Prediction of hepatic clearance and availability by cryopreserved human hepatocytes: an application of serum incubation method

Introducing the Dynamic Well-Stirred Model for Predicting Hepatic Clearance and Extraction Ratio

The well-stirred model (WSM) incorporating the fraction of unbound drug (fu) to account for the effect of plasma binding on intrinsic clearance has been widely used for predicting hepatic clearance under the assumption that drug protein binding reaches equilibrium instantaneously. Our theoretical analysis reveals that the effect of protein binding on intrinsic clearance is better accounted for with the dynamic free fraction (fD), a measure of drug protein binding affinity, which leads to a putative dynamic well-stirred model (dWSM) without the instantaneous equilibrium assumption. Using recombinant CYP3A4 as the in vitro clearance system, we demonstrate that the binding effect of albumin on the intrinsic clearance of both highly bound midazolam and highly free verapamil is fully corrected by their corresponding fD values, respectively. On the other hand, fu only corrects the binding effect of albumin on the intrinsic clearance of verapamil, and yields severe over-correction of the intrinsic clearance of midazolam. The results suggest that the traditional WSM is suitable for highly free drugs like verapamil but not necessarily for highly bound drugs such as midazolam due to the violation of the instantaneous equilibrium assumption or under-estimating the true free drug concentration. In comparison, the dWSM incorporating fD holds true as long as drug elimination follows steady-state kinetics, and hence, it is more broadly applicable to drugs with different protein binding characteristics. Here we demonstrate with 36 diverse drugs, that the dWSM significantly improves the accuracy of predicting human hepatic clearance and liver extraction ratio from in vitro microsomal clearance data, highlighting the importance of drug plasma protein binding kinetics in addressing the under-prediction of hepatic clearance by the WSM.

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency's ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some-but not all-cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.

Prioritization of mixtures of neurotoxic chemicals for biomonitoring using high-throughput toxicokinetics and mixture toxicity modeling

Modern society continues to pollute the environment with larger quantities of chemicals that have also become more structurally and functionally diverse. Risk assessment of chemicals can hardly keep up with the sheer numbers that lead to complex mixtures of increasing chemical diversity including new chemicals, substitution products on top of still abundant legacy compounds. Fortunately, over the last years computational tools have helped us to identify and prioritize chemicals of concern. These include toxicokinetic models to predict exposure to chemicals as well as new approach methodologies such as in-vitro bioassays to address toxicodynamic effects. Combined, they allow for a prediction of mixtures and their respective effects and help overcome the lack of data we face for many chemicals. In this study we propose a high-throughput approach using experimental and predicted exposure, toxicokinetic and toxicodynamic data to simulate mixtures, to which a virtual population is exposed to and predict their mixture effects. The general workflow is adaptable for any type of toxicity, but we demonstrated its applicability with a case study on neurotoxicity. If no experimental data for neurotoxicity were available, we used baseline toxicity predictions as a surrogate. Baseline toxicity is the minimal toxicity any chemical has and might underestimate the true contribution to the mixture effect but many neurotoxicants are not by orders of magnitude more potent than baseline toxicity. Therefore, including baseline-toxic effects in mixture simulations yields a more realistic picture than excluding them in mixture simulations. This workflow did not only correctly identify and prioritize known chemicals of concern like benzothiazoles, organochlorine pesticides and plasticizers but we were also able to identify new potential neurotoxicants that we recommend to include in future biomonitoring studies and if found in humans, to also include in neurotoxicity screening.

Unravelling the pharmacokinetics of aflatoxin B1: In vitro determination of Michaelis–Menten constants, intrinsic clearance and the metabolic contribution of CYP1A2 and CYP3A4 in pooled human liver microsomes

Mycotoxins, fungal secondary metabolites, are ubiquitously present in food commodities. Acute exposure to high levels or chronic exposure to low levels has an impact on the human body. The phase I metabolism in the human liver, performed by cytochrome P450 (CYP450) enzymes, is accountable for more than 80% of the overall metabolism of exogenous and endogenous compounds. Mycotoxins are (partially) metabolized by CYP450 enzymes. In this study, in vitro research was performed on CYP450 probes and aflatoxin B1 (AFB1), a carcinogenic mycotoxin, to obtain pharmacokinetic data on AFB1, required for further experimental work. The CYP450 probes of choice were a CYP3A4 substrate, midazolam (MDZ) and a CYP1A2 substrate, phenacetin (PH) since these are the main metabolizing phase I enzymes of AFB1. Linearity experiments were performed on the three substrates indicating that linear conditions were achieved at a microsomal protein concentration and incubation time of 0.25 mg/ml and 5 min, 0.50 mg/ml and 20 min and 0.25 mg/ml and 5 min for MDZ, PH and AFB1, respectively. The K_m was determined in human liver microsomes and was estimated at 2.15 μM for MDZ, 40.0 μM for PH and 40.9 μM for AFB1. The associated V_max values were 956 pmol/(mg.min) (MDZ), 856 pmol/(mg.min) (PH) and 11,536 pmol/(mg.min) (AFB1). Recombinant CYP systems were used to determine CYP450-specific Michaelis–Menten values for AFB1, leading to a CYP3A4 K_m of 49.6 μM and an intersystem extrapolation factor (ISEF) corrected V_max of 43.6 pmol/min/pmol P450 and a CYP1A2 K_m of 58.2 μM and an ISEF corrected V_max of 283 pmol/min/pmol P450. An activity adjustment factor (AAF) was calculated to account for differences between microsome batches and was used as a correction factor in the determination of the human in vivo hepatic clearance for MDZ, PH and AFB1. The hepatic blood clearance corrected for the AAF CL_H,B,MDZ,AAF, CL_H,B,PH,AAF CL_{H,B,AFB1,AAF(CYP3A4)} and CL_{H,B,AFB1,AAF(CYP1A2)} were determined in HLM at 44.1 L/h, 21.7 L/h, 40.0 L/h and 38.5 L/h. Finally, inhibition assays in HLM showed that 45% of the AFB1 metabolism was performed by CYP3A4/3A5 enzymes and 49% by CYP1A2 enzymes.

