Validation of Methods for in Vitro-in Vivo Extrapolation Using Hepatic Clearance Measurements in Isolated Perfused Fish Livers.

In vitro biotransformation assays using hepatocytes or liver subcellular fractions, combined with in vitro-in vivo extrapolation (IVIVE) models, have been proposed as an alternative to live fish bioconcentration studies. The uncertainty associated with IVIVE approaches to date has been attributed to assay protocols, model assumptions, or variability of in vivo data. An isolated perfused trout liver model that measures hepatic clearance has been proposed for validating IVIVE predictions in the absence of other confounding factors. Here, we investigated the hepatic clearances of five chemicals (pyrene, phenanthrene, 4-n-nonlyphenol, deltamethrin, and methoxychlor) in this model and compared measured rates to values predicted from published in vitro intrinsic clearances for validation of IVIVE models. Additionally, we varied protein concentrations in perfusates to test binding assumptions of these models. We found that measured and predicted hepatic clearances were in very good agreement (root mean squared error 16.8 mL h-1 g-1) across three levels of protein binding and across a more diverse chemical space than previously studied within this system. Our results show that current IVIVE methods can reliably predict in vivo clearance rates and indicate that discrepancies from measured bioconcentration factors might be driven by other processes, such as extrahepatic biotransformation, etc., and help streamline optimization efforts to the processes that truly matter.

Prediction of Unbound Fractions for in Vitro-in Vivo Extrapolation of Biotransformation Data.

For in vitro-in vivo extrapolation of biotransformation data, the different sorptive environments in vitro and in vivo need to be considered. The most common approach for doing so is using the ratio of unbound fractions in vitro and in vivo. In the literature, several algorithms for prediction of these unbound fractions are available. In this study, we present a theoretical evaluation of the most commonly used algorithms for prediction of unbound fractions in S9 assays and blood and compare prediction results with empirical values from the literature. The results of this analysis prove a good performance of "composition-based" algorithms, i.e. algorithms that represent the inhomogeneous composition of in vitro assay and in vivo system and describe sorption to the individual components (lipids, proteins, water) in the same way. For strongly sorbing chemicals, these algorithms yield constant values for the ratio of unbound fractions in vitro and in vivo. This is mechanistically plausible, because in these cases, the chemicals are mostly bound, and the ratio of unbound fractions is determined by the volume ratio of sorbing components in both phases.

In Vitro- in Vivo Extrapolation of Hepatic Metabolism for Different Scenarios - a Toolbox.

The extrapolation of metabolism data from in vitro experiments to in vivo clearances can provide useful information in the fields of pharmacokinetics and toxicokinetics. Depending on the purpose, different toxicokinetic models are used, and these different models require the in vivo metabolic information in different forms. In this study, a comprehensive toolbox for in vitro- in vivo extrapolation (IVIVE) of hepatic metabolism is presented addressing a variety of different extrapolation goals: extrapolation to hepatic blood clearance, extrapolation to organ clearance, extrapolation to whole-body clearance, and extrapolation to clearance at the level of hepatocytes. The use of the extrapolated clearances for calculation of extraction efficiencies and the use in physiologically based pharmacokinetic models are discussed. Furthermore, a sensitivity analysis demonstrates which parameters affect the accuracy of the extrapolation results the most, and the presented extrapolation procedure is evaluated by comparison to experimental data from perfused liver experiments.

The impact of desorption kinetics from albumin on hepatic extraction efficiency and hepatic clearance: a model study.

Until now, the question whether slow desorption of compounds from transport proteins like the plasma protein albumin can affect hepatic uptake and thereby hepatic metabolism of these compounds has not yet been answered conclusively. This work now combines recently published experimental desorption rate constants with a liver model to address this question. For doing so, the used liver model differentiates the bound compound in blood, the unbound compound in blood and the compound within the hepatocytes as three well-stirred compartments. Our calculations show that slow desorption kinetics from albumin can indeed limit hepatic metabolism of a compound by decreasing hepatic extraction efficiency and hepatic clearance. The extent of this decrease, however, depends not only on the value of the desorption rate constant but also on how much of the compound is bound to albumin in blood and how fast intrinsic metabolism of the compound in the hepatocytes is. For strongly sorbing and sufficiently fast metabolized compounds, our calculations revealed a twentyfold lower hepatic extraction efficiency and hepatic clearance for the slowest known desorption rate constant compared to the case when instantaneous equilibrium between bound and unbound compound is assumed. The same desorption rate constant, however, has nearly no effect on hepatic extraction efficiency and hepatic clearance of weakly sorbing and slowly metabolized compounds. This work examines the relevance of desorption kinetics in various example scenarios and provides the general approach needed to quantify the effect of flow limitation, membrane permeability and desorption kinetics on hepatic metabolism at the same time.

