Intracellular Unbound Atorvastatin Concentrations in the Presence of Metabolism and Transport

Aldo-keto reductase-7A2 protects against atorvastatin-induced hepatotoxicity via Nrf2 activation

Atorvastatin (ATO), as a cholesterol-lowering drug, was the world's best-selling drug in the early 2000s. However, ATO overdose-induced liver or muscle injury is a threat to many patients, which restricts its application. Previous studies suggest that ATO overdose is accompanied with ROS accumulation and increased lipid peroxidation, which are the leading causes of ATO-induced liver damage. This study is, therefore, carried out to investigate the roles of anti-oxidant pathways and enzymes in protection against ATO-induced hepatotoxicity. Here we show that in ATO-challenged HepG2 cells, the expression levels of transcription factor NFE2L2/Nrf2 (nuclear factor erythroid 2 p45-related factor 2) are significantly upregulated. When Nrf2 is pharmacologically inhibited or genetically inactivated, ATO-induced cytotoxicity is significantly aggravated. Aldo-keto reductase-7A (AKR7A) enzymes, transcriptionally regulated by Nrf2, are important for bioactivation and biodetoxification. Here, we reveal that in response to ATO exposure, mRNA levels of human AKR7A2 are significantly upregulated in HepG2 cells. Furthermore, knockdown of AKR7A2 exacerbates ATO-induced hepatotoxicity, suggesting that AKR7A2 is essential for cellular adaptive response to ATO-induced cell damage. In addition, overexpression of AKR7A2 in HepG2 cells can significantly mitigate ATO-induced cytotoxicity and this process is Nrf2-dependent. Taken together, these findings indicate that Nrf2-mediated AKR7A2 is responsive to high concentrations of ATO and contributes to protection against ATO-induced hepatotoxicity, making it a good candidate for mitigating ATO-induced side effects.

Using Partition Analysis as a Facile Method to Derive Net Clearances

Partition analysis has been described previously by W.W. Cleland to derive net rate constants and simplify the derivation of enzyme kinetic equations. Here, we show that partition analysis can be used to derive elimination and transfer (distribution) net clearances for use in pharmacokinetic models. For elimination clearances, the net clearance approach is exemplified with a mammillary two‐compartment model with peripheral elimination, and the established well‐stirred and full hepatic clearance models. The intrinsic hepatic clearance associated with an observed average hepatic clearance can be easily calculated with net clearances. Expressions for net transfer clearances are easily derived, including models with explicit membranes (e.g., monolayer permeability and blood–brain barrier models). Together, these approaches can be used to derive equations for physiologically based and hybrid compartmental/ physiologically based models. This tutorial describes how net clearances can be used to derive relationships for simple models as well as increasingly complex models, such as inclusion of active transport and target mediated processes.

Recent developments in in vitro and in vivo models for improved translation of preclinical pharmacokinetics and pharmacodynamics data

Improved pharmacokinetics/pharmacodynamics (PK/PD) prediction in the early stages of drug development is essential to inform lead optimization strategies and reduce attrition rates. Recently, there have been significant advancements in the development of new in vitro and in vivo strategies to better characterize pharmacokinetic properties and efficacy of drug leads. Herein, we review advances in experimental and mathematical models for clearance predictions, advancements in developing novel tools to capture slowly metabolized drugs, in vivo model developments to capture human etiology for supporting drug development, limitations and gaps in these efforts, and a perspective on the future in the field.

Prediction of Drug Clearance from Enzyme and Transporter Kinetics

Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.

