Permeabilized cryopreserved human hepatocytes as an

1

Permeabilized cryopreserved human hepatocytes as an exogenous metabolic system in a novel

metabolism-dependent cytotoxicity assay (MDCA) for the evaluation of metabolic activation and

detoxification of drugs associated with drug induced liver injuries: Results with acetaminophen,

amiodarone, cyclophosphamide, ketoconazole, nefazodone, and troglitazone

Author names

Hong Wei and Albert P. Li

Author affiliations

In Vitro ADMET Laboratories Inc., Columbia, MD

This article has not been copyedited and formatted. The final version may differ from this version.DMD Fast Forward. Published on November 8, 2021 as DOI: 10.1124/dmd.121.000645

at ASPE

T Journals on January 24, 2022

dmd.aspetjournals.org

Dow

nloaded from

http://dmd.aspetjournals.org/

2

Running title: Metabolic activation and detoxification of DILI drugs

Corresponding author: Albert P. Li, Ph. D., In Vitro ADMET Laboratories Inc., 9221 Rumsey Road Suite 8,

Columbia, MD 21045, USA. Telephone: (410)869-9037. Fax: (410)869-9034. Email:

[email protected].

Number of text pages: 31

Number of tables: 6

Number of figures: 7

Number of references: 84

Number of words (Abstract): 243

Number of words (Introduction): 749

Number of words (Discussion): 1438

List of Abbreviations: DILI (drug-induced liver injuries), DME (drug metabolizing enzymes), MDCA

(metabolism-dependent cytotoxicity assay), GSH (L-glutathione), GST(glutathione S-transferase), MMHH

(MetMax cryopreserved human hepatocytes), HIM (Hepatocyte Induction Medium), NAD+

(Nicotinamide adenine dinucleotide), NADPH (nicotinamide adenine dinucleotide phosphate), PAPS (3'-

phosphoadenosine-5'-phosphosulfate), SULT (cytosolic sulfotransferase), UCPM (Universal

Cryopreservation Plating Medium), UDPGA (uridine 5′-diphosphoglucuronic acid, UGT (uridine 5'-

diphospho-glucuronosyltransferase),


at ASPE



Dow

nloaded from

mailto:[email protected]


3

Abstract:

We report here a novel in vitro experimental system, the metabolism-dependent cytotoxicity assay

(MDCA), for the definition of the roles of hepatic drug metabolism in toxicity. MDCA employs

permeabilized cofactor-supplemented cryopreserved human hepatocytes (MetMax™ human

hepatocytes, MMHH), as an exogenous metabolic activating system, and HEK293 cells, a cell line devoid

of drug metabolizing enzyme activity, as target cells for the quantification of drug toxicity. The assay was

performed in the presence and absence of cofactors for key drug metabolism pathways known to play

key roles in drug toxicity: NADPH/NAD+ for phase 1 oxidation, UDPGA for UGT mediated

glucuronidation, PAPS for SULT mediated sulfation, and GSH for GST mediated GSH conjugation. Six

drugs with clinically significant hepatoxicity, resulting in liver failure or a need for liver transplantation:

acetaminophen, amiodarone, cyclophosphamide, ketoconazole, nefazodone and troglitazone were

evaluated. All six drugs exhibited cytotoxicity enhancement by NADPH/NAD+, suggesting metabolic

activation via phase 1 oxidation. Attenuation of cytotoxicity by UDPGA was observed for

acetaminophen, ketoconazole and troglitazone, by PAPS for acetaminophen, ketoconazole and

troglitazone, and by GSH for all six drugs. Our results suggest that MDCA can be applied towards the

elucidation of metabolic activation and detoxification pathways, providing information that can be

applied in drug development to guide structure optimization to reduce toxicity and to aid the

assessment of metabolism-based risk factors for drug toxicity. GSH detoxification represents an

endpoint for the identification of drugs forming cytotoxic reactive metabolites, a key property of drugs

with idiosyncratic hepatotoxicity.


at ASPE



Dow

nloaded from


4

Significance Statement: Application of the metabolism-dependent cytotoxicity assay (MDCA) for the

elucidation of the roles of metabolic activation and detoxification pathways in drug toxicity may provide

information to guide structure optimization in drug development to reduce hepatotoxic potential, and

to aid the assessment of metabolism-based risk factors. GSH detoxification represents an endpoint for

the identification of drugs forming cytotoxic reactive metabolites that may be applied towards the

evaluation of idiosyncratic hepatotoxicity.


at ASPE



Dow

nloaded from


5

Introduction

Metabolic activation – metabolism of a relatively nontoxic parent molecule to toxic metabolites, and

detoxification – biotransformation of the toxic parent and metabolite molecules to nontoxic metabolites,

are key determinants of drug toxicity (Hinson et al., 1994). Definition of metabolic activation and

detoxification pathways of drug candidates is an important aspect of drug development, providing

information that can be applied towards structural design and drug candidate selection to minimize

toxicological liability, identification of animal species for preclinical evaluation to improve the accuracy

of assessment of human toxicity, identification of at risk patient populations based on metabolic

activation and detoxification capacity, and identification of environmental factors that may exacerbate

drug toxicity via their induction of metabolic activating pathways and inhibition of detoxification

pathways (Spielberg, 1984; Nebert et al., 1996; Li, 2002; Tuschl et al., 2008; Baillie and Rettie, 2011).

We report here a novel in vitro experimental system for the evaluation of the roles of drug metabolizing

enzymes in drug toxicity, the Metabolism Dependent Cytotoxicity assay (MDCA). MDCA utilizes a novel

in vitro model for the evaluation of hepatic drug metabolism developed in our laboratory, the cofactor-

supplemented permeabilized human hepatocytes (MetMax™ Human Hepatocytes, MMHH) (Li et al.,

2018), as an exogenous hepatic metabolic system, with human embryonic kidney 293 (HEK293) cells, a

cell line deficient in drug-metabolizing enzyme activities (Westlind et al., 2001; Koga et al., 2011), as

target cells for the quantification of cytotoxicity (Figure 1). MMHH was originally developed to enhance

the ease of use of human hepatocytes for drug metabolism studies, with features that can overcome

several limitations of intact human hepatocytes: 1. The drug metabolizing enzyme (DME) activities of

MMHH are not affected by drug toxicity, a major limitation of intact hepatocytes in the evaluation of

toxic drugs. 2. Due to permeabilization, plasma membranes no longer serve as a barrier for drug entry

and metabolite exit, thereby allowing the evaluation of the metabolic fate of drugs with low

permeability (Keefer et al., 2020). 3. As permeabilization drastically reduced endogenous cofactor


at ASPE



Dow

nloaded from


6

concentrations due to leakage, drug metabolism by MMHH requires cofactor supplementation and

thereby can be directed via cofactor specification, for instance, reduced nicotinamide adenine

dinucleotide phosphate (NADPH) for phase 1 oxidation, uridine 5′-diphosphoglucuronic acid (UDPGA)

for uridine 5'-diphospho-glucuronosyltransferase (UDP-glucuronosyltransferase, UGT) mediated

glucuronidation, 3'‐phosphoadenosine 5'‐phosphosulfate (PAPS) for cytosolic sulfotransferase (SULT)-

mediated sulfate conjugation, and reduced glutathione (GSH) for glutathione S-transferase (GST)-

mediated GSH conjugation). These features suggest that MMHH may also be applicable as an

exogenous activating system for protoxicant activation for a co-cultured target cells, akin to the

application of induced rat liver S9 in the evaluation of promutagens in genotoxicity assays (McCann et al.,

1975; Li, 1984; Mitchell et al., 1997). Furthermore, the contribution of specific drug metabolism

pathways towards drug toxicity may be defined via supplementation of MMHH with selected cofactors.

We report here MDCA results with six drugs associated with drug-induced liver injuries (DILI):

acetaminophen, amiodarone, cyclophosphamide, ketoconazole, nefazodone, and troglitazone (Figure 2).

Acetaminophen is a widely used over-the-counter analgesic and antipyretic drug which is known to

cause severe liver injury, mainly due to overdose (Clark and Taubman, 2016; Bouvet et al., 2020).

