Proof of Concept of an All-in-One System for Measuring Hepatic Influx, Egress, and Metabolic Clearance Based on the Extended Clearance Concept.

Hepatic clearance (CLH ) prediction is a critical parameter to estimate human dose. However, CLH underpredictions are common, especially for slowly metabolized drugs, and may be attributable to drug properties that pose challenges for conventional in vitro absorption, distribution, metabolism, and elimination (ADME) assays, resulting in nonvalid data, which prevents in vitro to in vivo extrapolation and CLH predictions. Other processes, including hepatocyte and biliary distribution via transporters, can also play significant roles in CLH Recent advances in understanding the interplay of metabolism and drug transport for clearance processes have aided in developing the extended clearance model. In this study, we demonstrate proof of concept of a novel two-step assay enabling the measurement of multiple kinetic parameters from a single experiment in plated human primary hepatocytes with and without transporter and cytochrome P450 inhibitors-the hepatocyte uptake and loss assay (HUpLA). HUpLA accurately predicted the CLH of eight of the nine drugs (within twofold of the observed CLH ). Distribution clearances were within threefold of observed literature values in standard uptake and efflux assays. In comparison, the conventional suspension hepatocyte stability assay poorly predicted the CLH The CLH of only two drugs was predicted within twofold of the observed CLH Therefore, HUpLA is advantageous by enabling the measurement of enzymatic and transport processes concurrently within the same system, alleviating the need for applying scaling factors independently. The use of primary human hepatocytes enables physiologically relevant exploration of transporter-enzyme interplay. Most importantly, HUpLA shows promise as a sensitive measure for low-turnover drugs. Further evaluation across different drug characteristics is needed to demonstrate method robustness. SIGNIFICANCE STATEMENT: The hepatocyte uptake and loss assay involves measuring four commonly derived in vitro hepatic clearance endpoints. Since endpoints are generated within a single test system, it blunts experimental error originating from assays otherwise conducted independently. A key advantage is the concept of removing drug-containing media following intracellular drug loading, enabling the measurement of drug reappearance rate in media as well as the measurement of loss of total drug in the test system unencumbered by background quantities of drug in media otherwise present in a conventional assay.

Plasma protein-mediated uptake and contradictions to the free drug hypothesis: a critical review.

According to the free drug hypothesis (FDH), only free, unbound drug is available to interact with biological targets. This hypothesis is the fundamental principle that continues to explain the vast majority of all pharmacokinetic and pharmacodynamic processes. Under the FDH, the free drug concentration at the target site is considered the driver of pharmacodynamic activity and pharmacokinetic processes. However, deviations from the FDH are observed in hepatic uptake and clearance predictions, where observed unbound intrinsic hepatic clearance (CLint,u) is larger than expected. Such deviations are commonly observed when plasma proteins are present and form the basis of the so-called plasma protein-mediated uptake effect (PMUE). This review will discuss the basis of plasma protein binding as it pertains to hepatic clearance based on the FDH, as well as several hypotheses that may explain the underlying mechanisms of PMUE. Notably, some, but not all, potential mechanisms remained aligned with the FDH. Finally, we will outline possible experimental strategies to elucidate PMUE mechanisms. Understanding the mechanisms of PMUE and its potential contribution to clearance underprediction is vital to improving the drug development process.

Assumptions Underlying Hepatic Clearance Models: Recognizing the Influence of Saturable Protein Binding on Driving Force Concentration and Discrimination Between Models of Hepatic Clearance.

One underlying assumption of hepatic clearance models is often underappreciated. Namely, plasma protein binding is assumed to be nonsaturable within a given drug concentration range, dependent only on protein concentration and equilibrium dissociation constant. However, in vitro hepatic clearance experiments often use low albumin concentrations that may be prone to saturation effects, especially for high-clearance compounds, where the drug concentration changes rapidly. Diazepam isolated perfused rat liver literature datasets collected at varying concentrations of albumin were used to evaluate the predictive utility of four hepatic clearance models (the well-stirred, parallel tube, dispersion, and modified well-stirred model) while both ignoring and accounting for potential impact of saturable protein binding on hepatic clearance model discrimination. In agreement with previous literature findings, analyses without accounting for saturable binding showed poor clearance prediction using all four hepatic clearance models. Here we show that accounting for saturable albumin binding improves clearance predictions across the four hepatic clearance models. Additionally, the well-stirred model best reconciles the difference between the predicted and observed clearance data, suggesting that the well-stirred model is an appropriate model to describe diazepam hepatic clearance when considering appropriate binding models. SIGNIFICANCE STATEMENT: Hepatic clearance models are vital for understanding clearance. Caveats in model discrimination and plasma protein binding have sparked an ongoing scientific discussion. This study expands the understanding of the underappreciated potential for saturable plasma protein binding. Fraction unbound must correspond to relevant driving force concentration. These considerations can improve clearance predictions and address hepatic clearance model disconnects. Importantly, even though hepatic clearance models are simple approximations of complex physiological processes, they are valuable tools for clinical clearance predictions.