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: [email protected]
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].