A hybrid model to evaluate the impact of active uptake transport on hepatic distribution of atorvastatin in rats

1. Mathematical modeling remains a useful tool to study the impact of transporters on overall and intracellular drug disposition. The impact of organic anion transporter protein mediated uptake on atorvastatin systemic and intracellular pharmacokinetics, specifically distribution volume, was studied in rats with mathematical modeling and conducting in vivo pharmacokinetic studies for atorvastatin in presence and absence of rifampicin. A previously developed 5-compartment explicit membrane model for the liver was combined with a compartmental model to develop a semi-physiological hybrid model for atorvastatin disposition. 2. Rifampicin treatment resulted in a decrease in systemic clearance and steady-state distribution volume, and an increase in half-life of atorvastatin. The hybrid model predicted higher unbound intracellular liver atorvastatin concentrations than unbound plasma concentrations in both rifampicin treated and untreated groups, indicating involvement of uptake transporters. The intracellular unbound concentrations during the distributive phase were unaffected by rifampicin. The dependence of clearance on blood flow as well as hepatic uptake for atorvastatin (a moderate-to-high extraction ratio drug) can explain this lack of change in intracellular concentrations if there is incomplete inhibition of transport at the tested rifampicin dose. 3. The hybrid model successfully allowed the evaluation of effect of active uptake on intracellular and plasma atorvastatin disposition.

Prediction of Tissue-Plasma Partition Coefficients Using Microsomal Partitioning: Incorporation into Physiologically based Pharmacokinetic Models and Steady-State Volume of Distribution Predictions

Drug distribution is a necessary component of models to predict human pharmacokinetics. A new membrane-based tissue-plasma partition coefficient (K p) method (K p,mem) to predict unbound tissue to plasma partition coefficients (K pu) was developed using in vitro membrane partitioning [fraction unbound in microsomes (f um)], plasma protein binding, and log P The resulting K p values were used in a physiologically based pharmacokinetic (PBPK) model to predict the steady-state volume of distribution (V ss) and concentration-time (C-t) profiles for 19 drugs. These results were compared with K p predictions using a standard method [the differential phospholipid K p prediction method (K p,dPL)], which differentiates between acidic and neutral phospholipids. The K p,mem method was parameterized using published rat K pu data and tissue lipid composition. The K pu values were well predicted with R 2 = 0.8. When used in a PBPK model, the V ss predictions were within 2-fold error for 12 of 19 drugs for K p,mem versus 11 of 19 for Kp,dPL With one outlier removed for K p,mem and two for K p,dPL, the V ss predictions for R 2 were 0.80 and 0.79 for the K p,mem and K p,dPL methods, respectively. The C-t profiles were also predicted and compared. Overall, the K p,mem method predicted the V ss and C-t profiles equally or better than the K p,dPL method. An advantage of using f um to parameterize membrane partitioning is that f um data are used for clearance prediction and are, therefore, generated early in the discovery/development process. Also, the method provides a mechanistically sound basis for membrane partitioning and permeability for further improving PBPK models. SIGNIFICANCE STATEMENT: A new method to predict tissue-plasma partition coefficients was developed. The method provides a more mechanistic basis to model membrane partitioning.

Methods to Predict Volume of Distribution

PURPOSE OF REVIEW

Prior to human studies, knowledge of drug disposition in the body is useful to inform decisions on drug safety and efficacy, first in human dosing, and dosing regimen design. It is therefore of interest to develop predictive models for primary pharmacokinetic parameters, clearance, and volume of distribution. The volume of distribution of a drug is determined by the physiological properties of the body and physiochemical properties of the drug, and is used to determine secondary parameters, including the half-life. The purpose of this review is to provide an overview of current methods for the prediction of volume of distribution of drugs, discuss a comparison between the methods, and identify deficiencies in current predictive methods for future improvement.

RECENT FINDINGS

Several volumes of distribution prediction methods are discussed, including preclinical extrapolation, physiological methods, tissue composition-based models to predict tissue:plasma partition coefficients, and quantitative structure-activity relationships. Key factors that impact the prediction of volume of distribution, such as permeability, transport, and accuracy of experimental inputs, are discussed. A comparison of current methods indicates that in general, all methods predict drug volume of distribution with an absolute average fold error of 2-fold. Currently, the use of composition-based PBPK models is preferred to models requiring in vivo input.