Does Addition of Protein to Hepatocyte or Microsomal In Vitro Incubations Provide a Useful Improvement in In Vitro-In Vivo Extrapolation Predictability?

Accurate prediction of in vivo hepatic clearance is an essential part of successful and efficient drug development; however, many investigators have recognized that there are significant limitations in the predictability of clearance with a tendency for underprediction for primarily metabolized drugs. Here, we examine the impact of adding serum or albumin into hepatocyte and microsomal incubations on the predictability of in vivo hepatic clearance. The addition of protein into hepatocyte incubations has been reported to improve the predictability for high clearance (extraction ratio) drugs and highly protein-bound drugs. Analyzing published data for 60 different drugs and 97 experimental comparisons (with 17 drugs being investigated from two to seven) we confirmed the marked underprediction of clearance. However, we could not validate any relevant improved predictability within twofold by the addition of serum to hepatocyte incubations or albumin to microsomal incubations. This was the case when investigating all measurements, or when subdividing analyses by extraction ratio, degree of protein binding, Biopharmaceutics Drug Disposition Classification System class, examining Extended Clearance Classification System class 1B drugs only, or drug charge. Manipulating characteristics of small data sets of like compounds and adding scaling factors can appear to yield good predictability, but the carryover of these methods to alternate drug classes and different laboratories is not evident. Improvement in predictability of poorly soluble compounds is greater than that for soluble compounds, but not to a meaningful extent. Overall, we cannot confirm that protein addition improves in vitro-in vivo extrapolation predictability to any clinically meaningful degree when considering all drugs and different subsets. SIGNIFICANCE STATEMENT: The addition of protein into microsomal or hepatocyte incubations has been widely proposed to improve hepatic clearance predictions. To date, studies examining this phenomenon have not included appropriate negative controls where predictability is achieved without protein addition and have been conducted with small data sets of similar compounds that don't apply to alternate drug classes. Here, an extensive analysis of published data for 60 drugs and 97 experimental comparisons couldn't validate any relevant clinically improved clearance predictability with protein addition.

Variability in Human In Vitro Enzyme Kinetics

There are many factors which are known to cause variability in human in vitro enzyme kinetic data. Factors such as the source of enzyme and how it was prepared, the genetics and background of the donor, how the in vitro studies are designed, and how the data are analyzed contribute to variability in the resulting kinetic parameters. It is important to consider not only the factors which cause variability within an experiment, such as selection of a probe substrate, but also those that cause variability when comparing kinetic data across studies and laboratories. For example, the artificial nature of the microsomal lipid membrane and microenvironment in some recombinantly expressed enzymes, relative to those found in native tissue microsomes, has been shown to influence enzyme activity and thus can be a source of variability when comparing across the two different systems. All of these factors, and several others, are discussed in detail in the chapter below. In addition, approaches which can be used to visualize the uncertainty arising from the use of enzyme kinetic data within the context of predicting human pharmacokinetics are discussed.

Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma

Variability of the unbound fraction in plasma (f_u) between labs, methods and conditions is known to exist. Variability and uncertainty of this parameter influence predictions of the overall pharmacokinetics of drug candidates and might jeopardise safety in early clinical trials. Objectives of this study were to evaluate the variability of human in vitro f_u-estimates between labs for a range of different drugs, and to develop and validate an in silico f_u-prediction method and compare the results to the lab variability.A new in silico method with prediction accuracy (Q²) of 0.69 for log f_u was developed. The median and maximum prediction errors were 1.9- and 92-fold, respectively. Corresponding estimates for lab variability (ratio between max and min f_u for each compound) were 2.0- and 185-fold, respectively. Greater than 10-fold lab variability was found for 14 of 117 selected compounds.Comparisons demonstrate that in silico predictions were about as reliable as lab estimates when these have been generated during different conditions. Results propose that the new validated in silico prediction method is valuable not only for predictions at the drug design stage, but also for reducing uncertainties of f_u-estimations and improving safety of drug candidates entering the clinical phase.

High-throughput PBTK models for in vitro to in vivo extrapolation

INTRODUCTION

Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. High-throughput screening (HTS) for in vitro bioactivity, when used with high-throughput toxicokinetic (HTTK) data and models, allows characterization of these thousands of chemicals.

AREAS COVERED

This review covers generic physiologically based toxicokinetic (PBTK) models and high-throughput PBTK modeling for in vitro-in vivo extrapolation (IVIVE) of HTS data. We focus on 'httk', a public, open-source set of computational modeling tools and in vitro toxicokinetic (TK) data.

EXPERT OPINION

HTTK benefits chemical risk assessors with its ability to support rapid chemical screening/prioritization, perform IVIVE, and provide provisional TK modeling for large numbers of chemicals using only limited chemical-specific data. Although generic TK model design can increase prediction uncertainty, these models provide offsetting benefits by increasing model implementation accuracy. Also, public distribution of the models and data enhances reproducibility. For the httk package, the modular and open-source design can enable the tool to be used and continuously improved by a broad user community in support of the critical need for high-throughput chemical prioritization and rapid dose estimation to facilitate rapid hazard assessments.

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.

New framework for a non-animal approach adequately assures the safety of cosmetic ingredients - A case study on caffeine

This case study on the model substance caffeine demonstrates the viability of a 10-step read-across (RAX) framework in practice. New approach methodologies (NAM), including RAX and physiologically-based kinetic (PBK) modelling were used to assess the consumer safety of caffeine. Appropriate animal systemic toxicity data were used from the most relevant RAX analogue while assuming that no suitable animal toxicity data were available for caffeine. Based on structural similarities, three primary metabolites of the target chemical caffeine (theophylline, theobromine and paraxanthine) were selected as its most relevant analogues, to estimate a point of departure in order to support a next generation risk assessment (NGRA). On the basis of the pivotal mode of action (MOA) of caffeine and other methylxanthines, theophylline appeared to be the most potent and suitable analogue. A worst-case aggregate exposure assessment determined consumer exposure to caffeine from different sources, such as cosmetics and food/drinks. Using a PBK model to estimate human blood concentrations following exposure to caffeine, an acceptable Margin of Internal Exposure (MOIE) of 27-fold was derived on the basis of a RAX using theophylline animal data, which suggests that the NGRA approach for caffeine is sufficiently conservative to protect human health.