Amiodarone is an antiarrhythmic that was withdrawn from the market due to adverse toxicity in multiple

tissues, including fatalities due to hepatotoxicity (Agozzino et al., 2002; Kang et al., 2007; Babatin et al.,

2008; Wu et al., 2021). Cyclophosphamide is a widely used alkylating anticancer agent with toxic

consequences including hepatotoxicity (Subramaniam et al., 2013), cardiotoxicity (Liu et al., 2018),

hemorrhagic cystitis (Kolb et al., 1994), hepatic venoocclusive disease (Ortega et al., 1997),

immunosuppression (Hauser et al., 1983), and genotoxicity/carcinogenicity (Hansel et al., 1997).

Ketoconazole is an orally administered azole antifungal known to cause severe hepatotoxicity, resulting

in liver failure (Findor et al., 1998; Zollner et al., 2001; Li et al., 2021). Nefazodone, an antagonist for the

5-hydroxytryptanine receptor for the treatment of depression, was withdrawn from the market due to


at ASPE



Dow

nloaded from


7

its association with severe hepatoxicity, resulting in liver failure (Stewart, 2002). Troglitazone, the first

oral peroxisome proliferator-associated receptor gamma agonist for the treatment of type 2 diabetes,

was marketed in March 1997 and was removed from the U.S. market 36 months later after 90 cases of

liver failure. Troglitazone is often regarded as a prototypical drug for research to further the

mechanistic understanding of idiosyncratic drug toxicity as well as the development of predictive

approaches for the identification of structures with idiosyncratic hepatotoxic potential in drug

development (Goda et al., 2016; Takemura et al., 2016; Kim et al., 2017a; Mak et al., 2018).

Materials and Methods

Chemicals. The hepatotoxic drugs acetaminophen, amiodarone, cyclophosphamide, ketoconazole,

nefazodone, troglitazone and the cofactors NAD+, NADPH, UDPGA, PAPS and GSH were obtained from

Sigma Aldrich (St. Louis, MO).

Cell Culture Reagents and Plates. Hepatocyte Induction Medium (HIM), Hepatocyte Incubation Medium

(HQM), and Universal Cryopreservation Plating Medium (UCPM) were obtained from In Vitro ADMET

Laboratories Inc. (Columbia, MD). 384-well white plates (Falcon® 384-Well Tissue Culture Treated

Microplates, us.vwr.com) and 75 cm2 tissue culture flasks (Falcon® Tissue Culture Flasks) were obtained

from VWR Inc. (www.usvwr.com). Trypsin (0.25% trypsin solution from bovine pancreas) was obtained

from Sigma Aldrich (St. Louis, MO).

MMHH. MMHH (lot number: PHHX8012) was prepared from cryopreserved human hepatocytes pooled

from 10 donors as previously described (Li et al., 2018). The demographics of the 10 donors are shown

in Table 1. For the MDCA assay, MMHH was supplemented with and without the following pathway-

specific cofactors for the evaluation of the roles of specific DME pathways on drug toxicity:

NADPH/NAD+ (2 mM) for phase 1 oxidative metabolism, UDPGA (1 mM) for UDP-


at ASPE



Dow

nloaded from

http://www.usvwr.com/


8

glucuronosyltransferase (UGT)-dependent glucuronidation, PAPS (0.1 mM) for sulfotransferases (SULT)-

dependent sulfate conjugation, and GSH (1 mM) for GST-dependent GSH conjugation.

HEK293 cells. HEK293 cells (human embryo kidney cells) were a gift from United States Environmental

Protection Agency. The cells were routinely maintained at approximately 50% confluency in UCPM in 75

cm2 tissue culture flasks in a cell culture incubator at 37 deg. C in a highly humidified atmosphere of 5%

CO2 and 95% air.

Evaluation of cofactor-directed acetaminophen metabolism by MMHH. Incubation of MMHH with

acetaminophen for the quantification of metabolite formation was performed as previously reported (Li

et al., 2018). Briefly, acetaminophen was dissolved in HQM at 2 mM (2X of the final concentration of 1

mM), added at a volume of 50 L per well in triplicate (metabolism wells) of a 96-well cell culture plate

(metabolism plate) and prewarmed to 37o C for approximately 15 minutes in a cell culture incubator.

Cofactor-supplemented MMHH (Li et al., 2018) was thawed from cryopreservation and placed in a cell

culture incubator to prewarm the reagent to approximately 37o C. Metabolism of acetaminophen was

initiated by adding 50 L of MMHH at a cell density of 2 x 106 cells/mL (2X of the final cell density of 1 x

106 cells/mL) into the metabolism wells (containing 50 L of 2 mM acetaminophen) and returned to the

cell culture incubator without shaking for 30 minutes. At the end of the incubation, 200 µL of

acetonitrile was added into each well for the termination of the metabolism. The 96-well plate was

stored in a -80o C freezer for later LC/MS-MS quantification of metabolite formation.

LC/MS/MS Quantification of Metabolite Formation. Upon thawing of the contents of the reaction plate,

100 µL aliquots of acetonitrile solution containing 250 nM of tolbutamide (internal standard) were

added to each of the metabolism wells followed by centrifugation at 3,500 rpm for 5 minutes to remove

the cellular debris. An aliquot of 100 µL of supernatant from each well was transferred to a 96 well plate

and was diluted with 200 µL of deionized water for the quantification of metabolites using an API 4000


at ASPE



Dow

nloaded from


9

QTRAP mass spectrometer with an electrospray ionization source (AB SCIEX, Framingham, MA)

connected to Agilent 1200 series HPLC (Agilent Technologies, Santa Clara, CA) using LC/MS/MS MRM

mode, monitoring the mass transitions (parent to daughter ion) (Table 2). An Agilent Zorbax Eclipse Plus

C18 column (4.6 x 75 mm i.d., 3.5 μm; Agilent Technologies, Santa Clara, CA) at a flow rate of 1 mL/min

was used for the chromatography separation. The mobile phase consisted of 0.1 % formic acid in

acetonitrile (A) and 0.1 % formic acid in water (B). The gradient for the positive ion mode operation was

programed as: 0 to 2.5 min, increase B from 5 to 95%; 2.5 to 3.5 min, 95% B; 3.5 to 3.6min, decrease B

to 5%; run-time 5 min. The gradient program for the negative ion mode was: 0 to 3 min, increase B from

5 to 95%; 3 to 4 min, 95% B; 4 to 4.2 min, decrease B to 5%; run-time 6 min. Data acquisition and data

processing were performed with the software Analyst 1.6.2 (AB SCIEX, Framingham, MA).

MDCA assay. The principle of the assay is to evaluate cytotoxicity of the various drugs in HEK293 in the

presence MMHH as an exogenous metabolic system, and in the presence and absence of cofactors for

specific metabolic pathways: NADPH/NAD+ for oxidative metabolism, UDPGA for UGT-mediated

glucuronidation, PAPS for SULT-mediated sulfation, and GSH for GST-mediated GSH conjugation. For the

assay, HEK293 cells were trypsinized from the stock cultures and suspended in UCPM. Cell density was

quantified using a hemocytometer and adjusted with UCPM to approximately 520000 cells per mL. A

volume of 10 L (containing approximately 5200 cells) was delivered into each of the wells in the 384-

well white plates (treatment plates) and placed in the cell culture incubator for approximately 4 hours to

allow cell attachment. The drugs to be evaluated were prepared in culture medium at 3 times of the

final desired concentrations (3X treatment media). Acetaminophen and cyclophosphamide were

dissolved directly in HIM. Amiodarone, ketoconazole, nefazodone, and troglitazone were firstly

dissolved in DMSO as 3000X stock solutions followed by 1:1000 (v/v) dilution in HIM to constitute the 3X

treatment media. Treatment was initiated by the addition of 10 L of MMHH supplemented with the

designated cofactors into each of the treatment wells containing HEK293 cells in 10 uL of culture


at ASPE



Dow

nloaded from


10

medium, followed by 10 L of the 3X treatment media. HIM was used as the negative control for

acetaminophen and cyclophosphamide. HIM with 0.1% DMSO was used as the negative control (solvent

control) for amiodarone, ketoconazole, nefazodone and troglitazone. The treatment plates were then

returned to the cell culture incubator for a treatment duration of 24 hours followed by viability

determination.

Quantification of cell viability. Viability of the HEK293 cells after treatment was determined via

quantification of cellular ATP contents using a luminescence-based reagent (Perkin Elmer ATPLite

Luminescence Assay System, www.perkinelmer.com). Luminescence was quantified using a Victor3V

Multiwell plate reader (Perkin Elmer, Waltham, MA, USA). Results are expressed as relative viability

using the following equation:

Relative viability (%) = (Luminescence (treatment))/Luminescence (solvent control)) x 100

Data Analysis.

