Systems Toxicology: Modelling Biomarkers of Glutathione Homeostasis

and Paracetamol Metabolism.Simone H. Stahl1, James W. Yates2, Andrew W. Nicholls3, J Gerry Kenna4, Muireann

Coen5, Fernando Ortega6, Jeremy K. Nicholson5, Ian D. Wilson5*

1 AstraZeneca, DMPK, Drug Safety and Metabolism, Alderley Park, Macclesfield, Cheshire,

SK10 4TG, UK2 AstraZeneca, DMPK, Oncology Innovative Medicines, Alderley Park, Macclesfield,

Cheshire, SK10 4TG, UK3 GlaxoSmithKline, Investigative Preclinical Toxicology, Park Road, Ware, Hertfordshire,

SG12 0DP, UK4 FRAME, Russell & Burch House, North Sherwood Street, Nottingham NG1 4EE, UK5 Department of Surgery and Cancer, Imperial College London, Exhibition Road, South

Kensington, London SW7 2AZ, UK6 Centre for Applied Pharmacokinetic Research, Manchester Pharmacy School, The

University of Manchester, Manchester, M13 9PT, UK.

Keywords

Toxicology, drug-induced liver injury, glutathione homeostasis, paracetamol.

Abstract

One aim of systems toxicology is to deliver mechanistic, mathematically rigorous, models

integrating biochemical and pharmacological processes that result in toxicity to enhance the

assessment of the risk posed to humans by drugs and other xenobiotics. The benefits of such

“in silico” models would be in enabling the rapid and robust prediction of the effects of

compounds over a range of exposures, improving in vitro-in vivo correlations and the

translation from preclinical species to humans. Systems toxicology models of organ

toxicities that result in high attrition rates during drug discovery and development, or post-

marketing withdrawals (e.g., drug-induced liver injury (DILI)) should facilitate the discovery

of safe new drugs. Here, systems toxicology as applied to the effects of paracetamol

(acetaminophen, N-acetyl-para-aminophenol (APAP)) is used to exemplify the potential of

the approach.

*Author for correspondence Phone no: 00 44 207 594 3225

Fax no: 00 44 207 594 3226

E-mail address: i.wilson@imperial.ac.uk

Introduction

Systems biology, systems pharmacology and systems toxicology are terms that are employed

in many different ways by their users and advocates. To those on the periphery, it can seem

that each “expert” in the field has their own definition of what they encompass. There also

seems to be a trend for many of those specialising in the various, and ever-expanding, omics

fields to assume (at least on grant applications) that whatever they are doing must be systems

biology as they are dealing with biological systems. As has been noted previously [1]

systems biology is commonly, but mistakenly, assumed to be aimed at a systems-level,

holistic understanding of a biological response. In fact this is the domain of physiology,

which is the science of the whole. At the other end of the biomolecular spectrum is

molecular biology which is the science of the parts. Systems biology on the other hand

brings together molecular biology and physiology via multi-level models that provide a

means of describing the interrelationships between them, linking biological function at the

level of network interactions. These network interactions begin at the level of the molecule,

are genome-wide and, through the emergent properties of the individual components of the

system, give rise to the observed functional biology of cells, organs and organisms. The same

principles apply to systems toxicology, adding an extra layer that considers the interactions

between xenobiotic stressors and the molecular networks.

Systems biologists, and systems toxicologists, can (at their extremes) be classified into one of

two groups. The first group comprises those who, on studying a biological model and

observing effects at various levels of biomolecular organisation (from genes to metabolites),

establish links between enzymes and pathways etc. By putting these effects into a qualitative

biological context a narrative is then constructed which attempts to explain how all of this

works in a “systems” context. The second group represents individuals who, on observing a

biological phenomenon, and espousing qualitative approaches as insufficiently rigorous,

adopt a more fundamentalist approach and proceed to replicate key features of the “system”

in silico, aiming to produce quantitative models that take account of the kinetics (Km, Vmax

and similar data) of the enzymes involved. Both approaches (the qualitative/narrative and

the quantitative/fundamentalist) have merit and, when used appropriately, can be

complementary. Thus the “narrative” description of biology, based on classical views of

cause and effect, is especially useful for a priori hypothesis generation and for the selection

and prioritisation of molecular processes and candidate network interactions which most

merit quantitative exploration. Conversely, the “fundamentalist” approach is required for

generation of mathematically rigorous computational models that describe and/or predict the

dynamics, duration and magnitude of effects that arise when the studied biological system is

perturbed.