Successful and Unsuccessful Prediction of Human Hepatic Clearance for Lead Optimization

Development of new chemical entities is costly, time-consuming, and has a low success rate. Accurate prediction of pharmacokinetic properties is critical to progress compounds with favorable drug-like characteristics in lead optimization. Of particular importance is the prediction of hepatic clearance, which determines drug exposure and contributes to projection of dose, half-life, and bioavailability. The most commonly employed methodology to predict hepatic clearance is termed in vitro to in vivo extrapolation (IVIVE) that involves measuring drug metabolism in vitro, scaling-up this in vitro intrinsic clearance to a prediction of in vivo intrinsic clearance by reconciling the enzymatic content between the incubation and an average human liver, and applying a model of hepatic disposition to account for limitations of protein binding and blood flow to predict in vivo clearance. This manuscript reviews common in vitro techniques used to predict hepatic clearance as well as current challenges and recent theoretical advancements in IVIVE.

Usage of In Vitro Metabolism Data for Drug-Drug Interaction in Physiologically Based Pharmacokinetic Analysis Submissions to the US Food and Drug Administration

The key parameters necessary to predict drug-drug interactions (DDIs) are intrinsic clearance (CL_int ) and fractional contribution of the metabolizing enzyme toward total metabolism (f_m ). Herein, we summarize the accumulated knowledge from 53 approved new drug applications submitted to the Office of Clinical Pharmacology, US Food and Drug Administration, from 2016 to 2018 that contained physiologically based pharmacokinetic (PBPK) models to understand how in vitro data are used in PBPK models to assess drug metabolism and predict DDIs. For evaluation of CL_int and f_m , 29 and 20 new drug applications were included for evaluation, respectively. For CL_int , 86.2% of the PBPK models used modified values based on in vivo data with modifications ranging from -82.5% to 2752.5%. For f_m , 45.0% of the models used modified values with modifications ranging from -28% to 178.6%. When values for CL_int were used from in vitro testing without modification, the model resulted in up to a 14.3-fold overprediction of the area under the concentration-time curve of the substrate. When values for f_m from in vitro testing were used directly, the model resulted in up to a 2.9-fold underprediction of its DDI magnitude with an inducer, and up to a 1.7-fold overprediction of its DDI magnitude with an inhibitor. Our analyses suggested that the in vitro system usually provides a reasonable estimation of f_m when the drug metabolism by a given CYP pathway is more than 70% of the total clearance. In vitro experiments provide important information about basic PK properties of new drugs and can serve as a starting point for building a PBPK model. However, key PBPK parameters such as CL_int and f_m still need to be optimized based on in vivo data.

Consideration of the Unbound Drug Concentration in Enzyme Kinetics

The study of enzyme kinetics in drug metabolism involves assessment of rates of metabolism and inhibitory potencies over a suitable concentration range. In all but the very simplest in vitro system, these drug concentrations can be influenced by a variety of nonspecific binding reservoirs that can reduce the available concentration to the enzyme system(s) under investigation. As a consequence, the apparent kinetic parameters, such as K_m or K_i, that are derived can deviate from the true values. There are a number of sources of these nonspecific binding depots or barriers, including membrane permeation and partitioning, plasma or serum protein binding, and incubational binding. In the latter case, this includes binding to the assay apparatus as well as biological depots, depending on the characteristics of the in vitro matrix being used. Given the wide array of subcellular, cellular, and recombinant enzyme systems utilized in drug metabolism, each of these has different components which can influence the free drug concentration. The physicochemical properties of the test compound are also paramount in determining the influential factors in any deviation between true and apparent kinetic behavior. This chapter describes the underlying mechanisms determining the free drug concentration in vitro and how these factors can be accounted for in drug metabolism studies, illustrated with case studies from the literature.

Using Chemical Structure Information to Develop Predictive Models for In Vitro Toxicokinetic Parameters to Inform High-throughput Risk-assessment

The toxicokinetic (TK) parameters fraction of the chemical unbound to plasma proteins and metabolic clearance are critical for relating exposure and internal dose when building in vitro-based risk assessment models. However, experimental toxicokinetic studies have only been carried out on limited chemicals of environmental interest (~1000 chemicals with TK data relative to tens of thousands of chemicals of interest). This work evaluated the utility of chemical structure information to predict TK parameters in silico; development of cluster-based read-across and quantitative structure-activity relationship models of fraction unbound or fub (regression) and intrinsic clearance or Cl_int (classification and regression) using a dataset of 1487 chemicals; utilization of predicted TK parameters to estimate uncertainty in steady-state plasma concentration (C_ss); and subsequent in vitro-in vivo extrapolation analyses to derive bioactivity-exposure ratio (BER) plot to compare human oral equivalent doses and exposure predictions using androgen and estrogen receptor activity data for 233 chemicals as an example dataset. The results demonstrate that fub is structurally more predictable than Cl_int. The model with the highest observed performance for fub had an external test set RMSE/σ=0.62 and R²=0.61, for Cl_int classification had an external test set accuracy = 65.9%, and for intrinsic clearance regression had an external test set RMSE/σ=0.90 and R²=0.20. This relatively low performance is in part due to the large uncertainty in the underlying Cl_int data. We show that C_ss is relatively insensitive to uncertainty in Cl_int. The models were benchmarked against the ADMET Predictor software. Finally, the BER analysis allowed identification of 14 out of 136 chemicals for further risk assessment demonstrating the utility of these models in aiding risk-based chemical prioritization.