1. Statistical analysis: Statistical analysis was performed using student’s t-test with the Microsoft

Excel 6.0 software, with the probability of p<0.05 to be considered statistically significant.

2. IC50 determination: Graphpad Prism 9.0.2 software was used for the determination of IC50

values from nonlinear regression analysis of plots of relative viability versus log drug

concentrations.

3. Metabolic dependent cytotoxicity index (MDCI) calculation: MDCI values were calculated as a

ratio of the IC50 values in the absence and presence of a cofactor for a specific pathway of drug

metabolism using the following equation,

MDCI = IC50 (without cofactor)/IC50 (with cofactor)


at ASPE



Dow

nloaded from

http://www.perkinelmer.com/


11

Metabolic activation is indicated by MDCI values > 1 (lower IC50 values in the presence of

cofactor than that without cofactor), and metabolic detoxification is indicated by MDCI values

<1 (higher IC50 values in the presence of cofactor than that without cofactor).

Results

Cofactor-specification of acetaminophen metabolism. Acetaminophen metabolism was evaluated in

MMHH supplemented with NADPH/NAD+ in the presence and absence of each of the phase 2

conjugating cofactors. Significantly higher rates of formation of glucuronide, sulfate, and GSH conjugate

were observed in the presence of UDPGA, PAPS, and GSH, respectively, than that in the absence the

cofactors (Figure 3). The relative rates of metabolite formation expressed as percentages of that in the

absence of each of the cofactors were 5721% ± 437% for UDPGA-mediated glucuronidation, 1411% ± 83%

for PAPS-mediated sulfation, and 2907% ± 466% for GST-mediated GSH conjugation. The results

demonstrate the specification of drug metabolism pathways by cofactor selection.

Cofactor-specification of drug toxicity in MDCA. The cytotoxicity of acetaminophen, amiodarone,

cyclophosphamide, ketoconazole, nefazodone, and troglitazone in the presence and absence of

NADPH/NAD+, and in the presence of NADPH/NAD+ with either UDPGA, PAPS, or GSH is as follows:

NADPH/NAD+: Statistically significant enhancement of cytotoxicity by NADPH/NAD+ was observed for

all six drugs (Figure 4) as demonstrated by decreases in IC50 values (Table 3). The MDCI values ranged

from 2.16 (amiodarone) to 5.48 (ketoconazole) (Table 4). The MDCI for nefazodone was determined to

be >4.95 due to the lack of cytotoxicity in the absence of NADPH, thereby not allowing an accurate

calculation of MDCI value for oxidative metabolism.

UDPGA: Statistically significant attenuation of cytotoxicity by UDGPA was observed for acetaminophen,

ketoconazole, nefazodone, and troglitazone at one or more of the drug concentrations evaluated (Figure

5), resulting in increases in IC50 values (Table 3). No attenuation was observed for amiodarone and


at ASPE



Dow

nloaded from


12

cyclophosphamide. The MDCI values ranged from 0.32 (acetaminophen) to >1 (amiodarone and

cyclophosphamide) (Table 4).

PAPS: Statistically significant attenuation of cytotoxicity by PAPS was only observed for acetaminophen

at the concentrations evaluated (Figure 6) resulting in an increase in IC50 value (Table 3). While none of

the other treatments yielded statistically significant attenuation by PAPS, increases in IC50 values were

also observed for ketoconazole and troglitazone (Table 3). The MDCI values ranged from 0.19

(acetaminophen) to 1.06 (cyclophosphamide) (Table 4).

GSH: Statistically significant attenuation of cytotoxicity by GSH was observed at multiple concentrations

for all drugs except amiodarone where attenuation was observed at only one concentration (Figure 7),

resulting in increases in IC50 values (Table 3). The MDCI values ranged from <0.15 (troglitazone) to 0.75

(amiodarone) (Table 4).

MDCI ranking. Ranking of the MDCI values of the six drugs for each of the cofactor-dependent

metabolic pathways is shown in Table 5, demonstrating the drug-dependent effects of each cofactor on

cytotoxicity. Ranking of the MDCI values for each detoxification pathway for each of the drugs is shown

in Table 6, demonstrating that GSH was the most effective cofactor for all six drugs in the attenuation of

drug toxicity.


at ASPE



Dow

nloaded from


13

Discussion

The major objective of our study was to investigate the utility of MDCA, a novel assay with HEK293as

target cells and MMHH supplemented by specific cofactors as an exogenous metabolic system, as an

experimental tool to define key metabolic activating and detoxifying pathways of drug toxicity. The

effectiveness of pathway specification by cofactor selection was clearly demonstrated by results with

acetaminophen metabolism where each of the cofactors NADPH /NAD+, UDPGA, PAPS, and GSH was

found to enable glucuronidation, sulfation, and GSH conjugation, respectively, at levels substantially

higher than that observed in the absence of the factors.

The higher cytotoxicity of acetaminophen in the presence of NADPH/NAD+ in MDCA is consistent with

the known metabolic activation of acetaminophen to the “ultimate” toxic metabolite, N-acetyl-p-

benzoquinone imine (NAPQI), by P450 isoforms CYP1A2 (Laine et al., 2009), CYP2D6 (Dong et al., 2000),

CYP2E1 (Laine et al., 2009), and CYP3A4 (Thummel et al., 1993; Laine et al., 2009). UDPGA and PAPS

attenuation of acetaminophen cytotoxicity observed in MDCA is consistent with the established

protective roles of UGT and SULT conjugation via removal of the parent molecule from metabolic

activation (Price et al., 1986; Ziemniak et al., 1987; Kane et al., 1990; Dalhoff and Poulsen, 1993; Court

and Greenblatt, 1997; Mutlib et al., 2006; McGill and Jaeschke, 2013). Attenuation of acetaminophen

cytotoxicity by GSH is consistent with the known detoxification of NAPQI via GSH conjugation (Mitchell

et al., 1973; Hoffmann and Baillie, 1988; Henderson et al., 2000; Zhao et al., 2002). An interesting

observation is that GSH supplementation resulted in an IC50 value of 13.98 mM which is 2.47X that in the

absence of NADPH. A plausible explanation of the observation is that by detoxifying NAPQI via GSH

conjugation, P450 metabolism serves as a detoxifying rather than activating pathway by reducing the

concentration and the accompanying cytotoxic effects of the parent chemical.


at ASPE



Dow

nloaded from


14

NADPH/NAD+ enhancement of amiodarone cytotoxicity is consistent with previous findings that CYP3A4

induction by rifampin resulted in its higher cytotoxicity in cultured human hepatocytes and that

cytotoxicity in HepG2 cells was enhanced via supplementation with exogenous CYP3A4 supersomes

(Zahno et al., 2011). GSH attenuation of amiodarone cytotoxicity is consistent with the detection of its

reactive metabolites in rat bile (Parmar et al., 2016) and upon incubation with human and rat liver

homogenates (Ramesh Varkhede et al., 2014). The lack of effects of UDPGA and PAPS on amiodarone in

MDCA suggest that the cytotoxic metabolites of amiodarone are not significantly detoxified by

glucuronidation and sulfation. As of this writing, there are no reports on the detoxification of

amiodarone by glucuronidation and sulfation.

Our observation of metabolic activation of cyclophosphamide by oxidative metabolism and

detoxification by GSH-conjugation is consistent with previous findings of its metabolism by various P450

isoforms to the toxic metabolites 4-hydroxycyclophosphamide, phosphoramide mustard, and acroleins

(Rodriguez-Antona and Ingelman-Sundberg, 2006; Doloff et al., 2010; Bachanova et al., 2015; Nishikawa

et al., 2015), and detoxification by GSH-conjugation and antioxidants (Dirven et al., 1994; DeLeve, 1996;

Terakura et al., 2020). Cyclophosphamide cytotoxicity was not attenuated by UDPGA and PAPS in

MDCA, suggesting that glucuronidation and sulfation are not detoxifying pathways.

Ketoconazole is now known to undergo complex metabolic schemes, resulting in myriads of metabolites.

CYP1A1 (Korashy et al., 2007), CYP3A4 (Fitch et al., 2009) and flavin-containing monooxygenase

(Rodriguez and Acosta, 1997; Rodriguez and Buckholz, 2003) have been reported to be involved in

ketoconazole metabolism. Our results with MDCA suggest that ketoconazole was metabolically

activated by oxidative metabolism and detoxified by glucuronidation, sulfation, and GSH conjugation.

