding proteins (FABPs) in vitro but the impact of this binding to drug distribution has not been incorporated into K p predictions. We hypothesized that incorporating intracellular protein binding into tissue K p predictions will improve K p prediction accuracy. Using liver as a model organ, four physiologically based dynamic liver distribution models (LDMs) were developed to assess the role of distribution processes in K p predictions. The developed LDMs incorporated known distribution mechanisms and intracellular drug binding to liver FABP (FABP1). The liver K p values for drugs that bind to FABP1 were accurately predicted using the LDM that incorporates lipid partitioning, albumin distribution, and FABP1 binding but not using LDMs without FABP1 binding. Human FABP1 expression was quantified in 61 human livers and the interindividual variability in tissue FABP1 binding was incorporated into tissue K p predictions. These simulations showed that intracellular FABP1 binding can cause interindividual variability in K p values and result in concentration dependent tissue distribution. Significance Statement This study shows that incorporating intracellular protein binding such as binding to FABP1 into tissue K p predictions improves accuracy of the predictions. The novel dynamic LDM can be extrapolated to other organs of interest and integrated into full-body PBPK models to predict drug distribution kinetics. With dynamic and saturable distribution mechanisms incorporated into a PBPK model, nonlinear distribution kinetics can be simulated for various drugs.