It is important to recognise that fundamentalist models cannot be expected to be accurate in

the first instance and that they must always be tested experimentally, and then be refined, via

make/test iterations. Typically, this cyclical process incorporates mechanistically sound

modifications taking account of processes and interactions which were not considered

adequately in the first instance. Once an in silico model has been devised and refined

through this “evolutionary” process so that it delivers accurate predictions it can be used

prospectively to predict outcomes that otherwise would only be evident following studies

undertaken in vitro and in vivo in animals or humans. This in turn provides obvious benefits

due to the savings in time and resources that would otherwise be used conducting

unproductive in vitro and in vivo experiments.

Systems Biology of Glutathione Homeostasis and the Molecular (Systems) Toxicology of

Paracetamol

A useful model drug for developing systems toxicology approaches is provided by the

analgesic paracetamol (termed acetaminophen in the USA). Whilst paracetamol has an

excellent safety profile when ingested orally by normal adults at doses < 4 g/day, when taken

in overdose (which may be deliberate or inadvertent, since the drug is contained within many

over the counter remedies), it has the potential to cause severe liver injury that may result in

fatal liver failure. Consequently, paracetamol ingestion is a leading cause of DILI in the UK,

USA and many other countries [2]. At therapeutic doses the bulk of the drug is converted to

sulphate and glucuronide conjugates, with a smaller proportion metabolized via CYP2E1 to

the chemically reactive intermediate N-acetyl-para-benzoquinone imine (NAPQI). The small

amount of NAPQI formed (up to ca. 15% of the dose) is readily detoxified by cellular

glutathione. Toxicity arises because, when paracetamol is ingested at high doses (much in

excess of those required for a sufficient pharmacological effect), its normal pathways of safe

metabolic clearance (sulfation and glucuronidation) are saturated and large amounts NAPQI

are formed. This toxic intermediate, in turn, overwhelms the protective detoxifying capacity

of the hepatic glutathione system and triggers oxidative stress, organelle injury and ultimately

cell death [3]. Therefore, the identification of biomarkers which are indicative of hepatic

glutathione status and which can be used to aid the clinical treatment of patients who sustain,

or are at risk of, paracetamol induced liver damage, is an important unmet need. In addition,

high intracellular concentrations of glutathione play a vital role in neutralising reactive

metabolites produced in the liver following the biotransformation of many other drugs and

xenobiotics [4], raising the possibility that an improved understanding of hepatic glutathione

homeostasis could also have more general value.

Elevated amounts of the intermediary metabolite 5-oxoproline have been detected in urine

from animals given high paracetamol doses via the diet, via both direct chemical analysis and

metabonomic profiling [5,6]. In addition, a metabolomics study in mice highlighted the non-

sulphur-containing glutathione analogue ophthalmic acid as another potential marker of

paracetamol-induced liver toxicity [7]. Both 5-oxoproline and ophthalmic acid are associated

with the biosynthesis of glutathione via the γ-glutamyl cycle, either directly as intermediates

(5-oxoproline) or produced following glutathione depletion via its conjugation to NAPQI

(ophthalmic acid). Although they do not reveal anything about the nature of the sub-cellular

targets of NAPQI, where mitochondrial dysfunction seems to represent a major mechanism

of toxicity [8], these biomarkers provide insight into a critical physiological process by which

liver cells are protected from cellular stress and toxicity caused by paracetamol and by a

broad range of other compounds which cause liver toxicity via oxidative stress following

glutathione depletion. Nonetheless, when considering biomarkers of toxicity there are

several additional layers of “mechanistic” toxicology which need to be considered, as well

aseffects on the γ-glutamyl cycle. Therefore there have been a number of omics-based

“systems biology” studies, in addition to those highlighted above, which have aimed at

finding useful novel biomarkers e.g., [9-15].