Our results are consistent with a recent report showing that ketoconazole is metabolized to 28

metabolites, including 3 reactive metabolites based on cyanide trapping (Kim et al., 2017b). It is to be


at ASPE



Dow

nloaded from


15

noted that ketoconazole-GSH conjugates have not yet been identified. Further research will be

performed in our laboratory to evaluate if ketoconazole-GSH conjugates can be detected in MDCA.

Nefazodone cytotoxicity was enhanced by NADPH/NAD+ and attenuated by UDPGA and GSH, suggesting

that it is metabolically activated by oxidative metabolism, and detoxified by glucuronidation and GSH-

conjugation. The results are consistent with previous reports that it is metabolized by CYP3A4 to

quinone-imine, a reactive metabolite that is detoxified by GSH conjugation (Kalgutkar et al., 2005;

Bauman et al., 2008), but is different from that reported by Kostrubsky et al (Kostrubsky et al., 2006)

that in cultured human hepatocytes, inhibition of P450 metabolism by 1-aminobenzotriazole increased

its cytotoxicity. The difference between our results with MDCA and that reported for human

hepatocytes will be further evaluated in our laboratory.

Troglitazone cytotoxicity was enhanced by NADPH and attenuated by UDPGA, PAPS, and GSH, with GSH

being the most protective. NADPH/NAD+ enhancement of cytotoxicity and detoxification by GSH

provides clear evidence with that troglitazone can be metabolized by oxidative metabolism to cytotoxic

reactive metabolites, consistent with previous findings of its metabolic activation to reactive

metabolites involving the sulfur moiety of thiazolidinedione nucleus firstly reported by Kassahun et al.

(Kassahun et al., 2001) and subsequently confirmed by others (Prabhu et al., 2002; Saha et al., 2010;

Okada et al., 2011). That troglitazone metabolism to reactive metabolites and detoxification by GSH

conjugation are critical to the manifestation of liver injuries is also suggested by clinical data

demonstrating increased hepatotoxicity in glutathione-S-transferase (GST) deficient patients (Watanabe

et al., 2003), and in an in vitro study in which liver microsomes generated from GST-deficient donors had

increased covalent binding levels compared to that from normal livers (Usui et al., 2011). While

inhibition of the bile salt export pump by troglitazone sulfate resulting in accumulation of bile salt to

hepatotoxic levels has been postulated to be a mechanism of troglitazone hepatotoxicity (Yang and


at ASPE



Dow

nloaded from


16

Brouwer, 2014), our results with MDCA suggest that cytotoxic reactive metabolites may also be involved

in the clinically manifested liver injuries upon troglitazone administration.

A large majority of DILI drugs are known to be metabolized to highly reactive metabolites which, as a

result of covalent binding to key biological molecules, may lead to a cascade of events ultimately

resulting in severe liver toxicity (Gibaldi, 1992; Boelsterli, 2002; Li, 2002; Cho and Uetrecht, 2017).

Evaluation of reactive metabolite formation to minimize the risks of developing drugs with drug induced

liver injuries (DILI potential) was firstly applied in Merck Research Laboratories (Evans et al., 2004) and

now widely adopted in drug development. Commonly used approaches include Incubation of the

chemicals in question with human liver microsomes in the presence of a trapping agent such as

glutathione, followed by LC-MS/MS identification of the reactive metabolite-GSH conjugate (Ma and

Chowdhury, 2012; Huang et al., 2015; Hosaka et al., 2018; Paludetto et al., 2019) as well as

quantification of covalent binding to human liver microsomal proteins (Mitrea et al., 2010; Kakutani et

al., 2021). However, there are concerns with the inadvertent removal of drug candidates without

toxicological consequences with this approach (Obach et al., 2008; Kalgutkar and Didiuk, 2009). Our

results suggest that GSH attenuation of cytotoxicity in MDCA represents an experimental approach to

identify reactive metabolites with toxicological consequences, therefore allowing a more accurate

identification of structures with toxic liability.

We also introduce in this report a novel parameter, MDCI, calculated as a ratio of the IC50 values in the

absence to that in the presence of each of the cofactors, as a quantitative measure of the effects of each

drug metabolism pathway on toxicity. MDCI values of less than 1 indicates metabolic detoxification

(higher IC50 values in the presence of the cofactor), and that higher than 1 representing metabolic

activation (lower IC50 values in the presence of the cofactor). MDCI may be useful as a quantitative

parameter for the comparison of multiple drugs in the effects of a specific drug metabolism pathway on


at ASPE



Dow

nloaded from


17

toxicity (Table 5) as well as a comparison of the effects of various pathways on the toxicity of a specific

drug (Table 6).

Definition of the role of specific drug metabolizing enzyme pathways in drug toxicity, as described in this

report with MDCA, is an important aspect of drug development. The information may aid structural

optimization approaches to minimize toxicity (e.g., identification and removal of chemical moieties

required for cytotoxic reactive metabolite formation), identification of potential environmental factors

that may exacerbate drug toxicity (e.g., CYP3A4 induction resulting in enhanced metabolic activation)

and compromising metabolic detoxification (e.g. GSH depletion). In our laboratory, we will further

apply MDCA to evaluate additional biological molecules such as antioxidants as well as inflammatory

cytokines to further our understanding of the key determinants of drug toxicity. Lastly, our results

suggest that MMHH may be applicable as an exogenous hepatic metabolic system for in vitro toxicity

assays involving nonhepatic organ target cells to allow a more accurate assessment of in vivo drug

toxicity.

Authorship Contributions.

Participate in research design: Li, Wei

Conducted experiments: Wei

Contributed new reagents or analytical tools: Li, Wei

Performed data analysis: Wei, Li

Wrote or contribute to the writing of the manuscript: Wei, Li


at ASPE



Dow

nloaded from


18

References

Agozzino F, Picca M, and Pelosi G (2002) Acute hepatitis complicating intravenous amiodarone treatment. Ital Heart J 3:686-688.

Babatin M, Lee SS, and Pollak PT (2008) Amiodarone hepatotoxicity. Curr Vasc Pharmacol 6:228-236. Bachanova V, Shanley R, Malik F, Chauhan L, Lamba V, Weisdorf DJ, Burns LJ, and Lamba JK (2015)

Cytochrome P450 2B6*5 Increases Relapse after Cyclophosphamide-Containing Conditioning and Autologous Transplantation for Lymphoma. Biol Blood Marrow Transplant 21:944-948.

Baillie TA and Rettie AE (2011) Role of biotransformation in drug-induced toxicity: influence of intra- and inter-species differences in drug metabolism. Drug Metab Pharmacokinet 26:15-29.

Bauman JN, Frederick KS, Sawant A, Walsky RL, Cox LM, Obach RS, and Kalgutkar AS (2008) Comparison of the bioactivation potential of the antidepressant and hepatotoxin nefazodone with aripiprazole, a structural analog and marketed drug. Drug Metab Dispos 36:1016-1029.

Boelsterli UA (2002) Xenobiotic acyl glucuronides and acyl CoA thioesters as protein-reactive metabolites with the potential to cause idiosyncratic drug reactions. Curr Drug Metab 3:439-450.

Bouvet R, Cauchois A, Baert A, Fromenty B, Morel I, Turlin B, and Gicquel T (2020) Fatal acetaminophen poisoning with hepatic microvesicular steatosis in a child after repeated administration of therapeutic doses. Forensic Sci Int 310:110258.

Cho T and Uetrecht J (2017) How Reactive Metabolites Induce an Immune Response That Sometimes Leads to an Idiosyncratic Drug Reaction. Chem Res Toxicol 30:295-314.

Clark LL and Taubman SB (2016) Acetaminophen overdoses, active component, U.S. Armed Forces, 2006-2015. MSMR 23:16-19.

Court MH and Greenblatt DJ (1997) Molecular basis for deficient acetaminophen glucuronidation in cats. An interspecies comparison of enzyme kinetics in liver microsomes. Biochem Pharmacol 53:1041-1047.

Dalhoff K and Poulsen HE (1993) Synthesis rates of glutathione and activated sulphate (PAPS) and response to cysteine and acetaminophen administration in glutathione-depleted rat hepatocytes. Biochem Pharmacol 46:1295-1297.

DeLeve LD (1996) Cellular target of cyclophosphamide toxicity in the murine liver: role of glutathione and site of metabolic activation. Hepatology 24:830-837.

