Integrating in vitro testing and physiologically-based pharmacokinetic (PBPK) modelling for chemical liver toxicity assessment—A case study of troglitazone

Journal Pre-proof Integrating In Vitro Testing and Physiologically-Based Pharmacokinetic (PBPK) Modelling for Chemical Liver Toxicity Assessment – a Case Study of Troglitazone Lin Yu, Hequn Li, Chi Zhang, Qiang Zhang, Jiabin Guo, Jin Li, Haitao Yuan, Lizhong Li, Paul Carmichael, Shuangqing Peng

PII:

S1382-6689(19)30170-X

DOI:

https://doi.org/10.1016/j.etap.2019.103296

Reference:

ENVTOX 103296

To appear in:

Environmental Toxicology and Pharmacology

Received Date:

12 September 2019

Revised Date:

30 October 2019

Accepted Date:

31 October 2019

Please cite this article as: Yu L, Li H, Zhang C, Zhang Q, Guo J, Li J, Yuan H, Li L, Carmichael P, Peng S, Integrating In Vitro Testing and Physiologically-Based Pharmacokinetic (PBPK) Modelling for Chemical Liver Toxicity Assessment – a Case Study of Troglitazone, Environmental Toxicology and Pharmacology (2019), doi: https://doi.org/10.1016/j.etap.2019.103296

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Integrating In Vitro Testing and Physiologically-Based Pharmacokinetic (PBPK) Modelling for Chemical Liver Toxicity Assessment – a Case Study of Troglitazone

Lin Yu1,2, Hequn Li3, Chi Zhang1,2, Qiang Zhang4, Jiabin Guo2, Jin Li3, Haitao Yuan2, Lizhong Li2, Paul Carmichael3, Shuangqing Peng2, * 1

Academy of Military Medicine, Academy of Military Sciences, 27 Taiping Road, Beijing 100850, PR China Institute of Disease Control and Prevention, People’s Liberation Army, 20 Dongda

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Street, Beijing 100071, PR China 3

Unilever Safety and Environmental Assurance Center, Colworth Science Park,

Department of Environmental Health, Rollins School of Public Health, Emory

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University, Atlanta, GA 30322, USA

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Sharnbrook, Bedfordshire MK44 1LQ, UK

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*To whom correspondence should be addressed. E-mail: [email protected]

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Highlights: 

A PBPK model was developed to predicting pharmacokinetics of troglitazone in human.

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Mitochondria-mediated toxicity endpoints were used to derive BMDLs.

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The performance of the animal-free approach was preliminarily evaluated in human.

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The BMDLs based Cmax metric matched well with clinical hepatotoxic context.

Abstract

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In vitro to in vivo extrapolation (IVIVE) for next-generation risk assessment (NGRA) of chemicals requires computational modeling and faces unique challenges. Using mitochondriarelated toxicity data of troglitazone (TGZ), a prototype drug known for liver toxicity, from

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HepaRG, HepG2, HC-04, and primary human hepatocytes, we explored inherent uncertainties

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in IVIVE, including cell models, cellular response endpoints, and dose metrics. A human population physiologically-based pharmacokinetic (PBPK) model for TGZ was developed to

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predict in vivo doses from in vitro point-of-departure (POD) concentrations. Compared to the 200-800 mg/d dose range of TGZ where liver injury was observed clinically, the predicted

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POD doses for the mean and top one percentile of the PBPK population were 28-372 and 15178 mg/d respectively based on Cmax dosimetry, and 185-2552 and 83-1010 mg/d respectively

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based on AUC. In conclusion, although with many uncertainties, integrating in vitro assays and

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PBPK modeling is promising in informing liver toxicity-inducing TGZ doses.

Keywords: troglitazone, PBPK modeling, reverse dosimetry, PODs, toxicity testing alternatives

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1. Introduction To set “safe” levels of chemical exposure for humans, an enormous number of experimental animals (more than 100 million and worth €2 billion) are currently used in toxicological studies every year (Hartung, 2009). However, this testing method is costly and time-consuming and the relevance and accuracy of extrapolating findings in animal studies to human health risk has been questioned (Hartung, 2008). In particular, traditional animal-based testing has not utilized many of the advances in toxicology regarding the mechanisms of chemical perturbation of

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biological systems. Using animals for toxicity testing nowadays is therefore facing enormous challenges for scientific, ethical, economic and legislative constraints. As a result, toxicity testing of chemicals is evolving towards innovative, animal-free, in vitro and in silico

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approaches (Thiel et al., 2017).

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In vitro to in vivo extrapolation (IVIVE) is an integrated process of translating in vitro

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toxicity data to in vivo health risk predictions, containing both toxicokinetic and toxicodynamic extrapolations. The IVIVE process faces many challenges and uncertainties, requiring a number of in silico approaches to bridge the data gaps (NRC, 2017; Zhang et al., 2018). To this

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end, physiologically-based pharmacokinetic/toxicokinetic (PBPK/PBTK) models are especially useful as “bottom-up” tools that can quantitatively characterize the dynamic changes

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in the concentrations of a chemical and its metabolite in plasma and organs by considering the

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chemical’s physiochemical properties, the absorption, distribution, metabolism and excretion (ADME) characteristics and related physiological processes (Rowland et al., 2011). While they are commonly used in a forward manner to predict plasma/tissue dosimetry given an external exposure, PBPK models are also used in a “reverse” manner to predict, from a tissue concentration, the corresponding external dose levels in a given exposure scenario. This reverse dosimetry approach can be applied in the setting of IVIVE, where the concentrations of the test

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chemical in a cell assay causing a predefined in vitro points-of-departure (PODs) are used to predict the in vivo exposure levels that can result in tissue or plasma concentrations similar to the in vitro POD concentration (Louisse et al., 2015). The predicted exposure levels can then be compared with the expected or actual exposure levels in humans, if available, to assess margin of safety.