However, even if we concentrate only on the response of the cell to reactive metabolites via

effects on the -glutamyl cycle, deeper questions remain that relate to how best to interpret

5- oxoproline and ophthalmic acid biomarker data. Since these metabolites are formed via

different enzymatic processes, it is conceivable that they might each reveal different phases

of the toxic insult. Most important is to understand whether these and other possible

biomarkers of glutathione production and depletion might be used clinically, to predict

individuals who ingest high doses of paracetamol and are at highest risk of acute liver failure.

Such knowledge might lead to improvements in the clinical management of paracetamol

overdose and in the risk assessment for new drugs and xenobiotics that cause toxicity through

the formation of reactive metabolites which deplete hepatocellular glutathione.

Consideration of the key role played by the γ-glutamyl cycle in paracetamol toxicity therefore

led us to develop a fundamentalist and mathematically rigorous systems model of this

pathway and of other mechanisms involved in glutathione homeostasis which could be

applied in vitro and in vivo. Our approach, which initially was based on a model devised by

Reed et al. [16] and is represented schematically in Figure 1, was used to model the effects

of paracetamol metabolism on cellular glutathione status.

Building and Refining the Model

Our initial model [17] was used to simulate the in vitro effects of liver cell exposure to

various concentrations of paracetamol on concentrations of glutathione, 5-oxoproline,

ophthalmic acid and various other intermediary metabolites. These simulations were

compared with data obtained experimentally in human hepatocyte-derived THLE (SV40-T-

antigen-immortalized human liver epithelial cells) cells stably transfected with cytochrome

P450 (CYP) 2E1, which is the enzyme primarily responsible for formation of NAPQI [18].

This work highlighted a critical limitation of the model. As shown in Figure 2, it failed to

predict the stimulation of glutathione biosynthesis which occurred in the cells. Biosynthesis

of glutathione is regulated by the enzyme γ-glutamylcysteine synthase. Quantification of the

activity of this enzyme in the cells revealed that this increased markedly in response to

exposure to paracetamol. When this observed up-regulation was factored into the model, the

resulting simulations matched closely with the measured concentrations of glutathione,

5-oxoproline and ophthalmic acid (Figure 3). Another important limitation of the initial

model was that it assumed all raw materials enter the system at a constant rate. De novo

glutathione biosynthesis requires availability of the essential amino acid methionine, but

methionine stores in vivo are limited and depleted rapidly at high paracetamol doses (due to

the formation of NAPQI). We investigated the effects of limiting methionine supply (via the

use of methionine-free culture medium) on experimentally determined glutathione,

5-oxoproline and ophthalmic acid concentrations in paracetamol exposed THLE cells. We

then compared these results with those predicted using the revised model, which took account

of induction of glutathione synthesis. The combination of modelling and model-led

experiments confirmed that, as expected, a further iteration of the model was needed to

adequately describe the observed changes in both 5-oxoproline and ophthalmic acid

concentrations when cells were exposed to paracetamol. For example, both in vitro and in

silico, paracetamol exposure caused increases in 5-oxoproline concentrations, which

correlated well with up-regulated glutathione biosynthesis and the continuing availability of

methionine, whereas ophthalmic acid concentrations increased with reduced availability of

methionine and declining concentrations of glutathione. Thus, by determining the

concentrations of both of these two biomarkers in the medium it was possible to deduce the

intracellular glutathione concentration. The in silico systems biology model of glutathione

biosynthesis that was developed in this way was combined with a physiologically based

pharmacokinetic (PBPK) model of paracetamol disposition [19]. This has provided

predictions of the in vivo interplay between paracetamol exposure, concentrations of

5-oxoproline and ophthalmic acid in plasma and hepatic glutathione status [19], the accuracy

of which are being evaluated currently in a rat model.