Dirven HA, van Ommen B, and van Bladeren PJ (1994) Involvement of human glutathione S-transferase isoenzymes in the conjugation of cyclophosphamide metabolites with glutathione. Cancer Res 54:6215-6220.

Doloff JC, Su T, and Waxman DJ (2010) Adenoviral delivery of pan-caspase inhibitor p35 enhances bystander killing by P450 gene-directed enzyme prodrug therapy using cyclophosphamide+. BMC Cancer 10:487.

Dong H, Haining RL, Thummel KE, Rettie AE, and Nelson SD (2000) Involvement of human cytochrome P450 2D6 in the bioactivation of acetaminophen. Drug Metab Dispos 28:1397-1400.

Evans DC, Watt AP, Nicoll-Griffith DA, and Baillie TA (2004) Drug-protein adducts: an industry perspective on minimizing the potential for drug bioactivation in drug discovery and development. Chem Res Toxicol 17:3-16.

Findor JA, Sorda JA, Igartua EB, and Avagnina A (1998) Ketoconazole-induced liver damage. Medicina (B Aires) 58:277-281.

Fitch WL, Tran T, Young M, Liu L, and Chen Y (2009) Revisiting the metabolism of ketoconazole using accurate mass. Drug Metab Lett 3:191-198.

Gibaldi M (1992) Adverse drug effect-reactive metabolites and idiosyncratic drug reactions: Part I. Ann Pharmacother 26:416-421.


at ASPE



Dow

nloaded from


19

Goda K, Takahashi T, Kobayashi A, Shoda T, Kuno H, and Sugai S (2016) Usefulness of in vitro combination assays of mitochondrial dysfunction and apoptosis for the estimation of potential risk of idiosyncratic drug induced liver injury. J Toxicol Sci 41:605-615.

Hansel S, Castegnaro M, Sportouch MH, De Meo M, Milhavet JC, Laget M, and Dumenil G (1997) Chemical degradation of wastes of antineoplastic agents: cyclophosphamide, ifosfamide and melphalan. Int Arch Occup Environ Health 69:109-114.

Hauser SL, Dawson DM, Lehrich JR, Beal MF, Kevy SV, Propper RD, Mills JA, and Weiner HL (1983) Intensive immunosuppression in progressive multiple sclerosis. A randomized, three-arm study of high-dose intravenous cyclophosphamide, plasma exchange, and ACTH. N Engl J Med 308:173-180.

Henderson CJ, Wolf CR, Kitteringham N, Powell H, Otto D, and Park BK (2000) Increased resistance to acetaminophen hepatotoxicity in mice lacking glutathione S-transferase Pi. Proc Natl Acad Sci U S A 97:12741-12745.

Hinson JA, Pumford NR, and Nelson SD (1994) The role of metabolic activation in drug toxicity. Drug Metab Rev 26:395-412.

Hoffmann KJ and Baillie TA (1988) The use of alkoxycarbonyl derivatives for the mass spectral analysis of drug-thioether metabolites. Studies with the cysteine, mercapturic acid and glutathione conjugates of acetaminophen. Biomed Environ Mass Spectrom 15:637-647.

Hosaka S, Honda T, Lee SH, and Oe T (2018) Biomimetic trapping cocktail to screen reactive metabolites: use of an amino acid and DNA motif mixture as light/heavy isotope pairs differing in mass shift. Anal Bioanal Chem 410:3847-3857.

Huang K, Huang L, and van Breemen RB (2015) Detection of reactive metabolites using isotope-labeled glutathione trapping and simultaneous neutral loss and precursor ion scanning with ultra-high-pressure liquid chromatography triple quadruple mass spectrometry. Anal Chem 87:3646-3654.

Kakutani N, Iwai T, Ohno Y, Kobayashi S, and Nomura Y (2021) Evaluation of covalent binding of flutamide and its risk assessment using (19)F-NMR. Xenobiotica 51:88-94.

Kalgutkar AS and Didiuk MT (2009) Structural alerts, reactive metabolites, and protein covalent binding: how reliable are these attributes as predictors of drug toxicity? Chem Biodivers 6:2115-2137.

Kalgutkar AS, Vaz AD, Lame ME, Henne KR, Soglia J, Zhao SX, Abramov YA, Lombardo F, Collin C, Hendsch ZS, and Hop CE (2005) Bioactivation of the nontricyclic antidepressant nefazodone to a reactive quinone-imine species in human liver microsomes and recombinant cytochrome P450 3A4. Drug Metab Dispos 33:243-253.

Kane RE, Tector J, Brems JJ, Li AP, and Kaminski DL (1990) Sulfation and glucuronidation of acetaminophen by cultured hepatocytes replicating in vivo metabolism. ASAIO Trans 36:M607-610.

Kang HM, Kang YS, Kim SH, Seong JK, Kang DY, Lee HY, and Lee BS (2007) Amiodarone-induced hepatitis and polyneuropathy. Korean J Intern Med 22:225-229.

Kassahun K, Pearson PG, Tang W, McIntosh I, Leung K, Elmore C, Dean D, Wang R, Doss G, and Baillie TA (2001) Studies on the metabolism of troglitazone to reactive intermediates in vitro and in vivo. Evidence for novel biotransformation pathways involving quinone methide formation and thiazolidinedione ring scission. Chem Res Toxicol 14:62-70.

Keefer C, Chang G, Carlo A, Novak JJ, Banker M, Carey J, Cianfrogna J, Eng H, Jagla C, Johnson N, Jones R, Jordan S, Lazzaro S, Liu J, Scott Obach R, Riccardi K, Tess D, Umland J, Racich J, Varma M, Visswanathan R, and Di L (2020) Mechanistic insights on clearance and inhibition discordance between liver microsomes and hepatocytes when clearance in liver microsomes is higher than in hepatocytes. Eur J Pharm Sci 155:105541.


at ASPE



Dow

nloaded from


20

Kim DE, Jang MJ, Kim YR, Lee JY, Cho EB, Kim E, Kim Y, Kim MY, Jeong WI, Kim S, Han YM, and Lee SH (2017a) Prediction of drug-induced immune-mediated hepatotoxicity using hepatocyte-like cells derived from human embryonic stem cells. Toxicology 387:1-9.

Kim JH, Choi WG, Lee S, and Lee HS (2017b) Revisiting the Metabolism and Bioactivation of Ketoconazole in Human and Mouse Using Liquid Chromatography-Mass Spectrometry-Based Metabolomics. Int J Mol Sci 18.

Koga T, Fujiwara R, Nakajima M, and Yokoi T (2011) Toxicological evaluation of acyl glucuronides of nonsteroidal anti-inflammatory drugs using human embryonic kidney 293 cells stably expressing human UDP-glucuronosyltransferase and human hepatocytes. Drug Metab Dispos 39:54-60.

Kolb NS, Hunsaker LA, and Vander Jagt DL (1994) Aldose reductase-catalyzed reduction of acrolein: implications in cyclophosphamide toxicity. Mol Pharmacol 45:797-801.

Korashy HM, Shayeganpour A, Brocks DR, and El-Kadi AO (2007) Induction of cytochrome P450 1A1 by ketoconazole and itraconazole but not fluconazole in murine and human hepatoma cell lines. Toxicol Sci 97:32-43.

Kostrubsky SE, Strom SC, Kalgutkar AS, Kulkarni S, Atherton J, Mireles R, Feng B, Kubik R, Hanson J, Urda E, and Mutlib AE (2006) Inhibition of hepatobiliary transport as a predictive method for clinical hepatotoxicity of nefazodone. Toxicol Sci 90:451-459.

Laine JE, Auriola S, Pasanen M, and Juvonen RO (2009) Acetaminophen bioactivation by human cytochrome P450 enzymes and animal microsomes. Xenobiotica 39:11-21.

Li AP (1984) Use of Aroclor 1254-induced rat liver homogenate in the assaying of promutagens in Chinese hamster ovary cells. Environ Mutagen 6:539-544.

Li AP (2002) A review of the common properties of drugs with idiosyncratic hepatotoxicity and the "multiple determinant hypothesis" for the manifestation of idiosyncratic drug toxicity. Chem Biol Interact 142:7-23.

Li AP, Ho MD, Amaral K, and Loretz C (2018) A Novel In Vitro Experimental System for the Evaluation of Drug Metabolism: Cofactor-Supplemented Permeabilized Cryopreserved Human Hepatocytes (MetMax Cryopreserved Human Hepatocytes). Drug Metab Dispos 46:1608-1616.