Although still in its infancy, a number of toxicokinetic IVIVE studies have been

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published integrating in vitro testing and extrapolation modeling to predict the risks of organ toxicity induced by various chemicals. For instance, the developmental toxicity caused by tebuconazole (Li et al., 2017), glycol ethers (Louisse et al., 2010), phenols (Strikwold et al.,

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2017), retinoid (Strikwold et al., 2013), and all-trans-retinoic acid (Louisse et al., 2015) were predicted for human and animals based on in vitro toxicity data and in silico kinetic modelling.

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The kidney toxicity induced by aristolochic acid I (Abdullah et al., 2016), liver injury caused

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by hepatotoxicant azathioprine (Thiel et al., 2017), and reproductive toxicity (anti-androgenic effect) of a diverse group of chemicals (Dent et al., 2018) were also predicted with the IVIVE approach. Despite these progresses, which have mainly used one cellular biomarker to estimate

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toxicity, more studies are required to gain further experience and knowledge to understand the uncertainties involved in predicting in vivo toxicity in humans before the in vitro and in silico

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approach could be reliably put into practice for human health risk assessment for a broader

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range of compounds. These uncertainties include selection of cells, dosimetry, selection of cellular biomarkers and time of observation, POD levels, “averageness” of selected cell models and inter-individual variability, etc (Zhang et al., 2018). One particular challenge is the determination of an in vitro POD which can correspond to the in vivo POD for a given apical endpoint. Traditional approaches have used NOEL, LOEL, and even IC50/EC50 to define the cutoff values. More recently benchmark dose modelling (BMD) has been used more frequently

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as it considers the entire concentration- or dose-response curve and the uncertainty inherent to the experimental data (Filipsson et al., 2003). Since future in vitro assays will be designed around the toxicity pathways a test chemical perturbs, identifying the appropriate POD for the involved toxicity pathways will play an important role in the success of the new approach method (NRC, 2007). In addition, which in vitro concentration metrics to use, such as maximal nominal concentration or area under the curve (AUC), both of which describe some aspects of the cellular exposure to the test chemical, is important, too (Groothuis et al., 2015). In this

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study, we aimed to use a prototype compound, troglitazone (TGZ), a drug known to induce liver toxicity in humans, to explore some of these in vitro assay parameters and understand how in vitro assays, combined with PBPK modelling-based reverse dosimetry, can be better

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used through reducing the uncertainties to improve the IVIVE approach.

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TGZ was the first thiazolidinediones (TZDs) used to treat type 2 diabetes by improving

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insulin resistance (Plosker and Faulds, 1999). It was withdrawn from the market due to a significant increase in the risk of hepatotoxicity developed after 3 months of use with no additional clinical benefit compared to other available TZDs (Babai et al., 2018; Kung and

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Henry, 2012). While multiple mechanisms have been proposed for the hepatotoxicity of TGZ (Yokoi, 2010), perturbation of the mitochondrion appears to be a major toxicity pathway

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involved despite that the molecular initiating event (MIE) is still not clear. TGZ is believed to

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inhibit the activities of mitochondrial respiratory chain (MRC) complex enzymes, increase reactive oxygen species (ROS), decrease mitochondrial membrane potential (MMP) and prevent ATP synthesis (Hu et al., 2015). Besides, TGZ selectively stimulates the degradation of PGC-1α protein, and suppresses the expression of mitochondrial biogenesis regulator Tfam and oxidative phosphorylation enzymes (ATP5b and cytochrome C oxidase) (Liao et al., 2010). ATP decrease can lead to the inhibition of the bile salt export pump (BSEP), whose main role

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is to pump bile salt molecules out of the hepatocyte (Kullak-Ublick et al., 2000). Bile salt accumulation in hepatocytes can induce further mitochondrial dysfunction and even cell death because of its intrinsic detergent property (Delzenne et al., 1992; Gores et al., 1998).

Therefore, in the present study we focus on using in vitro hepatocyte assays to interrogate mitochondrial toxicity. Various cell types have been used in vitro to study TGZ toxicity, including primary human hepatocytes (PHH) and several liver-derived cell lines:

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HepG2, HC-04, and HepaRG. These studies provide us an opportunity to compare these cell models as applied to liver toxicity prediction. In the commonly used HepG2 cells, it was observed that (i) TGZ induced apoptosis (Yamamoto et al., 2001), which may involve c-Jun

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N-terminal protein kinase activation (Bae and Song, 2003) and upregulating apoptotic genes (Guo et al., 2006), (ii) TGZ induced decreases in MMP followed by cell death (Tirmenstein et

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al., 2002) or a rise of intracellular calcium and activation of caspase 3 (Bova et al., 2005), (iii)

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TGZ promoted the degradation of PGC-1α protein (Liao et al., 2010), and (iv) TGZ induced toxicity through involving chaperone proteins (Maniratanachote et al., 2005). In HC-04 cells, TGZ caused intramitochondrial oxidative stress which in turn led to activation of Ask1-

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dependent cell death signaling pathways (Lim et al., 2008). In PHHs, it was observed that TGZ caused decreases in the oxygen consumption rate (Goda et al., 2016), mitochondrial DNA

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damages and dysfunction and cell death (Rachek et al., 2009).