Other Systems Biology Models for Paracetamol

For those interested in understanding the potential of paracetamol to cause liver injury in

humans, our model of effects on hepatic glutathione status and on concentrations of related

intermediary metabolites clearly addresses only part of the story. In view of the large body of

knowledge acquired on disposition and biotransformation of the drug, one obvious alternative

approach is to examine the problem from a more drug-centric point of view. In this vein,

Ben-Shachar et al. [20] described a whole body model for human paracetamol disposition

that was based on published parameters describing its transport and metabolism in the liver

and peripheral tissues. This approach predicted concentrations of paracetamol and its

biotransformation metabolites (sulphate, glucuronide and NAPQI-derived quinone-imine-

glutathione conjugates) in plasma and urine which matched well with experimental and

clinical data The model of paracetamol disposition was also connected to a model of

glutathione metabolism previously described by the same group [16], which enabled

simulation of the dose dependent depletion of glutathione that occurred following

paracetamol administration. The predictions also indicated that a therapeutic dose of

paracetamol of 4 g was expected to cause a modest decrease (10%) in hepatic glutathione

concentration, whereas doses of 10 g or greater were expected to cause toxicologically

significant levels of glutathione depletion (70% or greater). Moreover, chronic

administration of therapeutic paracetamol doses (e.g. 1 g every 6 hours for 10 days) were

predicted to cause notable and potentially concerning reductions in liver glutathione

concentration (of up to 30%). In these studies, the modelled and experimentally determined

plasma glutathione concentrations showed good agreement. In addition, the model was used

to consider the effectiveness of administration of N-acetyl-cysteine to patients who had taken

high paracetamol doses, in order to aid replenishment of glutathione and reduce the

likelihood of fatal liver failure. It was predicted that repletion of liver glutathione stores via

de novo biosynthesis would require several days.

Recently, a consortium has been formed to support the development and evaluation of a

mathematically and physiologically rigorous systems model of DILI caused by paracetamol

and other compounds (www.dilisym.com). In addition to reactive metabolite mediated

glutathione depletion, of the type shown by paracetamol, the model that is being developed

(DILIsym®) takes account of a variety of other potential mechanisms that may initiate liver

injury, including mitochondrial dysfunction and inhibition of the activity of the bile salt

export pump. The utility of the model for simulating reactive metabolite mediated toxicity

has been tested by exploring possible hypotheses which could explain why liver injury is

evident in mice given paracetamol, but not in mice given equivalent doses of a structurally

very similar regioisomer (3-hydroxyacetanilide, N-acetyl-meta-aminophenol (AMAP)) which

exhibits qualitatively similar pathways of drug metabolism [21]. Using published data on the

metabolism and toxicity of paracetamol and AMAP, four plausible hypotheses were

evaluated: (1) quantitative differences in biotransformation profiles between the two

compounds; (2) equivalent biotransformation via glucuronidation and sulfation, but markedly

reduced CYP2E1-mediated bioactivation of AMAP; (3) a greater extent of glutathione

depletion by NAPQI, when compared with the equivalent reactive intermediate of AMAP;

(4) less potent disruption of cellular processes caused by the AMAP reactive intermediate, on

a molar basis. Based on the outcomes of the simulations, hypotheses 1 and 2 (which implied

smaller amounts of reactive metabolites per mole of AMAP compared to APAP) were

favoured over hypotheses 3 and 4, while hypotheses 1 and 2 were considered equally likely

[21].

Where Next?

It is a truth, widely acknowledged, that a molecular toxicologist in possession of a novel

toxicity must be in search of an explanatory mechanism. The examples provided above

provide cause for cautious optimism that the mathematical modelling of aspects of

glutathione biosynthesis and APAP metabolism can lead to valuable new insights into the

toxicology of a much studied and widely used, but not necessarily well understood, drug.

The benefits of such models, when validated, are clear as they enable many “what if?”