Li D, Knox B, Gong B, Chen S, Guo L, Liu Z, Tong W, and Ning B (2021) Identification of Translational microRNA Biomarker Candidates for Ketoconazole-Induced Liver Injury Using Next-Generation Sequencing. Toxicol Sci 179:31-43.

Liu W, Zhai X, Wang W, Zheng B, Zhang Z, Fan X, Chen Y, and Wang J (2018) Aldehyde dehydrogenase 2 activation ameliorates cyclophosphamide-induced acute cardiotoxicity via detoxification of toxic aldehydes and suppression of cardiac cell death. J Mol Cell Cardiol 121:134-144.

Ma S and Chowdhury SK (2012) Application of LC-high-resolution MS with 'intelligent' data mining tools for screening reactive drug metabolites. Bioanalysis 4:501-510.

Mak A, Kato R, Weston K, Hayes A, and Uetrecht J (2018) Editor's Highlight: An Impaired Immune Tolerance Animal Model Distinguishes the Potential of Troglitazone/Pioglitazone and Tolcapone/Entacapone to Cause IDILI. Toxicol Sci 161:412-420.

McCann J, Choi E, Yamasaki E, and Ames BN (1975) Detection of carcinogens as mutagens in the Salmonella/microsome test: assay of 300 chemicals. Proc Natl Acad Sci U S A 72:5135-5139.

McGill MR and Jaeschke H (2013) Metabolism and disposition of acetaminophen: recent advances in relation to hepatotoxicity and diagnosis. Pharm Res 30:2174-2187.

Mitchell AD, Auletta AE, Clive D, Kirby PE, Moore MM, and Myhr BC (1997) The L5178Y/tk+/- mouse lymphoma specific gene and chromosomal mutation assay a phase III report of the U.S. Environmental Protection Agency Gene-Tox Program. Mutat Res 394:177-303.

Mitchell JR, Jollow DJ, Potter WZ, Gillette JR, and Brodie BB (1973) Acetaminophen-induced hepatic necrosis. IV. Protective role of glutathione. J Pharmacol Exp Ther 187:211-217.


at ASPE



Dow

nloaded from


21

Mitrea N, LeBlanc A, St-Onge M, and Sleno L (2010) Assessing covalent binding of reactive drug metabolites by complete protein digestion and LC-MS analysis. Bioanalysis 2:1211-1221.

Mutlib AE, Goosen TC, Bauman JN, Williams JA, Kulkarni S, and Kostrubsky S (2006) Kinetics of acetaminophen glucuronidation by UDP-glucuronosyltransferases 1A1, 1A6, 1A9 and 2B15. Potential implications in acetaminophen-induced hepatotoxicity. Chem Res Toxicol 19:701-709.

Nebert DW, McKinnon RA, and Puga A (1996) Human drug-metabolizing enzyme polymorphisms: effects on risk of toxicity and cancer. DNA Cell Biol 15:273-280.

Nishikawa T, Miyahara E, Kurauchi K, Watanabe E, Ikawa K, Asaba K, Tanabe T, Okamoto Y, and Kawano Y (2015) Mechanisms of Fatal Cardiotoxicity following High-Dose Cyclophosphamide Therapy and a Method for Its Prevention. PLoS One 10:e0131394.

Obach RS, Kalgutkar AS, Soglia JR, and Zhao SX (2008) Can in vitro metabolism-dependent covalent binding data in liver microsomes distinguish hepatotoxic from nonhepatotoxic drugs? An analysis of 18 drugs with consideration of intrinsic clearance and daily dose. Chem Res Toxicol 21:1814-1822.

Okada R, Maeda K, Nishiyama T, Aoyama S, Tozuka Z, Hiratsuka A, Ikeda T, Kusuhara H, and Sugiyama Y (2011) Involvement of different human glutathione transferase isoforms in the glutathione conjugation of reactive metabolites of troglitazone. Drug Metab Dispos 39:2290-2297.

Ortega JA, Donaldson SS, Ivy SP, Pappo A, and Maurer HM (1997) Venoocclusive disease of the liver after chemotherapy with vincristine, actinomycin D, and cyclophosphamide for the treatment of rhabdomyosarcoma. A report of the Intergroup Rhabdomyosarcoma Study Group. Childrens Cancer Group, the Pediatric Oncology Group, and the Pediatric Intergroup Statistical Center. Cancer 79:2435-2439.

Paludetto MN, Puisset F, Chatelut E, and Arellano C (2019) Identifying the reactive metabolites of tyrosine kinase inhibitors in a comprehensive approach: Implications for drug-drug interactions and hepatotoxicity. Med Res Rev 39:2105-2152.

Parmar KR, Jhajra S, and Singh S (2016) Detection of glutathione conjugates of amiodarone and its reactive diquinone metabolites in rat bile using mass spectrometry tools. Rapid Commun Mass Spectrom 30:1242-1248.

Prabhu S, Fackett A, Lloyd S, McClellan HA, Terrell CM, Silber PM, and Li AP (2002) Identification of glutathione conjugates of troglitazone in human hepatocytes. Chem Biol Interact 142:83-97.

Price VF, Schulte JM, Spaethe SM, and Jollow DJ (1986) Mechanism of fasting-induced suppression of acetaminophen glucuronidation in the rat. Adv Exp Med Biol 197:697-706.

Ramesh Varkhede N, Jhajra S, Suresh Ahire D, and Singh S (2014) Metabolite identification studies on amiodarone in in vitro (rat liver microsomes, rat and human liver S9 fractions) and in vivo (rat feces, urine, plasma) matrices by using liquid chromatography with high-resolution mass spectrometry and multiple-stage mass spectrometry: characterization of the diquinone metabolite supposedly responsible for the drug's hepatotoxicity. Rapid Commun Mass Spectrom 28:311-331.

Rodriguez-Antona C and Ingelman-Sundberg M (2006) Cytochrome P450 pharmacogenetics and cancer. Oncogene 25:1679-1691.

Rodriguez RJ and Acosta D, Jr. (1997) N-deacetyl ketoconazole-induced hepatotoxicity in a primary culture system of rat hepatocytes. Toxicology 117:123-131.

Rodriguez RJ and Buckholz CJ (2003) Hepatotoxicity of ketoconazole in Sprague-Dawley rats: glutathione depletion, flavin-containing monooxygenases-mediated bioactivation and hepatic covalent binding. Xenobiotica 33:429-441.

Saha S, New LS, Ho HK, Chui WK, and Chan EC (2010) Investigation of the role of the thiazolidinedione ring of troglitazone in inducing hepatotoxicity. Toxicol Lett 192:141-149.


at ASPE



Dow

nloaded from


22

Spielberg SP (1984) In vitro assessment of pharmacogenetic susceptibility to toxic drug metabolites in humans. Fed Proc 43:2308-2313.

Stewart DE (2002) Hepatic adverse reactions associated with nefazodone. Can J Psychiatry 47:375-377. Subramaniam SR, Cader RA, Mohd R, Yen KW, and Ghafor HA (2013) Low-dose cyclophosphamide-

induced acute hepatotoxicity. Am J Case Rep 14:345-349. Takemura A, Izaki A, Sekine S, and Ito K (2016) Inhibition of bile canalicular network formation in rat

sandwich cultured hepatocytes by drugs associated with risk of severe liver injury. Toxicol In Vitro 35:121-130.

Terakura S, Onizuka M, Fukumoto M, Kuwatsuka Y, Kohno A, Ozawa Y, Miyamura K, Inagaki Y, Sawa M, Atsuta Y, Suzuki R, Naoe T, Morishita Y, Murata M, Nagoya B, and Marrow Transplantation G (2020) Analysis of glutathione S-transferase and cytochrome P450 gene polymorphism in recipients of dose-adjusted busulfan-cyclophosphamide conditioning. Int J Hematol 111:84-92.

Thummel KE, Lee CA, Kunze KL, Nelson SD, and Slattery JT (1993) Oxidation of acetaminophen to N-acetyl-p-aminobenzoquinone imine by human CYP3A4. Biochem Pharmacol 45:1563-1569.

Tuschl G, Lauer B, and Mueller SO (2008) Primary hepatocytes as a model to analyze species-specific toxicity and drug metabolism. Expert Opin Drug Metab Toxicol 4:855-870.

Usui T, Hashizume T, Katsumata T, Yokoi T, and Komuro S (2011) In vitro investigation of the glutathione transferase M1 and T1 null genotypes as risk factors for troglitazone-induced liver injury. Drug Metab Dispos 39:1303-1310.