HepRG cells are a recently developed hepatocyte model believed to better resemble

PHHs, especially in metabolic capacity (Guillouzo et al., 2007). They have been used to characterize TGZ-induced cytotoxicity and mitochondrial toxicity either in 2D (Hu et al., 2015, Bell et al., 2017) or 3D (Gunness et al., 2013; Hendriks et al., 2016; Ramaiahgari et al., 2017) cultures. Yet in these studies, the concentration-response data of mitochondrial toxic and

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cytotoxic endpoints either did not cover enough concentration points or did not meet other criteria to allow us to identify POD using the BMD approach. To fill this data gap, we generated our own in vitro concentration-response data using HepaRG cells in the present study. The generated HepaRG data and those from other cell models reported in the literature were used together to identify PODs with the BMD modelling method. We then developed and validated a human population PBPK model for TGZ and used the model to conduct IVIVE that predicted the in vivo dose from the in vitro POD TGZ concentration metrics. By comparing the predicted

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in vivo dose to clinical dose range where liver injury was observed, our study demonstrated that the PBPK modelling-based IVIVE approach – when appropriate cells, in vitro biomarker, POD level, and dose metrics are determined – is a promising alternative to animal toxicity

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testing.

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2. Materials and Methods

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2.1. Cell culture

The human hepatoma HepaRG cells (Biopredict International, Saint Gregory, France) were maintained according to the supplier’s recommendations with minor modifications. Cells were

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thawed and seeded using Williams’ E medium (Gibco, Grand Island, USA) supplemented with 10% fetal bovine serum (FBS, Biological Industries, Kibbutz Beit-Haemek, Israel), 100 IU/mL

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penicillin (North China Pharmaceutical Co., Ltd, Shijiazhuang, China), 100 μg/mL

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streptomycin (Lukang Pharmaceutical Co., Ltd, Jining, China), 2 mM GlutaMax (Gibco, Grand Island, USA), 5 μg/mL insulin (National Institutes for Food and Drug Control, Beijing, China), and 0.5 mM hydrocortisone hemisuccinate (Solarbio, Beijing, China). Cultures were maintained in a sterile humidified incubator at 37 °C and 5% CO2. Passaging was performed every 2-3 days. Specifically, when cultures reached 80-90% confluence, the well-grown cells were rinsed with PBS, digested with 0.25% trypsin-0.02% EDTA for 2 min and observed for

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cell morphology under microscope. The trypsin solution was discarded to stop the digestion when significant cell shrinkage occurred, and then fresh serum-containing cell culture medium was added immediately. Cells were blown down gently, the cell suspension was centrifugated for 3 min (1000 rpm), and the supernatant was discarded. Cells were resuspended with culture medium, counted, and inoculated according to 1 to 4 volume ratio. Besides passaging, the digested cells were also used for toxicity assays.

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2.2. Measurement of cell viability Cell viability was measured using Alamar Blue (Life Technologies, Eugene, USA) according to the supplier’s protocol. 7, 000 cells per well were seeded in 96-well plates in 100 µL

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Williams’ E medium. TGZ (Batch NO.: 4B/197127, Tocris Bioscience, Bristol, UK) was dissolved with DMSO at 100 mM, and diluted with the culture medium to reach final

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concentrations of 0, 3.12, 6.25, 12.5, 25, 50 µM, respectively, at a volume of 100 µL. Cells

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were exposed to different concentrations of TGZ as above for 24 h. 10 μL Alamar Blue was then added to each well, followed by 2-h incubation at 37 ºC. The fluorescence intensity was read on a fluorescence spectrophotometer (SpectraMax i3x, Molecular Devices, San Jose, USA)

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at excitation and emission wavelengths of 530 nm and 590 nm respectively.

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2.3. Determination of mitochondrial superoxide and mitochondrial mass

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A fluorescein-staining cocktail of 100 nM Hoechst 33342 (Life Technologies), 5 µM MitoSOXTM Red (Life Technologies) and 100 nM MitoTracker® Green (Life Technologies) was prepared with 1 x HBSS (Gibco) at 37 oC. MitoSOXTM Red is a fluorescence dye selectively targeting mitochondria and labeling superoxide anion. MitoTracker® Green stains mitochondria independent of membrane potential and is used to detect mitochondrial mass. 7, 000 cells per well were seeded in 96-well plate in 100 µL Williams’ E medium. Cells were

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treated with 100 µL of TGZ medium at 0, 1.56, 3.12, 6.25, 12.5, and 25 µM for 24 h. Subsequently, each well was washed with 1 x HBSS, and 100 µL staining cocktail was added and incubated for 40 min in the dark. Prior to image acquisition 100 µL HBSS was used for a final rinse. Assay plates were analyzed using an Operetta High-Content Imaging System (PerkinElmer, Waltham, USA), with a 10X Plan Fluor objective. Nine digital images were captured in each well and analyzed using Harmony® 4.1 High Content Imaging and Analysis

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Software (PerkinElmer, Waltham, USA).

2.4. Statistical analysis

Data were normalized to control (without TGZ treatment) which was considered as 100%

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response and expressed as mean ± SD. All statistical analyses were conducted using one-way ANOVA in Origin 9.0 (Originlab, Northampton, USA) for evaluating differences between

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groups of interest and control. P < 0.05 was considered as statistically significant.

2.5. Literature review for TGZ-induced in vitro cytotoxicity and mitochondrial toxicity studies The terms “troglitazone”, “liver injury”, “hepatotoxicity”, combined in Boolean logic

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“troglitazone and (liver injury or hepatotoxicity)”, were searched in the PubMed database. In addition, the submission of Rezulin (TGZ tablet) to FDA (FDA, 1999) and relevant references

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therein were also examined. An article was accepted in our analysis if it simultaneously met

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the following inclusion criteria. (1) Experiments only used human hepatic cell models; (2) Cells were treated with TGZ for ≤ 24 h. (3) For the purpose of BMD modelling, there were at least three concentration groups including control, and the mean ± SD (or stand error, SE) was available when individual data was not reported (EPA, 2016).

