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.

PBPK model for antibody disposition in mouse brain: validation using large-pore microdialysis data.

The objective of this manuscript was to validate a physiologically-based pharmacokinetic (PBPK) model developed to characterize brain pharmacokinetics (PK) of monoclonal antibodies (mAbs) using novel large-pore microdialysis data generated in mice. To support this objective, brain, CSF, and ISF PK of a human anti-tetanus toxin (TeTx) antibody was measured in mice following intraperitoneal (IP) administration. This antibody has no binding in mice. In addition, our recently published mouse brain PK data generated following intravenous (IV) and IP administration of trastuzumab in mice, and other published PK data for brain disposition of antibody in mice, were used to evaluate the PBPK model. All the model parameters were obtained from literature or kept the same as in our previously published manuscript. The revised PBPK model was able to characterize the PK of antibodies in mice brain, CSF, and ISF reasonably well (i.e., within a three-fold error). However, a priori selected parameters led to underprediction of ISF PK during the initial phase of the profile. A local sensitivity analysis suggested that minor changes in several brain-related parameters can help overcome this discrepancy, where an increase in the convective flow of antibodies across BBB was found to be the most parsimonious way to capture all the PK profiles well. However, the presence of this pathway needs further validation. As such, here we have presented an improved PBPK model to characterize and predict the PK of mAbs in different regions of the mouse brain following systemic administration. This model can serve as a quantitative tool to facilitate the discovery, preclinical evaluation, and preclinical-to-clinical translation of novel antibodies targeted against CNS disorders.

Drug-induced reactive oxygen species (ROS) rely on cell membrane properties to exert anticancer effects.

Pharmacological concentrations of small molecule natural products, such as ascorbic acid, have exhibited distinct cell killing outcomes between cancer and normal cells whereby cancer cells undergo apoptosis or necrosis while normal cells are not adversely affected. Here, we develop a mathematical model for ascorbic acid that can be utilized as a tool to understand the dynamics of reactive oxygen species (ROS) induced cell death. We determine that not only do endogenous antioxidants such as catalase contribute to ROS-induced cell death, but also cell membrane properties play a critical role in the efficacy of ROS as a cytotoxic mechanism against cancer cells vs. normal cells. Using in vitro assays with breast cancer cells, we have confirmed that cell membrane properties are essential for ROS, in the form of hydrogen peroxide (H2O2), to induce cell death. Interestingly, we did not observe any correlation between intracellular H2O2 and cell survival, suggesting that cell death by H2O2 is triggered by interaction with the cell membrane and not necessarily due to intracellular levels of H2O2. These findings provide a putative mechanistic explanation for the efficacy and selectivity of therapies such as ascorbic acid that rely on ROS-induced cell death for their anti-tumor properties.