Watanabe I, Tomita A, Shimizu M, Sugawara M, Yasumo H, Koishi R, Takahashi T, Miyoshi K, Nakamura K, Izumi T, Matsushita Y, Furukawa H, Haruyama H, and Koga T (2003) A study to survey susceptible genetic factors responsible for troglitazone-associated hepatotoxicity in Japanese patients with type 2 diabetes mellitus. Clin Pharmacol Ther 73:435-455.

Westlind A, Malmebo S, Johansson I, Otter C, Andersson TB, Ingelman-Sundberg M, and Oscarson M (2001) Cloning and tissue distribution of a novel human cytochrome p450 of the CYP3A subfamily, CYP3A43. Biochem Biophys Res Commun 281:1349-1355.

Wu IU, Tsai JH, and Ho CM (2021) Fatal acute-on-chronic liver failure in amiodarone-related steatohepatitis: a case report. BMC Gastroenterol 21:50.

Yang K and Brouwer KL (2014) Hepatocellular exposure of troglitazone metabolites in rat sandwich-cultured hepatocytes lacking Bcrp and Mrp2: interplay between formation and excretion. Drug Metab Dispos 42:1219-1226.

Zahno A, Brecht K, Morand R, Maseneni S, Torok M, Lindinger PW, and Krahenbuhl S (2011) The role of CYP3A4 in amiodarone-associated toxicity on HepG2 cells. Biochem Pharmacol 81:432-441.

Zhao P, Kalhorn TF, and Slattery JT (2002) Selective mitochondrial glutathione depletion by ethanol enhances acetaminophen toxicity in rat liver. Hepatology 36:326-335.

Ziemniak JA, Allison N, Boppana VK, Dubb J, and Stote R (1987) The effect of acetaminophen on the disposition of fenoldopam: competition for sulfation. Clin Pharmacol Ther 41:275-281.

Zollner E, Delport S, and Bonnici F (2001) Fatal liver failure due to ketoconazole treatment of a girl with Cushing's syndrome. J Pediatr Endocrinol Metab 14:335-338.


at ASPE



Dow

nloaded from


23

Footnotes

1Funding: No external funding was received for the reported research.

2Conflict of interest: The authors, Hong Wei and Albert P. Li, are employees of In Vitro ADMET

Laboratories (IVAL). IVAL is a commercial provider of MMHH.


at ASPE



Dow

nloaded from


24

Table 1. Donor demographics of the human hepatocytes used in the preparation of the permeabilized

human hepatocytes (Lot PHHX8012) used in the study. PHHX8012 was prepared by combining

hepatocytes from ten individual donors followed by permeabilization and cryopreservation.

Lot Number Ethnicity Gender Age

(years) Body Mass Index

HH1004 Caucasian Male 30 26.9

HH1006 Hispanic Male 65 37.0

HH1009 Caucasian Female 47 27.3



HH1016 Hispanic Female 64 24.7



HH1035 Caucasian Female 46 22.0



at ASPE



Dow

nloaded from


25

Table 2. LC/MS-MS parameters for the quantification of acetaminophen metabolites.

Marker Metabolite Analyzed

Ion Mode Application

Mass Transitions Monitoring

Acetaminophen Glucuronide

Negative m/z 326.0 to 150.0

Acetaminophen Sulfate


Acetaminophen Glutathione



at ASPE



Dow

nloaded from


26

Table 3. IC50, 95% CI, and R-square values of the various model toxicants in the presence of the various cofactors in the MMHH/HEK293 assay.

The values were derived by nonlinear regression analysis of the dose-response curves using Graphpad Prism 9.0.2 software. NA: Not applicable

due to the lack of a defined dose-response relationship

Chemical Acetaminophen Amiodarone

Cofactor Composition

None NADPH NADPH/UDPGA NADPH/PAPS NADPH/GSH None NADPH NADPH/UDPGA NADPH/PAPS NADPH/GSH

IC50 5.65 mM

1.28 mM 4.01 mM 6.60 mM 13.98 mM 107.3M 49.6M 45.79M 52.44M 66.21M

95% CI (profile

likelihood)

4.701 to

6.759 mM

0.8725 to 1.835 mM

3.300 to 4.85 mM 5

5.298 to 8.200 mM

10.59 to 19.10 mM

NA 42.94 to

57.34M

34.58 to

61.63M

41.10 to

67.61M

51.54 to

88.55M

R squared 0.932 0.8408 0.9426 0.8989 0.8596 0.5209 0.9272 0.7999 0.8248 0.732

Chemical Cyclophosphamide Ketoconazole



IC50 24.00 mM

6.39 mM 6.26 mM 6.04 mM 16.13 mM 103.20M 18.83M 28.45M 25.32M 44.36M

95% CI (profile

likelihood) NA

5.001 to 8.191 mM

5.066 to 7.773 mM

4.831 to 7.578 mM

11.35 to 23.06 mM

NA 15.81 to

22.48M

21.09 to

39.31M

21.29 to

30.30M

36.32 to

55.35M

R squared 0.5127 0.8919 0.9332 0.9184 0.6847 0.02197 0.9398 0.8237 0.9422 0.9175

Chemical Nefazodone Troglitazone



IC50 >100

M 20.21M 30.28M 19.33M 53.47M 191.00M 58.24M 121.40M 89.28M >400M

95% CI (profile

likelihood) NA

16.14 to

25.52M

21.09 to

47.22M

15.45 to

24.29M

40.62 to

75.42M

151.1 to

237.1M

45.77 to

73.75M

80.81 to

201.5M

70.57 to

113.8M NA


at ASPE



Dow

nloaded from


27

R squared -

0.00511 0.9033 0.763 0.8997 0.8235 0.8787 0.8889 0.7086 0.8879 0.5859


at ASPE



Dow

nloaded from


28

Table 4. Metabolic dependent cytotoxicity index (MDCI) values for NADPH/NAD+-dependent

oxidative metabolism (oxidative metabolism), UGT-dependent glucuronidation (UGT), PAPS-

dependent sulfation (SULT) and GST-dependent GSH conjugation (GST) for acetaminophen,

amiodarone, cyclophosphamide, ketoconazole, nefazodone, and troglitazone. MDCI is calculated as

the ratio of IC50 values in the absence and presence of the cofactors (in the absence and presence of

NADPH for oxidative metabolism; with NADPH and absence and presence of each of the phase 2

conjugating cofactors for UGT, SULT and GST). MDCI values greater than 1 indicates metabolic activation,

less than 1 indicates detoxification. (The IC50 value of nefazodone in the absence of NADPH was >100

uM, thereby yielding MDCE of oxidative metabolism (cytotoxicity in the presence of NADPH) of >4.95.

The IC50 of troglitazone in the presence of GSH was found to be >400 uM, thereby yielding MDCI of

<0.15).

Drug Metabolism Dependent Cytotoxicity Index (MDCI)

Oxidative Metabolism UGT SULT GST

Acetaminophen 4.41 0.32 0.19 0.09

Amiodarone 2.16 1.08 0.95 0.75

Cyclophosphamide 3.76 1.02 1.06 0.40

Ketoconazole 5.48 0.66 0.74 0.42

Nefazodone >4.95 0.67 1.05 0.38

Troglitazone 3.28 0.48 0.65 <0.15


at ASPE



Dow

nloaded from


29

Table 5. Rank order of the MCDI values of acetaminophen (APAP), amiodarone (AMD),

cyclophosphamide (CPA), ketoconazole (KTZ), nefazodone (NFZ) and troglitazone (TGZ) for the various

metabolic activation and detoxification pathways.

Metabolic Activation/Detoxification Pathways

Rank Order MDCI Ranking Description

Metabolic activation by oxidative metabolism

NFZ > KTZ > APAP > CPA > TGZ > AMD

Highest to lowest MDCI, representing highest to

lowest levels of metabolic activation

UGT detoxification APAP > TGZ >KTZ= NFZ > CPA, > AMD

Lowest to highest MDCI, representing highest to

lowest levels of detoxification

SULT detoxification APAP > TGZ > KTZ (No apparent SULT

detoxification was observed for AMD, NFZ, CPA, NFZ)



GST detoxification TGZ > APAP > CPA = KTZ = NFZ >

AMD




at ASPE



Dow

nloaded from


30

Table 6. Rank order of the MCDI values of the phase 2 detoxification pathways (UGT, SULT and GST)

for acetaminophen (APAP), amiodarone (AMD), cyclophosphamide (CPA), ketoconazole (KTZ),

nefazodone (NFZ) and troglitazone (TGZ).