2.6. Human PBPK model development and validation 9

PBPK modelling was performed using the GastroPlusTM 9.6 software (Simulations Plus Inc., Lancaster, CA, USA). In brief, the model contains 14 organ/tissue compartments including the lung, liver, spleen, gut, adipose tissue, muscle, heart, brain, kidney, skin, reproductive organ, red marrow, yellow marrow, and rest of the body, which were connected by the venous and arterial blood circulation. A series of differential equations were used to quantitatively simulate the ADME processes. As TGZ was a small lipophilic molecule with good cell membrane permeability, perfusion-limited tissue distribution was assumed and the Lukacova (Rodgers-

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Single) method was used for calculating the tissue/plasma partition coefficients (Kp) of TGZ (Zhuang and Lu, 2016). The liver was considered to be the main site of TGZ elimination since it eliminates 84.5% of TGZ after oral administration in human (Loi et al., 1999b). PBPK

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parameters attributed to physiochemical and ADME properties of TGZ are listed in Table 1, including molecular weight, LogP, acid dissociation constant (pKa), solubility, mean

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precipitation time, diffusion coefficient, drug particle density, effective particle radius,

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blood/plasma concentration ratio (Rbp), unbound fraction of drug in plasma (Fup%) and clearance.

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Table 1. Troglitazone-related parameters for the construction of PBPK models using GastroPlusTM Value

Methods/reference

molecular weight (g/mol)

441.545

DrugBank Plus

logP

3.6

DrugBank Plus

pKa

7.77

Predicted-ADMET

solubility (mg/mL at pH 5.98)

0.0287

Predicted-ADMET

mean precipitation time (sec)

900

Predicted-ADMET

diffusion coefficient (cm2/s*105)

0.61

Predicted-ADMET

drug particle density (g/ml)

1.2

Predicted-ADMET

effective particle radius (μm)

25

Predicted-ADMET

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Parameter

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Rbp

0.55

Cubitt et al., 2011

Fup%

1

Cubitt et al., 2011

clearance (CL, mL/min/kg)

2.5

Cubitt et al., 2011

To evaluate the accuracy of the PBPK model in predicting TGZ pharmacokinetics in human, the blood/plasma concentrations predicted by the model with default parameter values

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were compared with observed ones in volunteers given multiple oral administrations (P.O.) of 200, 400, 600 mg TGZ tablets daily for 7 consecutive days (Loi et al., 1999a), as well as single P.O. of 400 mg TGZ tablet (Young et al., 1998), and these studies were not used in model

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building. Table 2 shows the volunteer demographics, doses, exposure routes and durations

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applied in PBPK model evaluation.

Table 2. Baseline demographic characteristics in the troglitazone pharmacokinetics studies Subjects

Age (yr)

Height (cm)

Body Weight (kg)

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Author, year

Diet

15 healthy 32 68.7 males and N.A. fed (20-55) (51.4-90.9) 6 healthy females Young 15 176 31 79.1 Fasted et al., healthy (170(20-41) (64.6-95.2) /fed 1998 males 186) N.A.- no subject information available; P.O.- oral administration.

P.O. 200, 400, 600 mg daily for 7 consecutive days on separate occasions P.O. 400 mg tablet without/with food

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Loi et al., 1999a

Administration

2.7. Sensitivity analysis Parameter sensitivity analysis (PSA) was performed with GastroPlusTM for oral TGZ tablet under fed condition, to assess the importance of the selected parameters in predicting Cmax and AUC. Only one parameter was changed at a time gradually from one-tenth to ten-fold of its

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default value by sequentially multiplying the lower-bound value with a constant calculated as follows:

constant =

9 higher bound value

√

lower bound value

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=√

0.1

= 1.668

PSA was performed for parameters related to drug physiochemical and ADME properties and their interactive nature which may influence Cmax and AUC, including small intestine transit time, stomach transit time, solubility, LogP, molar radius, effective permeability (Peff),

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diffusion coefficient, dose, particle radius, particle density, first pass extract from gut, first pass extract from liver, Rbp, fraction unbound in plasma (Fup%), Kps in organs, and hepatic

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clearance.

2.8. Population PBPK simulation

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A virtual PBPK population was developed in GastroPlusTM to simulate potential inter-

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individual viability in the pharmacokinetics of TGZ. All of the relevant physiological and pharmacokinetic parameters were randomly sampled from pre-defined distributions for each individual. Some PBPK parameters, which are dependent on race, gender and age, have

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distributions defined by the GastroPlusTM built-in Population Estimates for Age-Related Physiology (PEAR Physiology) generator. For this study, a virtual population of 100

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Caucasians with 50 males and 50 females, age ranging between 35-95 years, body weight

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between 60-120 kg and BMI between 25-40, was generated. During population simulation, mean and highest values of Cmax, and AUC(0-t) after oral administration of TGZ tablet in the virtual population were calculated.

2.9. Derivation of BMCL5 on in vitro toxicity data

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BMD modeling was performed according to the guidance by the US Environmental Protection Agency (EPA)’s BMD Software (BMDS) version 2.7 (https://www.epa.gov/bmds) to determine in vitro PODs for TGZ cytotoxicity and mitochondrial toxicity. In vitro data obtained from the present study and the literature were modeled using BMDS to obtain the benchmark concentration lower bound (BMCL) using all continuous models provided in BMDS. A 5% change from the control level was defined as the benchmark response (BMR) in our study. The requirements for acceptance of a model are: p-value Test 1 < 0.05, p-value Test 2 > 0.05, p-

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value Test 3 > 0.05, p-value Test 4 > 0.05, and the absolute value of scaled residual ((observed minus predicted response)/SE) of the concentration group closest to the BMD ≤ 2. All models that met the requirements were considered for the determination of BMCL5, but as a

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conservative measure, only the lowest BMCL5 was used as the in vitro POD for IVIVE in the

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present study.