“thought experiments” to be performed rapidly in silico, without the need for potentially

expensive and time consuming laboratory studies (in vitro or in vivo). Some examples could

be the in silico evaluation of the impacts of diet, of co-medications that also form reactive

metabolites, different expression levels of CYP proteins etc. The output provided by such

models has the potential to enable prioritisation of “wet” studies that are most likely to yield

the most useful data. On the basis of predictions of the models, the investigator can be

guided to perform only those “wet” studies that will provide the maximum return for

investigating the hypothesis. One especially useful features of such models is the manner in

which they can be adapted to investigate and understand new observations. As such, we

believe that the systems biology approach has much to offer to the modern toxicologist.

Indeed, “systems toxicology” is beginning to be used in both “narrative” and

“fundamentalist” contexts. Our studies of the causes of the elevated concentrations of 5-

oxoproline [5] and ophthalmic acid [6] observed by other investigators in rodent studies on

the effects of paracetamol involved initial generation of a plausible biochemical pathway

“narrative”. However, only by subsequently applying a more “fundamentalist” and

mathematically rigorous systems approach were we able to generate quantitative predictions

that could be verified by experiment. Narrative and fundamentalist systems toxicology

approaches therefore should be viewed as representing a continuum. Their use in risk

assessment has been recognised recently by Sturla and colleagues [22] as requiring a phased

approach (using mode of action and adverse outcome pathways, which are concepts more

readily aligned with traditional risk assessments) to ensure the development of sufficient

experience and training of risk assessors.

Declaration of Interest.

SHS and JWY are employees of AstraZeneca (AZ), AWN is an employee of

GlaxoSmithKline (GSK). JGK has previously acted as a paid scientific adviser to DILI-sim.

AZ and GSK provide financial support to DILI-sim as members of this initiative.  The other

authors declare no conflict of interest

References

[1] Alberghina L, Westerhoff H. Systems Biology: Did we know it all along?, Systems

biology: definitions and perspectives. Topics in Current Genetics 2005; 13: 3-9.

[2] Lee WM. Acetaminophen toxicity: changing perceptions on a social/medical issue.

Hepatology 2007;46:966-970.

[3] Mitchell JR, Jollow DJ, Potter WZ, Gillette JR, Brodie BB. Acetaminophen-induced

hepatic necrosis. IV. Protective role of glutathione. J Pharmacol Exp Ther 1973;187:211-217.

[4] Ketterer B, Coles B, Meyer DJ. The role of glutathione in detoxification. Environ Heal

Perspect 1983;49:59–69.

[5] McLean AEM, Armstrong GR, Beales D. Effect of D- or L-methionine and cysteine on

the growth inhibitory effects of feeding 1% paracetamol to rats. Biochem Pharmacol

1989;38:347-352.

[6] Ghauri FY, McLean AE, Beales D, Wilson ID, Nicholson JK. Induction of 5-

oxoprolinuria in the rat following chronic feeding with N-acetyl 4-aminophenol

(paracetamol). Biochem Pharmacol 1993;46:953-957.

[7] Soga T, Baran T, Suematsu M, Ueno Y, Ikeda S, Sakurakawa T, et al. Differential

metabolomics reveals ophthalmic acid as an oxidative stress biomarker indicating hepatic

glutathione consumption. J Biol Chem 2006; 281:16768–16776.

[8] Jaeschke H, McGill MR, Ramachandran A. Oxidant stress, mitochondria, and cell death

mechanisms in drug-induced liver injury: lessons learned from acetaminophen hepatotoxicity.

Drug Metab Rev 2012;44:88-106.

[9] Sun J, Schnackenberg LK, Holland RD, Schmitt TC, Cantor GH,Dragan YP, et al.

Metabonomics evaluation of urine from rats given acute and chronic doses of acetaminophen

using NMR and UPLC/MS. J Chromatogr B Analyt Technol Biomed Life Sci 2008;

871:328–340.