SUMMARY

Composition-based models perfusion-limited PBPK models are commonly used at present for prediction of tissue:plasma partition coefficients and volume of distribution, respectively. A better mechanistic understanding of important drug distribution processes will result in improvements in all modeling approaches.

Atorvastatin induces mitochondrial dysfunction and cell apoptosis in HepG2 cells via inhibition of the Nrf2 pathway

Atorvastatin (ATO) is a 3-hydroxy-3-methylglutaryl-CoA reductase inhibitor widely used to treat hypercholesterolemia. However, clinical application is limited by potential hepatotoxicity. Nuclear factor-erythroid 2-related factor 2 (Nrf2) is a master regulator of cellular antioxidants, and oxidative stress is implicated in statin-induced liver injury. This study investigated mechanisms of ATO-induced hepatotoxicity and potential mitigation by Nrf2 signaling. ATO reduced Nrf2 and antioxidant enzyme superoxide dismutase-2 (SOD2) expression in human hepatocarcinoma HepG2 cells. ATO also induced concentration-dependent HepG2 cell toxicity, reactive oxygen species (ROS) accumulation, and mitochondrial dysfunction as evidenced by decreased mitochondrial membrane potential (MMP) and cellular adenosine triphosphate (ATP). Further, ATO induced mitochondria-dependent apoptosis as indicated by increased Bax/Bcl-2 ratio, cleaved caspase-3, mitochondrial cytochrome c release and Annexin V-fluorescein isothiocyanate/propidium iodide staining. Tert-butylhydroquinone enhanced Nrf2 and SOD2 expression, and partially reversed ATO-induced cytotoxicity, ROS accumulation, MMP reduction, ATP depletion and mitochondria-dependent apoptosis. In conclusion, the present study demonstrates that ATO induces mitochondrial dysfunction and cell apoptosis in HepG2 cells, at least in part, via inhibition of the Nrf2 pathway. Nrf2 pathway activation is a potential prevention for ATO-induced liver injury.

A mechanistic modelling approach for the determination of the mechanisms of inhibition by cyclosporine on the uptake and metabolism of atorvastatin in rat hepatocytes using a high throughput uptake method

Determine the inhibition mechanism through which cyclosporine inhibits the uptake and metabolism of atorvastatin in fresh rat hepatocytes using mechanistic models applied to data generated using a high throughput oil spin method.Atorvastatin was incubated in fresh rat hepatocytes (0.05-150 nmol/ml) with or without 20 min pre-incubation with 10 nmol/ml cyclosporine and sampled over 0.25-60 min using a high throughput oil spin method. Micro-rate constant and macro-rate constant mechanistic models were ranked based on goodness of fit values.The best fitting model to the data was a micro-rate constant mechanistic model including non-competitive inhibition of uptake and competitive inhibition of metabolism by cyclosporine (Model 2). The association rate constant for atorvastatin was 150-fold greater than the dissociation rate constant and 10-fold greater than the translocation into the cell. The association and dissociation rate constants for cyclosporine were 7-fold smaller and 10-fold greater, respectively, than atorvastatin. The simulated atorvastatin-transporter-cyclosporine complex derived using the micro-rate constant parameter estimates increased in line with the incubation concentration of atorvastatin.The increased amount of data generated with the high throughput oil spin method, combined with a micro-rate constant mechanistic model helps to explain the inhibition of uptake by cyclosporine following pre-incubation.