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2.10. Translation of in vitro POD to in vivo POD and evaluation of the IVIVE approach To translate in vitro POD to in vivo POD, two concentration metrics were used: Cmax and AUC.

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Given that TGZ is hepatoxic, the liver concentration was deemed to be the most relevant metric to use for IVIVE of the in vitro POD concentration. When using the Cmax metric, the lowest

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BMCL5, derived from the suite of fitted BMD models for each in vitro endpoint, was aimed at by adjusting the oral dose of TGZ tablet for the PBPK model such that the predicted liver Cmax

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is equal to the lowest BMCL5. When using the AUC metric, the mathematical product of the lowest BMCL5 and 24-h was aimed at by the PBPK model to achieve an equivalent predicted AUC of the liver concentration (Daston et al., 2010). Finally, these predicted in vivo POD doses were compared with the clinical oral doses of TGZ where liver toxicity has been observed to evaluate the prediction performance of the IVIVE approach. 3. Results 13

3.1. TGZ induced concentration-dependent decreases in cell viability The viability of HepaRG cells after exposure to TGZ at concentrations of 0-50 µM for 24 h was evaluated. Compared with cells not treated with TGZ (control), cell viability decreased as the concentration of TGZ increased (Fig. 1A). Cell viability for the 12.5, 25, and 50 μM TGZ groups decreased significantly to 85.4%, 76.4%, and 47.7% of the control level, respectively.

3.2. TGZ induced mitochondrial superoxide accumulation and mitochondrial mass decline

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After HepaRG cells were exposed to TGZ for 24 h, mitochondrial superoxide levels, as monitored by MitoSox Red, were elevated and the 13.7% increase at 3.125 μM and 32.2% increase at 25 μM were significant compared to control (Fig. 1B). Mitochondrial mass, as

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measured with MitoTracker® Green, significantly declined concentration-dependently to 87.5%

3.3. Human PBPK model validation

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of the control level at 25 μM TGZ (Fig. 1C).

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The PBPK model was validated with clinical plasma concentration-time data. The PBPK model-simulated TGZ plasma concentration-time profiles (at default parameter values) and the

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clinical data are shown in Fig. 2. There was a 0.7-1.3 fold error between the Cmax predicted and observed, and 0.6-1.1 fold error between the AUC predicted and observed, under

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multiple/single dose or fed/fasted conditions (Table 3). Because the prediction errors for all conditions are within 0.5-2-fold, the PBPK model developed is considered reasonably reliable

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to predict the plasma and organ concentrations of TGZ in human (Jiang et al., 2013).

Table 3. The mean observed and predicted dose metrics of TGZ in human under different dosing regimens Dose

Plasma Cmax (µg/mL)

Plasma AUC0-t (µg×h/mL)

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Reference

Observed

Predicted

Fold error

Observed

Predicted

Fold error

200 mg

0.65

0.83

1.28

7.02

4.33

0.62

Loi et al., 1999a

400 mg

1.34

1.56

1.16

13.48

8.65

0.64

Loi et al., 1999a

600 mg

2.41

2.20

0.91

21.86

12.97

0.59

Loi et al., 1999a

400 mg fasted

1.26

1.23

0.98

7.85

8.42

1.07

Young et al., 1998

400 mg fed

2.10

1.44

0.69

13.15

8.46

0.64

Young et al., 1998

3.4. Sensitivity analysis

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PSA was performed for oral TGZ tablet under fed condition to determine important parameters that have large influence on the model. The results show that Cmax is more sensitive to changes in solubility, Peff, dose, mean drug particle radius, drug particle density, Fup% and hepatic

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clearance (Fig. 3A), with lower sensitivity to changes in other parameters such as the transit

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time in stomach and small intestine, Rbp, and Kps in organs (results not shown). Compared with the results for Cmax, AUC(0-t) is sensitive to fewer parameters including dose, Fup% and

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hepatic clearance (Fig. 3B).

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3.5. Derivation of BMCL5 from in vitro data

The concentration-response data of cytotoxicity and mitochondrial toxicity induced by TGZ,

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both from our own experiments and literature, were shown in Fig. 4. The derived BMCL5 of TGZ ranges between 0.3-6.4 μM for HepaRG, HC-04, PHH and HepG2 cells exposed to TGZ

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for 24 h (Table 4). For each cell model and endpoint combination, the BMCL5 values derived from different BMD models differ by about 2-fold or less and only the lowest was used for subsequent IVIVE as a conservative measure.

Table 4. BMCL5 derived from the concentration-response curves in Fig. 4 In vitro endpoint

Cell model

BMCL5 (µM)

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Studies

HepaRG

3.2-4.5

present study

HepG2

4.1

Liao et al., 2010

HC-04

3.1-6.4

Lim et al., 2008

PHH

1.1-2.3

Rachek et al., 2009

mito-mass

HepaRG

0.5-0.8

present study

mito-ROS

HepaRG

3.8-4.3

present study

HepG2

0.9-1.1

Liao et al., 2010

PHH

0.3-0.6

ATP

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viability

Rachek et al., 2009

Note: the ranges of BMCL5 values represent those derived from different fitted BMD models.