Application of the Extended Clearance Classification System (ECCS) in Drug Discovery and Development: Selection of Appropriate In Vitro Tools and Clearance Prediction

In vitro to in vivo extrapolation (IVIVE) to predict human hepatic clearance, including metabolism and transport, requires extensive experimental resources. In addition, there may be technical challenges to measure low clearance values. Therefore, prospective identification of rate-determining step(s) in hepatic clearance through application of the Extended Clearance Classification System (ECCS) could be beneficial for optimal compound characterization. IVIVE for hepatic intrinsic clearance (CLint,h) prediction is conducted for a set of 36 marketed drugs with low-to-high in vivo clearance, which are substrates of metabolic enzymes and active uptake transporters in the liver. The compounds were assigned to the ECCS classes, and CLint,h, estimated with HepatoPac (a micropatterned hepatocyte coculture system), was compared with values calculated based on suspended hepatocyte incubates. An apparent permeability threshold (apical to basal) of 50 nm/s in LLC-PK1 cells proved optimal for ECCS classification. A reasonable performance of the IVIVE for compounds across multiple classes using HepatoPac was achieved (with 2-3-fold error), except for substrates of uptake transporters (class 3b), for which scaling of uptake clearance using plated hepatocytes is more appropriate. Irrespective of the ECCS assignment, metabolic clearance can be estimated well using HepatoPac. The validation and approach elaborated in the present study can result in proposed decision trees for the selection of the optimal in vitro assays guided by ECCS class assignment, to support compound optimization and candidate selection. SIGNIFICANCE STATEMENT: Characterization of the rate-determining step(s) in hepatic elimination could be on the critical path of compound optimization during drug discovery. This study demonstrated that HepatoPac and plated hepatocytes are suitable tools for the estimation of metabolic and active uptake clearance, respectively, for a larger set of marketed drugs, supporting a comprehensive strategy to select optimal in vitro tools and to achieve Extended Clearance Classification System-dependent in vitro to in vivo extrapolation for human clearance prediction.

Estimating renal and hepatic clearance rates of organophosphate esters in humans: Impacts of intrinsic metabolism and binding affinity with plasma proteins

The renal and hepatic clearance rates of organophosphate esters (OPEs) in humans were estimated. Six OPEs and their corresponding diester metabolites (mOPEs) were quantified respectively in 30 paired human plasma and urine samples collected in Hengshui, Hebei province, China. The renal clearance rate (CL_renal) of triphenyl phosphate (TPHP), tris(chloroethyl) phosphate (TCEP) and tris(1,3-dichloro-isopropyl) phosphate (TDCIPP) was estimated to be 68.9, 50.9 and 33.3 mL/kg/day, respectively, while it was not calculated for other three OPEs due to the low detection frequencies in human samples. To estimate the clearance rates of the target OPEs, hepatic clearance rates (CL_h) of OPEs were extrapolated from their in vitro intrinsic clearance data in human liver microsomes (CL_HLM). The calculated CL_h values of TCEP and TDCIPP were comparable to their CL_renal, indicating that the in vitro extrapolation method was suitable for estimating the clearance rates of OPEs. The higher binding affinity of TDCIPP with plasma proteins could reduce its renal clearance. The estimated half-lives of Cl-OPEs in human were longer than those of the aryl- and alkyl-OPEs. This study provided a feasible in vitro method to predict the clearance and half-lives of OPEs in human, which is significant for their accurate health risk assessment.

Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization

High(er) throughput toxicokinetics (HTTK) encompasses in vitro measures of key determinants of chemical toxicokinetics and reverse dosimetry approaches for in vitro-in vivo extrapolation (IVIVE). With HTTK, the bioactivity identified by any in vitro assay can be converted to human equivalent doses and compared with chemical intake estimates. Biological variability in HTTK has been previously considered, but the relative impact of measurement uncertainty has not. Bayesian methods were developed to provide chemical-specific uncertainty estimates for 2 in vitro toxicokinetic parameters: unbound fraction in plasma (fup) and intrinsic hepatic clearance (Clint). New experimental measurements of fup and Clint are reported for 418 and 467 chemicals, respectively. These data raise the HTTK chemical coverage of the ToxCast Phase I and II libraries to 57%. Although the standard protocol for Clint was followed, a revised protocol for fup measured unbound chemical at 10%, 30%, and 100% of physiologic plasma protein concentrations, allowing estimation of protein binding affinity. This protocol reduced the occurrence of chemicals with fup too low to measure from 44% to 9.1%. Uncertainty in fup was also reduced, with the median coefficient of variation dropping from 0.4 to 0.1. Monte Carlo simulation was used to propagate both measurement uncertainty and biological variability into IVIVE. The uncertainty propagation techniques used here also allow incorporation of other sources of uncertainty such as in silico predictors of HTTK parameters. These methods have the potential to inform risk-based prioritization based on the relationship between in vitro bioactivities and exposures.

Quantitative Prediction of Human Hepatic Clearance for P450 and Non-P450 Substrates from In Vivo Monkey Pharmacokinetics Study and In Vitro Metabolic Stability Tests Using Hepatocytes

Accurate prediction of human pharmacokinetics for drugs remains challenging, especially for non-cytochrome P450 (P450) substrates. Hepatocytes might be suitable for predicting hepatic intrinsic clearance (CL_int) of new chemical entities, because they can be applied to various compounds regardless of the metabolic enzymes. However, it was reported that hepatic CL_int is underestimated in hepatocytes. The purpose of the present study was to confirm the predictability of human hepatic clearance for P450 and non-P450 substrates in hepatocytes and the utility of animal scaling factors for the prediction using hepatocytes. CL_int values for 30 substrates of P450, UDP-glucuronosyltransferase, flavin-containing monooxygenase, esterases, reductases, and aldehyde oxidase in human microsomes, human S9 and human, rat, and monkey hepatocytes were estimated. Hepatocytes were incubated in serum of each species. Furthermore, CL_int values in human hepatocytes were corrected with empirical, monkey, and rat scaling factors. CL_int values in hepatocytes for most compounds were underestimated compared to observed values regardless of the metabolic enzyme, and the predictability was improved by using the scaling factors. The prediction using human hepatocytes corrected with monkey scaling factor showed the highest predictability for both P450 and non-P450 substrates among the predictions using liver microsomes, liver S9, and hepatocytes with or without scaling factors. CL_int values by this method for 80% and 90% of all compounds were within 2- and 3-fold of observed values, respectively. This method is accurate and useful for estimating new chemical entities, with no need to care about cofactors, localization of metabolic enzymes, or protein binding in plasma and incubation mixture.

