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TOXICOKINETICS AND METABOLISM
The risky cocktail: what combination effects can we expect
between ecstasy and other amphetamines?
Diana Dias da Silva •Helena Carmo •
Elisabete Silva
Received: 14 June 2012 / Accepted: 27 August 2012 / Published online: 23 September 2012
ÓSpringer-Verlag 2012
Abstract The recreational and illicit use of amphetami-
nic designer compounds, specially 3,4-methylenediox-
ymethamphetamine (MDMA; Ecstasy), is of concern
worldwide. Such psychostimulating drugs are frequently
present as complex mixtures in ‘rave’ pills, making con-
comitant polysubstance use a common trend. However, the
understanding of possible combination effects with these
substances is still scarce. The present study was aimed at
predicting the cytotoxic effects of mixtures of four am-
phetaminic derivatives: MDMA, methamphetamine,
4-methylthioamphetamine and d-amphetamine in a human
hepatoma cell line. Concentration–response curves for all
single-mixture components were recorded by the MTT
assay. Data obtained for individual agents were then used
to compute the additivity expectations for mixtures of
definite composition, using the pharmacological models of
concentration addition (CA) and independent action.By
comparing the predicted calculations with the experimen-
tally observed effects, we concluded that CA accurately
predicts the combination of amphetamines, which act
together to generate additive effects over a large range of
concentrations. Notably, we observed substantial mixture
effects even when each drug was present at low concen-
trations, which individually produced unnoticeable effects.
Nonetheless, for all tested mixtures, a small deviation from
additivity was observed towards higher concentrations,
particularly at high effect levels. A possible metabolic
interaction, which could explain such deviation, was
investigated, and it was observed that at higher mixture
concentrations increased MDMA metabolism could be
contributing to divergences from additivity. In conclusion,
the present work clearly demonstrates that potentially
harmful interactions among amphetaminic drugs are
expected when these drugs are taken concomitantly.
Keywords 3,4-methylenedioxymethamphetamine
(ecstasy, MDMA) Amphetamine-related toxicity
Hepatocytes Combination effects Concentration
addition (CA) Independent action (IA)
Introduction
Amphetamine designer drugs are widely abused addictive
psychostimulants. 3,4-methylenedioxymethamphetamine
(MDMA), commonly known as ecstasy, is the most pop-
ular analogue and its use has increased in all social settings,
all over the world. As a result, ecstasy has often been
associated with toxic episodes, including fulminant
hyperthermia, disseminated intravascular coagulation,
rhabdomyolysis and multi-organ failure (Walubo and Seger
1999).
Reported drug abuse scenarios show that it is common
practice among misusers to consume multiple substances
D. Dias da Silva
Faculdade de Medicina, Universidade do Porto, Alameda Prof.
Herna
ˆni Monteiro, 4200-319 Porto, Portugal
D. Dias da Silva
UCL School of Pharmacy, University College London,
29-39 Brunswick Square, London WC1N 1AX, UK
D. Dias da Silva H. Carmo
REQUIMTE (Rede de Quı
´mica e Tecnologia), Laborato
´rio de
Toxicologia, Departamento de Cie
ˆncias Biolo
´gicas,
Faculdade de Farma
´cia, Universidade do Porto, Rua Anı
´bal
Cunha n.8164, 4050-047 Porto, Portugal
D. Dias da Silva E. Silva (&)
Institute for the Environment, Brunel University, Kingston Lane,
Uxbridge, Middlesex UB8 3PH, UK
e-mail: elisabete.silva@brunel.ac.uk
123
Arch Toxicol (2013) 87:111–122
DOI 10.1007/s00204-012-0929-9
concomitantly (Barrett et al. 2006; Wu et al. 2006;
Mohamed et al. 2011). In addition to the deliberate intake
of different types of drugs by the users, inadvertent con-
sumption of multiple substances often occurs, as large
number of other chemicals are regularly found in ecstasy
party pills (Pavlic et al. 2010; Morefield et al. 2011),
including lysergic acid diethylamide (LSD), dextroam-
phetamine (d-AMP), methamphetamine (METH), keta-
mine, mephedrone, cocaine and even the highly toxic
4-methylthioamphetamine (4-MTA), which has been
linked to several fatalities (Elliott 2000; De Letter et al.
2001). In fact, polydrug abuse is one of the most pertinent
confounding factors in predicting MDMA toxicity, since
the combination with other chemicals can exacerbate the
severity or widen the range of the toxic effects of this drug,
resulting in potentially lethal intoxications (De Letter et al.
2006; Verschraagen et al. 2007). Nevertheless, when
evaluating MDMA toxicity, most studies focus on MDMA
alone rather than in combination with other substances.