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3.6. Translation of in vitro BMCL5 to in vivo POD doses and evaluation of the predictive

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performance of the IVIVE approach

Two concentration metrics, liver Cmax and AUC, were used to derive in vivo POD doses for

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TGZ from the in vitro BMCL5 by conducting reverse dosimetry via the population PBPK model developed. There are two alternative underlying assumptions: in a human individual (1)

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if the liver Cmax reaches or surpasses BMCL5, liver toxicity may occur; (2) if the liver AUC reaches or surpassed BMCL5 × 24 h, liver toxicity may occur. For the first case, a single oral

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dose of TGZ, i.e., the input to the PBPK model, was varied until the mean liver Cmax predicted by the population PBPK model equaled BMCL5 and this oral dose was designated as the mean

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in vivo POD dose for the Cmax metric. Using BMCL5 for various cell models and cellular toxicity endpoints, the mean POD doses ranged between 28-372 mg/d, varying by about 13fold (Table 5). For the second case, a single oral dose of TGZ was similarly varied until the mean AUC of liver TGZ concentration in the first 24 h predicted by the population PBPK model equaled BMCL5 × 24 h and this oral dose was designated as the mean in vivo POD dose for the AUC metric. Using BMCL5 × 24 h for various cell models and cellular toxicity 16

endpoints, the POD doses derived ranged between 185-2552 mg/d, varying by about 14-fold (Table 5). This range of POD doses was about 7-fold higher than that when the Cmax metric was used.

While the above IVIVE analysis was based on the population means of Cmax and AUC, as a conservative approach we also examined the situation when the Cmax and AUC in the top one percentile of the virtual population (i.e., the most sensitive subpopulation which has the

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highest Cmax or AUC) reached BMCL5 and BMCL5 × 24 h respectively. For the case of using Cmax as the metric, the POD doses ranged between 15-178 mg/d, varying by about 12-fold between combinations of various cell models and cellular toxicity endpoints (Table 5). This

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range of POD doses is about half of that when population mean Cmax was used. For the case of using AUC as the metric, the POD doses ranged between 83-1010 mg/d, varying by about 12-

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fold between combinations of various cell models and cellular toxicity endpoints (Table 5).

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This range of POD doses is slightly more than half of that when population mean AUC was used.

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Table 5. Predicted POD doses of TGZ using PBPK modelling-based IVIVE approach, based on liver highest (top 1%) Cmax, mean Cmax, highest (top 1%) AUC and mean AUC of the virtual population

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In vitro endpoint

viability

mito-mass

Cell model

Predicted POD dose (mg/d) Reference

highest

mean

highest

mean

Cmax

Cmax

AUC

AUC

HepaRG

135

286

793

1878

present study

HepG2

178

372

1010

2552

Liao et al., 2010

HC-04

133

269

757

1783

Lim et al., 2008

PHH

48

90

268

599

Rachek et al., 2009

HepaRG

20

42

129

288

present study

17

mito-ROS

HepaRG

158

345

933

2303

present study

HepG2

39

77

230

510

Liao et al., 2010

ATP

PHH

15

28

83

185

Rachek et al., 2009

Fold range

-

11.9

13.3

12.2

13.8

-

To evaluate the performance of the PBPK modelling-based IVIVE approach, the predicted POD doses were compared with the frequently used clinical dose range (200-800

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mg/d) where liver adverse effects have been observed (Table 6 and references therein). The results show that the POD doses of TGZ derived based on population mean Cmax were below or within the clinical 200-800 mg/d dose range (Fig. 5A). However, all the POD doses derived

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based on the top one percentile Cmax were below the clinical dose range, with the highest POD dose 178 mg/d sitting right below the 200 mg/d mark (Fig. 5B). In contrast to using Cmax, using

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AUC resulted in considerable overlaps of predicted POD dose range with the clinical 200-800 mg/d dose range (Fig. 6), especially for the r top one percentile AUC, while population mean

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AUC seems to overshoot the range.

Male, 62

Medicatio n

Duration of treatment

Liver biochemist ry

Adverse response/ Intervention

Reference

type 2 diabetes

Phase I, 200 mg b.i.d.; phase II, 400 mg b.i.d.

Phase I, 3 months; phase II, 5 months

ALT=44 after 1 month; ALT=653, AST=413 after 5 months

Jaundice, liver zone 3 necrosis, hepatocyte drop out/stop troglitazone

Kohlroser et al., 2000

type 2 diabetes

400 mg q.d.

2 months (other medications had been

ALT=140; AST=65

Liver cell injury (cell swelling, frequent binucleated

Kohlroser et al., 2000

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Femal e, 48

Health conditio n

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Gende r, age (yr)

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Table 6. Case reports of TGZ-induced liver dysfunction, injury and failure

18

cells)/stop troglitazone

type 2 diabetes

400 mg/d

3.5 months

ALT=405; AST=798

Jaundice, hepatic encephalopathy/liv er transplant

Neuschwand er-Tetri et al., 1998

Male, 58

type 2 diabetes

400 mg/d

~4 months (concomitan t with glibenclami de for ~1.5 months)

ALT=1655 ; AST=699

Jaundice, fulminant Hepatitis/died

Shibuya et al., 1998

Male, 85

type 2 diabetes

Phase I, 200 mg q.d.; phase II, 400 mg q.d.

Phase I, 3.75 months; phase II, 1.25 months

ALT=608

Jaundice, hepatitis; hepatic necrosis/died

Femal e, 44

type 2 diabetes

Phase I, 200 mg q.d.; phase II, 400 mg q.d.

Phase I, 1 month; phase II, 4 months

ALT=594; AST=959

Femal e, 63

type 2 diabetes

Phase I, 200 mg b.i.d.; phase II, 200 mg t.i.d.; phase III 200 mg b.i.d

Male, 71

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Femal e, 55

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taken for ≥2 yr)

Vella et al., 1998

Gitlin et al., 1998

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Icteric, tender and hepatic necrosis/discontinu ed troglitzaone

ALT=45; AST=50

Jaundice, subacute massive necrosis of the liver/died

Fukano et al., 2000

10 months

ALT=318; AST=266

abdominal distention, jaundice and oedema/died

Li et al., 2000

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Phase I, 25 days; phase II, 14 days; phase III, 4 months

200 mg q.d.