[10] Chen C,  Krausz KW, Shah YM, Jeffrey R, Idle JR, Gonzalez FJ. Serum Metabolomics

Reveals Irreversible Inhibition of Fatty Acid β-Oxidation through the Suppression of PPARα

Activation as a Contributing Mechanism of Acetaminophen-Induced Hepatotoxicity. Chem

Res Toxico. 2009;22:699–707.

[11] Coen M, Lenz EM, Nicholson JK, Wilson ID, Pognan F, John C, Lindon JC. An

Integrated Metabonomic Investigation of Acetaminophen Toxicity in the Mouse Using NMR

Spectroscopy. Chem Res Toxicol 2003;16:295–303.

[12] Schnackenberg LK, Chen M, Sun J, Holland RD, Dragan Y, Tong W, et al. Evaluations

of the trans-sulfuration pathway in multiple liver toxicity studies. Toxicology and Applied

Pharmacology 2009;235:25–32.

[13] Ruepp SU, Tonge RP, Shaw J, Wallis N, Pognan F. Genomics and Proteomics Analysis

of Acetaminophen Toxicity in Mouse Liver. Toxicol Sci 2002;65:135-150.

[14] Coen M, Ruepp SU, Lindon JC, Nicholson JK, Pognan F, Lenz EM, Wilson ID.

Integrated application of transcriptomics and metabonomics yields new insight into the

toxicity due to paracetamol in the mouse. J Pharm Biomed Anal 2004;35:93–105.

[15] Sun J, Ando Y, Ahlbory-Dieker D, Schnackenberg LK, Yang X, Greenhaw J et al.

Systems Biology Investigation to Discover Metabolic Biomarkers of Acetaminophen-Induced

Hepatic Injury Using Integrated Transcriptomics and Metabolomics. J Mol Biomark Diagn

2013;S1:002. doi:10.4172/2155-9929.S1-002.

[16] Reed MC, Thomas RL, Pavisic J, James SJ, Ulrich CM, Nijhout HF. A mathematical

model of glutathione metabolism. Theor Biol Med Model 2008;28:5:8.

[17] Geenen S, Taylor PN, Snoep JL, Wilson ID, Kenna JG, Westerhoff HV. Systems

biology tools for toxicology. Arch Toxicol 2012;86:1251-71.

[18] Geenen S, du Preez FB, Snoep JL, Foster AJ, Sarda S, Kenna JG, et al. Glutathione

metabolism modeling: a mechanism for liver drug-robustness and a new biomarker strategy.

Biochim Biophys Acta 2013 ;1830:4943-59.

[19] Geenen S, Yates JW, Kenna JG, Bois FY, Wilson ID, Westerhoff HV. Multiscale

modelling approach combining a kinetic model of glutathione metabolism with PBPK models

of paracetamol and the potential glutathione-depletion biomarkers ophthalmic acid and 5-

oxoproline in humans and rats. Integr Biol (Camb) 2013;5:877-88.

[20] Ben-Shachar R, Chen Y, Luo S, Hartman C, Reed M, Nijhout HF. The biochemistry of

acetaminophen hepatotoxicity and rescue: a mathematical model. Theor Biol Med Model

2012;9:55.

[21] Howell BA, Siler SQ, Watkins PB. Use of a systems model of drug-induced liver injury

(DILIsym®) to elucidate the mechanistic differences between acetaminophen and its less-

toxic isomer, AMAP, in mice. Toxicology Letters 2014;226:163–172.

[22] Sturla SJ, Boobis AR, FitzGerald RE, Hoeng J, Kavlock RJ, Schirmer K, et al. Systems

toxicology: from basic research to risk assessment. Chem Res Toxicol .2014;27:314-329.