The Presence of a Transporter-Induced Protein Binding Shift: A New Explanation for Protein-Facilitated Uptake and Improvement for In Vitro-In Vivo Extrapolation

Accurately predicting hepatic clearance is an integral part of the drug-development process, and yet current in vitro to in vivo (IVIVE) extrapolation methods yield poor predictions, particularly for highly protein-bound transporter substrates. Explanations for error include inaccuracies in protein-binding measurements and the lack of recognition of protein-facilitated uptake, where both unbound and bound drug may be cleared, violating the principles of the widely accepted free drug theory. A new explanation for protein-facilitated uptake is proposed here, called a transporter-induced protein binding shift High-affinity binding to cell-membrane proteins may change the equilibrium of the nonspecific binding between drugs and plasma proteins, leading to greater cellular uptake and clearance than currently predicted. The uptake of two lower protein-binding organic anion transporting polypeptide substrates (pravastatin and rosuvastatin) and two higher binding substrates (atorvastatin and pitavastatin) were measured in rat hepatocytes in incubations with protein-free buffer versus 100% plasma. Decreased unbound K _m values and increased intrinsic clearance values were seen in the plasma incubations for the highly bound compounds, supporting the new hypothesis and mitigating the IVIVE underprediction previously seen for highly bound transporter substrates.

An examination of protein binding and protein-facilitated uptake relating to in vitro-in vivo extrapolation

As explained by the free drug theory, the unbound fraction of drug has long been thought to drive the efficacy of a molecule. Thus, the fraction unbound term, or f_u, appears in equations for fundamental pharmacokinetic parameters such as clearance, and is used when attempting in vitro to in vivo extrapolation (IVIVE). In recent years though, it has been noted that IVIVE does not always yield accurate predictions, and that some highly protein bound ligands have more efficient uptake than can be explained by their unbound fractions. This review explores the evolution of f_u terms included when implementing IVIVE, the concept of protein-facilitated uptake, and the mechanisms that have been proposed to account for facilitated uptake.

Development and Characterization of a Human Hepatocyte Low Intrinsic Clearance Assay for Use in Drug Discovery

Progression of new chemical entities is a multiparametric process involving a balance of potency; absorption, distribution, metabolism, and excretion; and safety properties. To accurately predict human pharmacokinetics and estimate human efficacious dose, the use of in vitro measures of clearance is often essential. Low metabolic clearance is often targeted to facilitate in vivo exposure and achieve appropriate half-life. Suspension primary human hepatocytes (PHHs) have been successfully used in predictions of clearance. However, incubation times are limited, hindering the limit of quantification. The aims herein were to evaluate the ability of a novel PHH media supplement, HepExtend, in order to maintain cell function, increase culture times, and define the clearance of stable compounds. Cell activity was analyzed with a range of cytochrome P450 (P450) and UDP-glucuronosyltransferase (UGT) substrates, and the mRNA expression of drug disposition and toxicity marker genes was determined. HepExtend and Geltrex were essential to maintain cell activity and viability for 5 days (N = 3 donors). In comparison with CM4000 ± Geltrex, HepExtend + Geltrex displayed a higher level of gene expression on day 1, particularly for the P450s, nuclear receptors, and UGTs. The novel medium, HepExtend + Geltrex, was robust and reproducible in generating statistically significant intrinsic clearance values at 0.1 µl/min/10⁶ cells over a 30-hour period (P < 0.05), which was lower than previously demonstrated. Following regression correction, human hepatic in vivo clearance was predicted within 3-fold for 83% of compounds tested for three human donors, with an average fold error of 2.2. The novel PHH medium, HepExtend, with matrix overlay offers significant improvement in determining compounds with low intrinsic clearance when compared with alternative approaches.

Evaluating In Vitro-In Vivo Extrapolation of Toxicokinetics

Prioritizing the risk posed by thousands of chemicals potentially present in the environment requires exposure, toxicity, and toxicokinetic (TK) data, which are often unavailable. Relatively high throughput, in vitro TK (HTTK) assays and in vitro-to-in vivo extrapolation (IVIVE) methods have been developed to predict TK, but most of the in vivo TK data available to benchmark these methods are from pharmaceuticals. Here we report on new, in vivo rat TK experiments for 26 non-pharmaceutical chemicals with environmental relevance. Both intravenous and oral dosing were used to calculate bioavailability. These chemicals, and an additional 19 chemicals (including some pharmaceuticals) from previously published in vivo rat studies, were systematically analyzed to estimate in vivo TK parameters (e.g., volume of distribution [Vd], elimination rate). For each of the chemicals, rat-specific HTTK data were available and key TK predictions were examined: oral bioavailability, clearance, Vd, and uncertainty. For the non-pharmaceutical chemicals, predictions for bioavailability were not effective. While no pharmaceutical was absorbed at less than 10%, the fraction bioavailable for non-pharmaceutical chemicals was as low as 0.3%. Total clearance was generally more under-estimated for nonpharmaceuticals and Vd methods calibrated to pharmaceuticals may not be appropriate for other chemicals. However, the steady-state, peak, and time-integrated plasma concentrations of nonpharmaceuticals were predicted with reasonable accuracy. The plasma concentration predictions improved when experimental measurements of bioavailability were incorporated. In summary, HTTK and IVIVE methods are adequately robust to be applied to high throughput in vitro toxicity screening data of environmentally relevant chemicals for prioritizing based on human health risks.

Interconnected Microphysiological Systems for Quantitative Biology and Pharmacology Studies

Microphysiological systems (MPSs) are in vitro models that capture facets of in vivo organ function through use of specialized culture microenvironments, including 3D matrices and microperfusion. Here, we report an approach to co-culture multiple different MPSs linked together physiologically on re-useable, open-system microfluidic platforms that are compatible with the quantitative study of a range of compounds, including lipophilic drugs. We describe three different platform designs – “4-way”, “7-way”, and “10-way” – each accommodating a mixing chamber and up to 4, 7, or 10 MPSs. Platforms accommodate multiple different MPS flow configurations, each with internal re-circulation to enhance molecular exchange, and feature on-board pneumatically-driven pumps with independently programmable flow rates to provide precise control over both intra- and inter-MPS flow partitioning and drug distribution. We first developed a 4-MPS system, showing accurate prediction of secreted liver protein distribution and 2-week maintenance of phenotypic markers. We then developed 7-MPS and 10-MPS platforms, demonstrating reliable, robust operation and maintenance of MPS phenotypic function for 3 weeks (7-way) and 4 weeks (10-way) of continuous interaction, as well as PK analysis of diclofenac metabolism. This study illustrates several generalizable design and operational principles for implementing multi-MPS “physiome-on-a-chip” approaches in drug discovery.