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type 2 diabetes

q.d.- once daily, b.i.d.- twice daily, t.i.d.-three times daily. ALT-alanine aminotransferase test (normal 5~40 U/L), AST-aspartate aminotransferase test (normal 10~35 U/L).

4. Discussion

19

While traditional animal-based toxicity testing and risk assessment approach is associated with uncertainties associated with inter-species extrapolation, inter-individual variabilities and other uncertainty factors (Hartung, 2009), next-generation risk assessment using the PBPK modelling-based IVIVE paradigm is associated with uncertainties arising from different sources. These uncertainties include those relating to choice of cell type, in vitro kinetics and dose metric, choice of in vitro biomarker(s)/endpoint(s), magnitude and duration of endpoint departure and determination of inter-individual variability (Zhang et al., 2018). The present

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study aimed to evaluate the fitness of the approach method under some of its uncertainties in informaing chemical doses that may result in liver injury in humans. TGZ was used as a case

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study due to its known hepatoxicity and availability ofin vitro assays and clinical data.

Choosing human cells and cellular biomarkers that are fit-for-purpose is important and

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probably is the step of the in vitro assay-based IVIVE approach that introduces the most

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uncertainty. To evaluate hepatotoxicity, here we used data from 4 different human liver cell models: PHH and three cell lines, HepaRG, HC-04 and HepG2. Of the cell lines it is unclear which one best represents the average human liver physiology in vivo, as the difference

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between these cells can potentially affect both the toxicokinetics and toxicodynamics (Berger et al., 2016). HepaRG cells are believed to better resemble primary human hepatocytes

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compared to HepG2 and HC-04 cells especially in metabolic capacity, but its other

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characteristics compared with the two cell lines are not clear (Guillouzo et al., 2007). In human, TGZ has three main metabolites: a sulfate conjugate (M1) catalyzed by phenol sulfotransferase (ST1A3), a glucuronide conjugate (M2) catalyzed by UDP-glucuronosyltransferase (UGT), and a quinone metabolite (M3) catalyzed by CYPs (CYP3A4, CYP2C8, and CYP2C19) from sulfation, glucuronidation, and oxidation reactions, respectively. TGZ itself, M1 and M3 are responsible for the liver toxicity (Yokoi, 2010). The variations of metabolizing enzymes in the

20

different liver cell models used in this work would result in different amounts of parent and toxic metabolites, and thus different toxic responses to TGZ.

We show here that the cytotoxicity of TGZ as evaluated by viability assays varied slightly among the 4 cell models. In PHHs, TGZ treatment resulted in BMCL5 values ranging between 1.1-2.3 µM, while the three cell lines have higher but similar BMCL5 levels ranging between 3.1-6.4 µM (Table 4), suggesting that PHHs are more sensitive to TGZ. When using

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the lowest cytotoxicity BMCL5 for each cell model and the population mean liver Cmax was matched to the BMCL5, we found that the POD doses of TGZ predicted with HepaRG, HC-04, and HepG2 cells fall within the dose range where liver injury has been observed clinically

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(200-800 mg/d), while PHH produced a more protective (conservative) POD dose (Fig. 5A). In comparison, among the POD doses derived from mitochondrial biomarkers, only the one

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using HepaRG ROS is within the clinical dose range, while mitochondrial mass and ATP are

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conservative as biomarkers for in vivo POD prediction using BMCL5 levels (Fig 5A). When the liver Cmax of only the top 1% most sensitive virtual population was matched to the BMCL5, the predicted in vivo POD doses from all cellular biomarkers are lower than the clinical dose

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range (Fig. 5B), suggesting overly conservative predictions. Increasing in vitro BMR level from 5% to 10% or higher for cellular responses may help to make the prediction less

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conservative, but uncertainty may increase due to divergence in concentration-response among

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different cell models and biomarkers. Moreover, the concentration-response of mitochondrial mass in HepaRG cells plateaued around 90% of the control (Fig. 4B), therefore identifying BMCL10 based on BMC10 will likely meet with increasing uncertainty with such data.

The in vivo POD doses predicted by the AUC approach consistently cover the 200-800 mg/d clinical dose range regardless of using the population mean or top one percentile AUC to

21

match the in vitro BMCL5 AUC, although half of the in vivo POD doses predicted with population mean surpassed the clinical dose range (Fig. 6). The POD doses predicted by the AUC metric are generally higher than those predicted by the Cmax metric by about 6-fold (Table 5). This less conservative prediction with AUC may be attributed to the fact that while the in vitro kinetics of TGZ in the cell models is unknown, BMCL5 × 24 h is likely an overestimate of the AUC that cells experienced in vitro because TGZ clearance in the medium is not considered when using BMCL5 × 24 h. Had a lower in vitro AUC value been used due to TGZ

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clearance, the derived in vivo POD doses will be correspondingly lower, thus closer to the values predicted with Cmax.