DILI Drugs Rank Order MDCI Ranking

Description

Acetaminophen GST>SULT>UGT

Lowest to highest MDCI, representing

highest to lowest levels of detoxification

Amiodarone GST>SULT>UGT

Cyclophosphamide GST>UGT>SULT

Ketoconazole GST>UGT>SULT

Nefazodone GST>UGT>SULT

Troglitazone GST>UGT>SULT


at ASPE



Dow

nloaded from


31

Figure Legends

Figure 1: A schematic representation of the metabolism-dependent cytotoxicity assay (MDCA). MDCA

employs permeabilized human hepatocytes (MetMax Human Hepatocytes, MMHH) as an exogenoouse

metabolic system, and HEK293 cells as the target of cytotoxicity. Upon drug treatment, the final

cytotoxicity will be a result of metabolic clearance of the parent drug, metabolic activation of the parent

drug to cytotoxic metabolites, and the detoxification of the parent drug and its cytotoxic metabolites.

Figure 2. Chemical structures of the six DILI drugs evaluated in MDCA. Acetaminophen, amiodarone,

cyclophosphamide, ketoconazole, nefazodone and troglitazone are drugs known to be associated with

severe clinically significant liver toxicity resulting in deaths or a need for liver transplantation.

Figure 3. Rates of formation of acetaminophen metabolites in the presence of NADPH/NAD+ and each

of the phase 2 cofactors: UDPGA (top left); PAPS (top right) and GSH (bottom). Data represent mean ±

S.D. (n = 3). (*: statistically significant (p<0.05) difference in metabolite formation rates between

incubation with NADPH/NAD+ only and that with UDPGA, PAPS, and GSH)

Figure 4. Drug cytotoxicity in the presence (squares) and absence (circles) of the cofactor mixture for

oxidative metabolism, NADPH/NAD+. Data represent mean ± S.D. (n = 3). (*: statistically significant

(p<0.05) difference in cytotoxicity between treatment with and without NADPH/NAD+)

Figure 5. Drug toxicity in the presence of NADPH/NAD+ with (squares) and without (circles) UDPGA, the

cofactor for UGT-mediated glucuronidation. Data represent mean ± S.D. (n = 3). (*: statistically

significant (p<0.05) difference in cytotoxicity between treatment with and without UDPGA).

Figure 6. Drug toxicity in the presence of NADPH/NAD+ with (squares) and without (circles) PAPS, the

cofactor for SULT-mediated sulfation. Data represent mean ± S.D. (n = 3). (*: statistically significant

(p<0.05) difference in cytotoxicity between treatment with and without PAPS).


at ASPE



Dow

nloaded from


32

Figure 7. Drug toxicity in the presence of NADPH/NAD+ with (squares) and without (circles) GSH, the

cofactor for GSH-conjugation. Data represent mean ± S.D. (n = 3). (*: statistically significant (p<0.05)

difference in cytotoxicity between treatment with and without GSH).


at ASPE



Dow

nloaded from


Figure 1.

Metabolic Activation

Nontoxic Metabolites

Cytotoxic Interactions

Parent Drug

Target Cell (HEK293) Viability Evaluation Cytotoxic Interactions

Toxic Metabolites

Metabolic Detoxification

Drug Metabolizing Enzymes/cofactors

MMHH (Exogenous Metabolic System)


at ASPE



Dow

nloaded from


Figure 2

Acetaminophen Cyclophosphamide

Ketoconazole Nefazodone Troglitazone

Amiodarone


at ASPE



Dow

nloaded from


Figure 3

0

2

4

6

8

10

12

14

NADPH/NAD+ UDPGA PAPS GSH

M

eta

bo

lite

Fo

rmat

ion

(p

mo

l/m

in/m

illio

n h

ep

ato

cyte

s)

Acetaminophen Glucuronide

0

2

4

6

8

10

12

14

16


M

eta

bo

lite

Fo

rmat

ion

(p

mo

l/m

in/m

illio

n h

ep

ato

cyte

s)

Acetaminophen Sulfate

0

2

4

6

8

10

12

14

16

18

20


M

eta

bo

lite

Fo

rmat

ion

(p

mo

l/m

in/m

illio

n h

ep

ato

cyte

s)

Acetaminophen Glutathione

*

*

*


at ASPE



Dow

nloaded from


Fig. 4

0.0 0.5 1.0 1.5 2.0 2.5

0

50

100

150

200

Nefazodone

Log Concentration (µM)

Rela

tive V

iab

ilit

y (

%)

0.5 1.0 1.5 2.0 2.5 3.0

0

50

100

150

Troglitazone


Rela

tive V

iab

ilit

y (

%)

1.0 2.0 3.0 4.0 5.0

0

50

100

150

Acetaminophen


Rela

tive V

iab

ilit

y (

%)

1.0 2.0 3.0 4.0 5.0

0

50

100

150

200

Cyclophosphamide


Rela

tive V

iab

ilit

y (

%)

0.0 0.5 1.0 1.5 2.0 2.5

0

50

100

150

Amiodarone


Rela

tive V

iab

ilit

y (

%)

0.0 0.5 1.0 1.5 2.0 2.5

0

50

100

150

200

Ketoconazole


Rela

tive V

iab

ilit

y (

%)

*

* *

* * * *

*

* *

* *

* *

* *

*

*

* * *

* *


at ASPE



Dow

nloaded from


Fig. 5

2.0 3.0 4.0 5.0

0

50

100

150

Acetaminophen


Rela

tive V

iab

ilit

y (

%)

2.0 3.0 4.0 5.0

0

50

100

150

Cyclophosphamide


Rela

tive V

iab

ilit

y (

%)

0.0 0.5 1.0 1.5 2.0 2.5

0

50

100

150

Amiodarone


Rela

tive V

iab

ilit

y (

%)

0.0 0.5 1.0 1.5 2.0 2.5

0

50

100

150

Ketoconazole


Rela

tive V

iab

ilit

y (

%)

0.0 0.5 1.0 1.5 2.0 2.5

0

50

100

150

Nefazodone


Rela

tive V

iab

ilit

y (

%)

0.5 1.0 1.5 2.0 2.5 3.0

0

20

40

60

80

100

Troglitazone


Rela

tive V

iab

ilit

y (

%)

* *

*

* *

*

*


at ASPE



Dow

nloaded from


Figure 6

2 3 4 5

0

50

100

150

Acetaminophen


Rela

tive V

iab

ilit

y (

%)

0.0 0.5 1.0 1.5 2.0 2.5

0

50

100

150

Amiodarone


Rela

tive V

iab

ilit

y (

%)

1 2 3 4 5

0

50

100

150

Cyclophosphamide


Rela

tive V

iab

ilit

y (

%)

0.0 0.5 1.0 1.5 2.0 2.5

0

50

100

150

Ketoconazole


Rela

tive V

iab

ilit

y (

%)

0.0 0.5 1.0 1.5 2.0 2.5

0

50

100

150

Nefazodone


Rela

tive V

iab

ilit

y (

%)

0.5 1.0 1.5 2.0 2.5 3.0

0

20

40

60

80

100

Troglitazone


Rela

tive V

iab

ilit

y (

%)

* *

*

* *


at ASPE



Dow

nloaded from


Figure 7

2.0 3.0 4.0 5.0

0

50

100

150

Acetaminophen


Rela

tive V

iab

ilit

y (

%)

2.0 3.0 4.0 5.0

0

50

100

150

200

Cyclophosphamide


Rela

tive V

iab

ilit

y (

%)

0.0 0.5 1.0 1.5 2.0 2.5

0

50

100

150

Amiodarone


Rela

tive V

iab

ilit

y (

%)

0.0 0.5 1.0 1.5 2.0 2.5

0

50

100

150

Ketoconazole


Rela

tive V

iab

ilit

y (

%)

0.0 0.5 1.0 1.5 2.0 2.5

0

50

100

150

Nefazodone


Rela

tive V

iab

ilit

y (

%)

0.5 1.0 1.5 2.0 2.5 3.0

0

50

100

150

Troglitazone


Rela

tive V

iab

ilit

y (

%)

* *

*

* * *

*

*

*

* *

* *

* *

*

*

* * *

* *

*


at ASPE



Dow

nloaded from


Documents

Permeabilized cryopreserved human hepatocytes as an