Advancing Predictions of Tissue and Intracellular Drug Concentrations Using In Vitro, Imaging and Physiologically Based Pharmacokinetic Modeling Approaches

This white paper examines recent progress, applications, and challenges in predicting unbound and total tissue and intra/subcellular drug concentrations using in vitro and preclinical models, imaging techniques, and physiologically based pharmacokinetic (PBPK) modeling. Published examples, regulatory submissions, and case studies illustrate the application of different types of data in drug development to support modeling and decision making for compounds with transporter-mediated disposition, and likely disconnects between tissue and systemic drug exposure. The goals of this article are to illustrate current best practices and outline practical strategies for selecting appropriate in vitro and in vivo experimental methods to estimate or predict tissue and plasma concentrations, and to use these data in the application of PBPK modeling for human pharmacokinetic (PK), efficacy, and safety assessment in drug development.

Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A

Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min-1, the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min-1. These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

Cellular Pharmacokinetic Model-Based Analysis of Genistein, Glyceollin, and MK-571 Effects on 5 (and 6)-Carboxy-2',7'-Dichloroflourescein Disposition in Caco-2 Cells

Pharmacokinetic modeling was used to describe 5 (and 6)-carboxy-2',7'-dichloroflourescein (CDF) disposition in Caco-2 cells following CDF or CDFDA (CDF diacetate) dosing. CDF transcellular flux was modeled by simple passive diffusion. CDFDA dosing models were based on simultaneous fitting of CDF levels in apical, basolateral, and intracellular compartments. Predicted CDF efflux was 50% higher across the apical versus the basolateral membrane. This difference was similar following apical and basolateral CDFDA dosing, despite intracellular levels being 3-fold higher following basolateral dosing, thus supporting nonsaturable CDF efflux kinetics. A 3-compartment catenary model with intracellular CDFDA hydrolysis described CDF disposition. This model predicted that apical CDF efflux was not altered in the presence of MK-571, and that basolateral membrane clearance was enhanced to account for reduced intracellular CDF in the presence of this multidrug resistance-associated protein (MRP) inhibitor. Similar effects were predicted for glyceollin, while genistein exposure had no predicted effects on CDF efflux. These modulator effects are discussed in the context of model predicted intracellular CDF concentrations relative to reports of CDF affinity (measured by Km) for MRP2 and MRP3. This model-based analysis confirms the complexity of efflux kinetics and suggests that other transporters may have contributed to CDF efflux.

Continuous Intestinal Absorption Model Based on the Convection-Diffusion Equation

Prediction of the rate and extent of drug absorption upon oral dosing needs models that capture the complexities of both the drug molecule and intestinal physiology. We report here the development of a continuous intestinal absorption model based on the convection-diffusion equation. The model includes explicit enterocyte apical membrane and intracellular lipid radial compartments along the length of the intestine. Physiologic functions along length x are built into the model and include velocity, diffusion, surface areas, and pH of the intestine. Also included are expression levels of the intestinal active uptake transporter OATP2B1 and efflux transporter P-gp. Oral dosing of solution as well as solid (with a dissolution function) was modeled for several drugs. The fraction absorbed (FA) and concentration-time (C-t) profiles were predicted and compared with clinical data. Overall, FA was well predicted upon oral (n = 21) or colonic dosing (n = 11), with four outliers. The overall accuracy (prediction of the correct bin) was 81% with outliers and 90% without outliers. Of the nine solution dosing data sets, six drugs were very well predicted with an exposure overlap coefficient (EOC) > 0.9 and predicted C_max and T_max values similar to those observed. Of the six solid dose formulations evaluated, the EOC values were > 0.9 for all drugs except budesonide. The observed precipitation of nifedipine at high doses was predicted by the model. Most of the poor predictions were for drugs that are known to be transporter substrates. As proof of concept, incorporating OATP2B1 and P-gp markedly improved the EOC and predicted C_max and T_max for fexofenadine. Finally, the continuous intestinal model accurately recapitulated the known relationships between drug absorption and permeability, solubility, and particle size. Together, these results indicate that this preliminary intestinal absorption model offers a simple and straightforward framework to build in complexities such as drug permeability, lipid partitioning, solubility, metabolism, and transport for improved prediction of the rate and extent of drug absorption.