Figure 1. Schematic representation of the metabolic network leading to gluthatione synthesis and consumption. The network includes methionine catabolism, gluthatione metabolism, 5-oxoproline, ophthalmic acid synthesis and gluthatione mediated detoxification. The blue and red shadings represent the intracellular and extracellular compartments respectively. The enzymes, transport processes across compartments and other processes are shown by their correspond acronyms in italics letters. Descriptions of the acronyms are the following: methionine adenosyl transferase-I (mati), methionine adenosyl transferase-III (matiii), glycine N-methyltransferase (meth), DNA-methyltransferase (gnmt), S-adenosylhomocysteine hydrolase (ah), betaine-homocysteine methyltransferase (bhmt), methionine synthase (ms), cystathionine gamma-synthase (cbs), cystathionase (ctgl), glutamylcysteine synthetase (gcs), glutathionesynthetase (gs), glutathione peroxidase (gpx), glutathione reductase (gr), glutaminase (glmn), 5-oxoprolinase (op), glutamylcysteine synthetase (gcs), aminopeptidase (ap), gamma-glutamylcyclotransferase (ggct), glutathione S-transferase (gpx), protein synthesis (protsyn), protein degradation (protdeg), methionine influx (metin), ophthalmic acid transporter (opatrs), cysteine influx (cysin), glutamate influx (glutin), 5-oxoprolinase transporter (oxotrs), glutamyl-amino acid transport (gluAAtrs), glycine influx (glyin), glutathione degradation (gshdeg), gluthathione disulphide (gssgout_l and gssgout_h) and glutathione (gshout_l, gshout_h) transports.

A

B0

50

100

150

200

250

0 5 25

Conc

entr

ation

(μM

)

Paracetamol mM

Adenosyl homocysteineExperimental

Predicted

0

100

200

300

400

500

600

700

0 5 25

Conc

entr

ation

(μM

)

Paracetamol mM

Cysteine Experimental

Predicted

0

0.1

0.2

0.3

0.4

0.5

0.6

0 5 25

Conc

entr

ation

(μM

)

Paracetamol mM

Ophthalmic acid Experimental

Predicted

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0 5 25

Conc

entr

ation

(μM

)

Paracetamol mM

5-Oxoproline Experimental

Predicted

0

10

20

30

40

50

60

70

80

0 5 25

Conc

entr

ation

(μM

)

Paracetamol mM

Para-conjugate Experimental

Predicted

0

1000

2000

3000

4000

5000

6000

7000

0 5 25

Conc

entr

ation

(μM

)

Paracetamol mM

GlutathioneExperimental

Predicted

0

50

100

150

200

250

300

350

0 5 25

Conc

entr

ation

(μM

)

Paracetamol mM

Methionine Experimental

Predicted

0

100

200

300

400

500

600

0 5 25

Flux

(μM

/hr)

Paracetamol mM

v1 glutamate in Experimental

Predicted

0

0.1

0.2

0.3

0.4

0.5

0.6

0 5 25

Flux

(μM

/hr)

Paracetamol mM

v3 Ophthalmic acid out Experimental

Predicted

0

50

100

150

200

250

300

350

400

0 5 25

Flux

(μM

/hr)

Paracetamol mM

v4 Para-conjugate out Experimental

Predicted

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 5 25

Flux

(μM

/hr)

Paracetamol mM

v5 5-Oxoproline out Experimental

Predicted

0

100

200

300

400

500

600

700

800

0 5 25

Flux

(μM

/hr)

Paracetamol mM

v6 Methionine in Experimental

Predicted

0

50

100

150

200

250

0 5 25

Flux

(μM

/hr)

Paracetamol mM

v7 to protein Experimental

Predicted

0

100

200

300

400

500

600

700

800

0 5 25

Flux

(μM

/hr)

Paracetamol mM

vCBS Experimental

Predicted

0

100

200

300

400

500

600

700

800

0 5 25

Flux

(μM

/hr)

Paracetamol mM

vGCS Experimental

Predicted

Figure 2A and 2B. Model predictions of the production of 5-oxoproline and glutathione by

THLE cells exposed to paracetamol at 0, 5 and 25 mM. Modified from [18].

A

B

Figure 3A and B. Model predictions of the production of 5-oxoproline and glutathione by

THLE cells exposed to paracetamol at 0, 5 and 25 mM with the up-regulation of the activity

of the enzyme γ-glutamylcysteine synthase factored into the model. Modified from [18].