Evaluation and calibration of high-throughput predictions of chemical distribution to tissues

Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (Kp) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat Kp measured by in vivo experiments for 143 compounds. Initially, predicted Kp were significantly larger than measured Kp for many lipophilic compounds (log10 octanol:water partition coefficient > 3). Hence the approach for predicting Kp was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 Kp across 23 compounds with only 3 Kp worsening by more than a factor of 10. The vast majority (92%) of Kp were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log10-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (Vss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R2 of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.

An Intuitive Approach for Predicting Potential Human Health Risk with the Tox21 10k Library

In vitro-in vivo extrapolation (IVIVE) analyses translating high-throughput screening (HTS) data to human relevance have been limited. This study represents the first report applying IVIVE approaches and exposure comparisons using the entirety of the Tox21 federal collaboration chemical screening data, incorporating assay response efficacy and quality of concentration-response fits, and providing quantitative anchoring to first address the likelihood of human in vivo interactions with Tox21 compounds. This likelihood was assessed using a maximum blood concentration to in vitro response ratio approach (C_max/AC₅₀), analogous to decision-making methods for clinical drug-drug interactions. Fraction unbound in plasma (f_up) and intrinsic hepatic clearance (CL_int) parameters were estimated in silico and incorporated in a three-compartment toxicokinetic (TK) model to first predict C_max for in vivo corroboration using therapeutic scenarios. Toward lower exposure scenarios, 36 compounds of 3925 unique chemicals with curated activity in the HTS data using high-quality dose-response model fits and ≥40% efficacy gave "possible" human in vivo interaction likelihoods lower than median human exposures predicted in the United States Environmental Protection Agency's ExpoCast program. A publicly available web application has been designed to provide all Tox21-ToxCast dose-likelihood predictions. Overall, this approach provides an intuitive framework to relate in vitro toxicology data rapidly and quantitatively to exposures using either in vitro or in silico derived TK parameters and can be thought of as an important step toward estimating plausible biological interactions in a high-throughput risk-assessment framework.

httk: R Package for High-Throughput Toxicokinetics

Thousands of chemicals have been profiled by high-throughput screening programs such as ToxCast and Tox21; these chemicals are tested in part because most of them have limited or no data on hazard, exposure, or toxicokinetics. Toxicokinetic models aid in predicting tissue concentrations resulting from chemical exposure, and a "reverse dosimetry" approach can be used to predict exposure doses sufficient to cause tissue concentrations that have been identified as bioactive by high-throughput screening. We have created four toxicokinetic models within the R software package httk. These models are designed to be parameterized using high-throughput in vitro data (plasma protein binding and hepatic clearance), as well as structure-derived physicochemical properties and species-specific physiological data. The package contains tools for Monte Carlo sampling and reverse dosimetry along with functions for the analysis of concentration vs. time simulations. The package can currently use human in vitro data to make predictions for 553 chemicals in humans, rats, mice, dogs, and rabbits, including 94 pharmaceuticals and 415 ToxCast chemicals. For 67 of these chemicals, the package includes rat-specific in vitro data. This package is structured to be augmented with additional chemical data as they become available. Package httk enables the inclusion of toxicokinetics in the statistical analysis of chemicals undergoing high-throughput screening.

httk: R Package for High-Throughput Toxicokinetics

Thousands of chemicals have been profiled by high-throughput screening programs such as ToxCast and Tox21; these chemicals are tested in part because most of them have limited or no data on hazard, exposure, or toxicokinetics. Toxicokinetic models aid in predicting tissue concentrations resulting from chemical exposure, and a "reverse dosimetry" approach can be used to predict exposure doses sufficient to cause tissue concentrations that have been identified as bioactive by high-throughput screening. We have created four toxicokinetic models within the R software package httk. These models are designed to be parameterized using high-throughput in vitro data (plasma protein binding and hepatic clearance), as well as structure-derived physicochemical properties and species-specific physiological data. The package contains tools for Monte Carlo sampling and reverse dosimetry along with functions for the analysis of concentration vs. time simulations. The package can currently use human in vitro data to make predictions for 553 chemicals in humans, rats, mice, dogs, and rabbits, including 94 pharmaceuticals and 415 ToxCast chemicals. For 67 of these chemicals, the package includes rat-specific in vitro data. This package is structured to be augmented with additional chemical data as they become available. Package httk enables the inclusion of toxicokinetics in the statistical analysis of chemicals undergoing high-throughput screening.

3D engineered In vitro hepatospheroids for studying drug toxicity and metabolism

Drug toxicity is one of the reasons for late stage drug attrition, because of hepatotoxicity. Various in vitro liver models like primary human hepatocytes, immortalized human hepatic cell lines, liver slices and microsomes have been used; but limited by viability, hepatic gene expression and function. The 3D-engineered construct of hepatocyte-like-cells (HLCs) differentiated from stem cells, may provide a limitless source of hepatocytes with improved reproducibility. Towards this end, we used hepatospheroids (diameter=50-80μm) differentiated from human-umbilical-cord-mesenchymal stem cells (hUC-MSCs) on 3D scaffold GEVAC (Gelatin-vinyl-acetate-copolymer) as in vitro model for studying drug metabolism/toxicity. Our data demonstrated that hUC-MSCs-derived-hepatospheroids cultured on GEVAC expressed significantly higher drug-metabolizing enzymes (CYPs) both at mRNA and activity level compared to 2D culture, using HR-LC/MS. We further showed that hepatospheroids convert phenacetin (by CYP1A2) and testosterone (by CYP3A4) to their human-specific metabolites acetaminophen and 6β-hydroxytestosterone with a predictive clearance rate of 0.011ml/h/10⁶ cells and 0.021ml/h/10⁶ cells respectively, according to first-order kinetics. Hepatotoxicity was confirmed by exposing hepatospheroids to ethanol and acetaminophen; ROS generation, cell viability, cytoskeleton structure, elevation of liver function enzymes, i.e. AST and ALT, was analyzed. To the best of our knowledge, this is the first report to use hUC-MSCs-derived-hepatospheroids on GEVAC as in vitro model for drug metabolism/toxicity study; which can replace the conventional 2D-models used in drug development.