Desorption kinetics of organic chemicals from albumin.

When present in blood, most chemicals tend to bind to the plasma protein albumin. For distribution into surrounding tissues, desorption from albumin is necessary, because only the unbound form of a chemical is assumed to be able to cross cell membranes. For metabolism of chemicals, the liver is a particularly important organ. One potentially limiting step for hepatic uptake of the chemicals is desorption from albumin, because blood passes the human liver within seconds. Desorption kinetics from albumin can thus be an important parameter for our pharmacokinetic and toxicokinetic understanding of chemicals. This work presents a dataset of measured desorption rate constants and reveals a possibility for their prediction. Additionally, the obtained extraction profiles directly indicate physiological relevance of desorption kinetics, because desorption of the test chemicals is still incomplete after time frames comparable to the residence time of blood in the liver.

Relevance of desorption kinetics and permeability for in vitro-based predictions of hepatic clearance in fish.

The impact of desorption kinetics and permeation kinetics on in vitro-based predictions of in vivo hepatic blood clearances is investigated in the present study. Most commonly, possible limitations due to slow desorption of chemicals from albumin or slow permeation of chemicals through cellular membranes are not considered when in vivo clearances are predicted from in vitro biotransformation rate constants. To evaluate whether the most commonly used extrapolation models might thus overlook important kinetic limitations, we compare predictions of in vivo clearance that explicitly consider desorption and permeation kinetics with predictions of in vivo clearance that neglect these aspects. Our results show that strong limitations due to slow permeation kinetics are possible depending on the assumed permeability value. While permeability values estimated with a mechanistic approach are fast enough to avoid significant limitations, other experimentally derived permeability values lead to dramatically decreased in vivo clearance predictions. These latter values lead to unrealistically low in vivo biotransformation estimates. Furthermore, we also evaluated the implications of desorption kinetics using experimentally determined desorption rate constants. These evaluations show that slow desorption kinetics are unlikely to limit in vivo clearance.

Could chemical exposure and bioconcentration in fish be affected by slow binding kinetics in blood?

The possible implications of slow binding kinetics on respiratory uptake, bioconcentration and exposure of chemicals were evaluated in the present study. Most physiological and chemical information needed for such an evaluation is already known from the literature or can be estimated. However, data for binding kinetics of chemicals in fish plasma have not been reported in the literature yet. In the first part of this study, we therefore experimentally investigated the plasma binding kinetics for ten chemicals, including pollutants like polycyclic aromatic hydrocarbons and a pesticide. The determined desorption rate constants were in the range of 0.4 s-1 to 0.1 s-1. In the second part of this study, we present a comparative modeling analysis of generic predictions with binding kinetics of different velocities. For doing so, a model that explicitly represents binding kinetics in blood was developed and applied for different hypothetical scenarios. The evaluation showed that slow sorption kinetics only limits respiratory uptake and thus influences the levels of bioaccumulation for extreme and, by that, rather unlikely parameter combinations (i.e. for strongly sorbing chemicals with very slow binding kinetics). It can therefore be assumed that limitations on respiratory uptake due to slow binding kinetics in blood are rather unlikely for most chemicals.

Comparison of a simple and a complex model for BCF prediction using in vitro biotransformation data.

A promising approach for bioaccumulation assessment with reduced animal use is the prediction of bioconcentration factors (BCFs) using in vitro biotransformation data. However, it has been recognized that the BCFs predicted using current models often are in poor agreement with experimental BCFs. Furthermore, extrahepatic biotransformation (e.g. in gill or GIT) is usually not accounted for. Here, we compare two BCF prediction models: a simple one-compartment and a more advanced multi-compartment model. Both models are implemented in a two-in-one calculation tool for the prediction of BCFs using in vitro data. Furthermore, both models were set up in a way that in vitro data for extrahepatic biotransformation can be easily considered, if desired. The models differ in their complexity: the one-compartment model is attractive because its simplicity, while the multi-compartment model is characterized by its refined closeness to reality. A comparison of the results shows that both models yield almost identical results for the presently evaluated cases with plausible physiological data. For regulatory purposes, there is thus no reason not to use the simple one-compartment model. However, if it is desired to represent special in vivo characteristics, e.g. first-pass effects or the direct GIT-to-liver blood flow, the multi-compartment model should be used.