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In risk assessments using results from traditional animal-based toxicology data, unless known otherwise, a default uncertainty factor of 10 is normally used to account for inter-species

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difference (Renwick, 1993). In human cell-based in vitro approaches to risk assessment, this

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inter-species uncertainty factor becomes irrelevant (Abdullah et al., 2016). However, risk assessments using data from in vitro human cells brings in other uncertainties such as selection of cell models and cellular biomarkers (Boonpawa et al., 2017). Ideally the human cells used

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should represent an average response of the particular cell type involved in vivo, however this can hardly be the case in practice, because many of the cells used as of today were originally

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derived from human cancer cells. Even the use of PHH or human iPSC-derived differentiated

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cells, which resemble normal cells more closely than cancer-derived cells, also need to be well characterized for their “averageness” because cells from human individuals can behave very differently due to inter-individual variability. In the present study, the cell viability concentration-response diverged between the four in vitro hepatocyte models (Fig. 4). However, because BMR5 is only a small departure from the baseline, the BMCL5 values for these cells are still comparable except for PHH cells which as primary cells appear to be more sensitive

22

to TGZ. The in vivo POD doses derived from these cell models and the multiple cellular endpoints differed by about one order of magnitude regardless of using the Cmax or AUC approach (Table 5), suggesting a reasonably constrained uncertainty in this regard.

Another default factor of 10 is also used to account for inter-individual difference in traditional human health risk assessment (Renwick, 1993), which is an issue that the in vitro cell assay-based approach also has to face. PBPK modeling-based IVIVE provides an

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opportunity to reduce the uncertainty by explicitly accounting for the inter-individual differences with chemical-specific adjustment factors (CSAF). Human population PBPK models can take into considerations the variations in physiological parameters and chemical-

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specific metabolism arising from genetic polymorphism to derive a CSAF to replace the default factor of 3.16 for individual-to-individual differences in PK. In the present study, we used

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human population PBPK models to explore the POD doses of the population mean as well as

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the top one percentile sensitive population. The ratio between these two POD doses is close to 2.0 for each cell model and cellular endpoint combination when Cmax is used, and is close to 2.3 when AUC is used (Table 5). Conceivably the CSAF for the PK step, if calculated as the

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ratio of the POD dose of the population mean over that of the top 5% of the population, will be even smaller, resulting in reduced uncertainty with the IVIVE approach. Regarding the source

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of PK variability, it was found that the donors of cryopreserved human hepatocytes which had

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lower amounts of glutathione (GSH)-conjugated TGZ were more sensitive to TGZ-induced hepatoxicity, suggesting differences in the cellular detoxification capability contribute to human variability (Kostrubsky et al., 2000; Prabhu et al., 2002). Given the role of glutathione S-transferases (GST) in catalyzing GSH to reactive chemicals or their metabolites, GST gene differences have been considered as an important contributor to inter-individual variability in TGZ-induced liver injury (Yokoi, 2010).

23

5. Conclusion The present work shows that integrating in vitro assays and PBPK modelling-based reverse dosimetry in the IVIVE approach provides a promising platform to inform the dose range of TGZ that induces hepatotoxicity. Yet further optimization remains for such new approach method before it can be reliably applied with confidence. For example, the free drug concentration in the culture medium should be considered, which may better correlate with the

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free tissue concentration. The predicted PODs vary with the in vitro cell models and endpoints selected, which is another source of uncertainty in this approach that needs to be further reduced. Whether the specific in vitro cellular POD used here can be generalized to the safety assessment

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of other hepatotoxic chemicals remain to be seen.

24

Conflicts of interest The authors declare no conflict of interests.

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal

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relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be

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considered as potential competing interests:

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Acknowledgement

This work was supported by National Natural Science Foundation of China (81430090,

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81470167); Beijing Nova Program (Z171100001117103); AMMS Innovative Foundation (2017CXJJ13) and Unilever International Collaborative Project (MA-2015-00410). Special

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thanks go to Alistair Middleton and Maria Baltazar for their great support while the IVIVE part

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of the study was carried out at the Unilever R&D Colworth, UK.

25

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Figure Legend

Fig. 1. TGZ-induced cytotoxicity and mitochondrial toxicity in HepaRG cells. Cells were

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treated with TGZ at various concentrations for 24 h. Cell viability (A) was measured using Alamar Blue assay. Mitochondrial ROS (B) and mitochondrial mass (C) were determined by

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P<0.05 compared with control.

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high content analysis. Data are presented as means ± SD from 3 independent experiments. *

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Fig. 2. Mean observed vs. predicted plasma concentration profiles of TGZ in human for model

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validation following various oral doses. Symbols represent mean observed data digitized from

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published studies indicated. P.O., oral administration.

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Fig. 3. Parameter sensitivity analysis for oral administration of TGZ tablet under fed condition. The Y-axis is simulated Cmax (A) and AUC (B). The center of the X-axis for each parameter tested represents the default value that was used in the simulations shown in Fig. 2. Each of the X-axis scales shows the lower and higher bounds of the parameter values for PSA. RefSol (reference solubility), Peff (effective permeability), RadPart (drug particle radius), Fup

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re

-p

ro of

(fraction unbound in plasma), CL-liver (liver clearance).

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Fig. 4. In vitro concentration-response curves of toxicity endpoints for TGZ in human liver cell

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models. Data are presented as mean ± SD.

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Fig. 5. Predicted POD single oral dose of TGZ in human by using PBPK modelling-based

lP

IVIVE approach with liver Cmax as the surrogate metric. (A) Using mean Cmax of the virtual population, (B) using top one percentile Cmax of the virtual population. Each diamond symbol

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denotes the POD dose derived from using in vitro BMCL5 (the lowest predicted by different BMD models) of a particular combination of cell model and in vitro endpoint. The two vertical

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dashed lines denote the clinical dose range (200-800 mg/d).

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Fig. 6. Predicted POD single oral dose of TGZ in human by using PBPK modelling-based

lP

IVIVE approach with liver AUC as the surrogate metric. (A) Using mean AUC of the virtual population, (B) using top one percentile AUC of the virtual population. Each diamond symbol

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denotes the POD dose derived from using in vitro BMCL5 (the lowest predicted by different BMD models) of a particular combination of cell model and in vitro endpoint. The two vertical

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ur

dashed lines denote the clinical dose range (200-800 mg/d).

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