Quantitative Assessment of Population Variability in Hepatic Drug Metabolism Using a Perfused Three-Dimensional Human Liver Microphysiological System

In this work, we first describe the population variability in hepatic drug metabolism using cryopreserved hepatocytes from five different donors cultured in a perfused three-dimensional human liver microphysiological system, and then show how the resulting data can be integrated with a modeling and simulation framework to accomplish in vitro–in vivo translation. For each donor, metabolic depletion profiles of six compounds (phenacetin, diclofenac, lidocaine, ibuprofen, propranolol, and prednisolone) were measured, along with metabolite formation, mRNA levels of 90 metabolism-related genes, and markers of functional viability [lactate dehydrogenase (LDH) release, albumin, and urea production]. Drug depletion data were analyzed with mixed-effects modeling. Substantial interdonor variability was observed with respect to gene expression levels, drug metabolism, and other measured hepatocyte functions. Specifically, interdonor variability in intrinsic metabolic clearance ranged from 24.1% for phenacetin to 66.8% for propranolol (expressed as coefficient of variation). Albumin, urea, LDH, and cytochrome P450 mRNA levels were identified as significant predictors of in vitro metabolic clearance. Predicted clearance values from the liver microphysiological system were correlated with the observed in vivo values. A population physiologically based pharmacokinetic model was developed for lidocaine to illustrate the translation of the in vitro output to the observed pharmacokinetic variability in vivo. Stochastic simulations with this model successfully predicted the observed clinical concentration-time profiles and the associated population variability. This is the first study of population variability in drug metabolism in the context of a microphysiological system and has important implications for the use of these systems during the drug development process.

Development of Caco-2 cells co-expressing CYP3A4 and NADPH-cytochrome P450 reductase using a human artificial chromosome for the prediction of intestinal extraction ratio of CYP3A4 substrates

The Caco-2 cells co-expressing cytochrome P450 (CYP) 3A4 and NADPH-cytochrome P450 reductase (CPR) were developed using a human artificial chromosome (HAC) vector. The CYP3A4 and CPR genes were cloned into the HAC vector in CHO cells using the Cre-loxP system, and the microcell-mediated chromosome transfer technique was used to transfer the CYP3A4-CPR-HAC vector to Caco-2 cells. After seeding onto semipermeable culture inserts, the CYP3A4-CPR-HAC/Caco-2 cells were found to form tight monolayers, similar to the parental cells, as demonstrated by the high transepithelial electrical resistance (TEER) value and comparable permeability of non-CYP3A4 substrates between parent and CYP3A4-CPR-HAC/Caco-2 cell monolayers. The metabolic activity of CYP3A4 (midazolam 1'-hydroxylase activity) in the CYP3A4-CPR-HAC/Caco-2 cells was constant from 22 to 35 passages, indicating that HAC vectors conferred sufficient and sustained CYP3A4 activity to CYP3A4-CPR-HAC/Caco-2 cells. The strong relationship between the metabolic extraction ratios (ER) obtained from the CYP3A4-CPR-HAC/Caco-2 cells and calculated intestinal extraction ratios in humans (Eg) from reported intestinal availability (Fg) was found for 17 substrates of CYP3A4 (r² = 0.84). The present study suggests that the CYP3A4-CPR-HAC/Caco-2 cell monolayer can serve as an in vitro tool that facilitates the prediction of intestinal extraction ratio (or availability) in humans.

Validation of Early Human Dose Prediction: A Key Metric for Compound Progression in Drug Discovery

Human dose prediction is increasingly recognized as an important parameter in Drug Discovery. Validation of a method using only in vitro and predicted parameters incorporated into a PK model was undertaken by predicting human dose and free Cmax for a number of marketed drugs and AZ Development compounds. Doses were compared to those most relevant to marketed drugs or to clinically administered doses of AZ compounds normalized either to predicted Cmin or Cmax values. Average (AFE) and absolute average (AAFE) fold-error analysis showed that best predictions were obtained using a QSAR model as the source of Vss, with Fabs set to 1 for acids and 0.5 for all other ion classes; for clearance prediction no binding correction to the well stirred model (WSM) was used for bases, while it was set to Fup/Fup(0.5) for all other ion classes. Using this combination of methods, predicted doses for 45 to 68% of the Cmin- and Cmax-normalized and marketed drug data sets were within 3-fold of the observed values, while 82 to 92% of these data sets were within 10-fold. This method for early human dose prediction is able to rank, identify, and flag risks or optimization opportunities for future development compounds within 10 days of first synthesis.

Upcyte Human Hepatocytes: a Potent In Vitro Tool for the Prediction of Hepatic Clearance of Metabolically Stable Compounds

In vitro models based on primary human hepatocytes (PHH) have been advanced for clearance (CL) prediction of metabolically stable compounds, representing state-of-the-art assay systems for drug discovery and development. Yet, limited cell availability and large interindividual variability of metabolic profiles remain shortcomings of PHH. Upcyte human hepatocytes (UHH) represent a novel hepatic cell system derived from PHH, exhibiting proliferative capacity for approximately 35 population doublings. UHH from three donors were evaluated during culture for up to 18 days, investigating relative mRNA expression and in situ enzyme activity of cytochrome P450s (P450s), UDP-glucuronosyltransferases, and sulfotransferases. Furthermore, UHH were used for predicting hepatic CL of 21 marketed low to intermediate CL drugs. In a typical experiment, expansion from 3.9 × 10(6) up to 8.5 × 10(7) cells was achieved during subculture. When maintained at confluence, transcripts of major P450s were expressed at donor-specific levels with sustained activities for the majority of isoforms, showing generally low CYP1A2 and high CYP2B6 activity levels. For donor 151-03, CL prediction based on depletion experiments resulted in an average fold error of 2.0, and 80% of compounds being predicted within twofold to in vivo CL for a subset of 10 low CL drugs. UHH showed sustained and consistent activity of drug-metabolizing enzymes (DME), resulting in highly reproducible CL prediction performance. In conclusion, UHH show promising potential as alternative to PHH for standardized in vitro applications in discovery research based on their stable, hepatocyte-like DME phenotype and virtually unlimited cell availability.

On the accuracy of calculation of the mean residence time of drug in the body and its volumes of distribution based on the assumption of central elimination

1. The steady state and terminal volumes of distribution, as well as the mean residence time of drug in the body (Vss, Vβ, and MRT) are the common pharmacokinetic parameters calculated using the drug plasma concentration-time profile (Cp(t)) following intravenous (iv bolus or constant rate infusion) drug administration. 2. These traditional calculations are valid for the linear pharmacokinetic system with central elimination (i.e. elimination rate being proportional to drug concentration in plasma). The assumption of central elimination is not valid in general, so that the accuracy of the traditional calculation of these parameters is uncertain. 3. The comparison of Vss, Vβ, and MRT calculated by the derived exact equations and by the commonly used ones was made considering a physiological model. It turned out that the difference between the exact and simplified calculations does not exceed 2%. 4. Thus the calculations of Vss, Vβ, and MRT, which are based on the assumption of central elimination, may be considered as quite accurate. Consequently it can be used as the standard for comparisons with kinetic and in silico models.

Toxicokinetic Triage for Environmental Chemicals

Toxicokinetic (TK) models link administered doses to plasma, blood, and tissue concentrations. High-throughput TK (HTTK) performs in vitro to in vivo extrapolation to predict TK from rapid in vitro measurements and chemical structure-based properties. A significant toxicological application of HTTK has been "reverse dosimetry," in which bioactive concentrations from in vitro screening studies are converted into in vivo doses (mg/kg BW/day). These doses are predicted to produce steady-state plasma concentrations that are equivalent to in vitro bioactive concentrations. In this study, we evaluate the impact of the approximations and assumptions necessary for reverse dosimetry and develop methods to determine whether HTTK tools are appropriate or may lead to false conclusions for a particular chemical. Based on literature in vivo data for 87 chemicals, we identified specific properties (eg, in vitro HTTK data, physico-chemical descriptors, and predicted transporter affinities) that correlate with poor HTTK predictive ability. For 271 chemicals we developed a generic HT physiologically based TK (HTPBTK) model that predicts non-steady-state chemical concentration time-courses for a variety of exposure scenarios. We used this HTPBTK model to find that assumptions previously used for reverse dosimetry are usually appropriate, except most notably for highly bioaccumulative compounds. For the thousands of man-made chemicals in the environment that currently have no TK data, we propose a 4-element framework for chemical TK triage that can group chemicals into 7 different categories associated with varying levels of confidence in HTTK predictions. For 349 chemicals with literature HTTK data, we differentiated those chemicals for which HTTK approaches are likely to be sufficient, from those that may require additional data.

Investigation of omeprazole and phenacetin first-pass metabolism in humans using a microscale bioreactor and pharmacokinetic models

A new in vitro microfluidic platform (integrated insert dynamic microfluidic platform, IIDMP) allowing the co-culture of intestinal Caco-2 TC7 cells and of human primary hepatocytes was used to test the absorption and first-pass metabolism of two drugs: phenacetin and omeprazole. The metabolism of these drugs by CYP1A2, CYP2C19 and CYP3A4 was evaluated by the calculation of bioavailabilities and of intrinsic clearances using a pharmacokinetic (PK) model. To demonstrate the usefulness of the device and of the PK model, predictions were compared with in vitro and in vivo results from the literature. Based on the IIDMP experiments, hepatic in vivo clearances of phenacetin and omeprazole in the IIDMP were predicted to be 3.10 ± 0.36 and 1.46 ± 0.25 ml/min/kg body weight, respectively. This appeared lower than the in vivo observed data with values ranging between 11.9-19.6 and 5.8-7.5 ml/min/kg body weight, respectively. Then the calculated hepatic and intestinal clearances led to predicting an oral bioavailability of 0.85 and 0.77 for phenacetin and omeprazole versus 0.92 and 0.78 using separate data from the simple monoculture of Caco-2 TC7 cells and hepatocytes in Petri dishes. When compared with the in vivo data, the results of oral bioavailability were overestimated (0.37 and 0.71, respectively). The feasibility of co-culture in a device allowing the integration of intestinal absorption, intestinal metabolism and hepatic metabolism in a single model was demonstrated. Nevertheless, further experiments with other drugs are needed to extend knowledge of the device to predict oral bioavailability and intestinal first-pass metabolism.

Analysis of the impact of controlled release formulations on oral drug absorption, gut wall metabolism and relative bioavailability of CYP3A substrates using a physiologically-based pharmacokinetic model

Controlled release (CR) formulations are usually designed to achieve similar exposure (AUC) levels as the marketed immediate release (IR) formulation. However, the AUC is often lower following CR compared to IR formulations. There are a few exceptions when the CR formulations have shown higher AUC. This study investigated the impact of CR formulations on oral drug absorption and CYP3A4-mediated gut wall metabolism. A review of the current literature on relative bioavailability (Frel) between CR and IR formulations of CYP3A substrates was conducted. This was followed by a systematic analysis to assess the impact of the release characteristics and the drug-specific factors (including metabolism and permeability) on oral bioavailability employing a physiologically-based pharmacokinetic (PBPK) modelling and simulation approach. From the literature review, only three CYP3A4 substrates showed higher Frel when formulated as CR. Several scenarios were investigated using the PBPK approach; in most of them, the oral absorption of CR formulations was lower as compared to the IR formulations. However, for highly permeable compounds that were CYP3A4 substrates the reduction in absorption was compensated by an increase in the fraction that escapes from first pass metabolism in the gut wall (FG), where the magnitude was dependent on CYP3A4 affinity. The systematic simulations of various interplays between different parameters demonstrated that BCS class 1 highly-cleared CYP3A4 substrates can display up to 220% higher relative bioavailability when formulated as CR compared to IR, in agreement with the observed data collected from the literature. The results and methodology of this study can be employed during the formulation development process in order to optimize drug absorption, especially for CYP3A4 substrates.