Moreover, the few combination studies reported so far
between MDMA and other psychoactive drugs have been
conducted without reference to the expected joint effects
(Clemens et al. 2005; Pontes et al. 2008). Consequently,
whilst the risk of interaction between MDMA and other
stimulants has been widely acknowledged, there is still a
lack of information regarding the toxicity and lethality of
drugs in co-administration. To define the way in which
amphetamine-like substances interact may represent an
important improvement for understanding their toxicity
mechanisms.
Over the last decades, several studies on mixture toxi-
cology have compared two well-established models for the
calculation of expected additive mixture effects (Drescher
and Boedeker 1995; Payne et al. 2000; Rajapakse et al.
2001; Silva et al. 2002; Pavlaki et al. 2011): concentration
addition (CA), first defined by Loewe and Muchnik (1926),
and independent action (IA) as described by Bliss (1939).
The concept of CA is based on the assumption that the
mixture constituents have similar modes of action, which
means that any component can be replaced partially or
totally with another without changing the overall mixture
effect. This means that each individual component con-
tributes to the global joint effect by acting in proportion to
its concentration, even below concentrations producing no
effect. This model has been used to assess combination
effects of agents with a common site of action (Backhaus
et al. 2000b; Silva et al. 2011a). Experimental evidence
from some studies showed that combination effects of
drugs with dissimilar mechanisms of action are better
described using the alternative approach, that is, IA, which
considers each agent interacting at differing sites of action
(Backhaus et al. 2000a). The fractional response of one
individual component is supposed to be independent from
those induced by other components, presuming that mix-
ture components present at zero effect concentrations will
not contribute to the overall effect.
The two models can produce very distinct expectations
and, to our knowledge, have never been applied to
amphetamine-like compounds. For this reason, the main
aim of this study was to compare the applicability of CA
and IA models in predicting the joint toxic effects of am-
phetaminic drugs in immortalized hepatoma Hep G2 cells,
based on comprehensive information on the individual
drugs. In addition to MDMA, the amphetamines d-AMP,
METH and 4-MTA were selected for this study, due to
their widespread presence in ecstasy pills. As amphetamine
derivatives are often found as low-level contaminants in
street drugs offered as ecstasy (Becker et al. 2003), we
were also interested in investigating the potential for sig-
nificant joint effects to occur, even when these individual
components were combined at low concentrations, repre-
sentative of real exposure scenarios. In order to address
these questions, three specifically designed mixtures of the
same four amphetamines, but combined at three different
ratios, were tested. With these, we were able to compare
the applicability of the described prediction models and
evaluate amphetamine interactions in relevant exposure
situations. Being able to accurately predict and study
combination effects of amphetamines will improve the
understanding of potential chemical interactions when
simultaneous consumption occurs, as well as provide some
potential insight into the reasons behind random occur-
rence of extreme toxicity and even fatalities after con-
sumption of ecstasy pills, when this per se are not
associated with high rates of mortality.
Materials and methods
Chemicals
All reagents were of analytical grade or of the highest
grade available. Minimum Essential Medium Alpha (MEM
Alpha) with GlutMAX, foetal bovine serum (FBS), 0.05 %
trypsin/1 mM EDTA, antibiotic (5,000 U/ml penicillin,
5,000 lg/ml streptomycin), fungizone (250 lg/ml ampho-
tericin B), human transferrin (4 mg/ml) and Hanks bal-
anced salt solution (HBSS) without Ca or Mg were
purchased from Invitrogen Corporations (Paisley, UK).
4-MTA (HCl salt) was synthesized at REQUIMTE/Toxi-
cology Laboratory, Biological Sciences Department of
Faculty of Pharmacy, University of Porto. MDMA (HCl
salt) was extracted and purified from high purity MDMA
tablets that were provided by the Portuguese Criminal
Police Department. The obtained salts were purified and
fully characterized by nuclear magnetic resonance (NMR)
112 Arch Toxicol (2013) 87:111–122
123
and mass spectrometry (MS) methodologies. d-AMP sul-
phate was generously provided by Dr Frederico Pereira
(IBILI, Faculty of Medicine, University of Coimbra, Portu-
gal), and 4-hydroxy-3-methoxymethamphetamine (HMMA,
3-O-Me-N-Me-a-MeDA) and 4-hydroxy-3-methoxyamph-
etamine (HMA, 3-O-Me-a-MeDA) were synthesized at the
Chemistry Department, Faculty of Science and Technol-
ogy, University Nova de Lisboa, Portugal, following a
previously described procedure (Capela et al. 2006). 1 cc
(30 mg) OASIS MCX SPE extraction cartridges were
purchased from Waters (Lisbon, Portugal). Trifluoroace-
tic anhydride (TFAA), 4-hydroxy-3-methoxybenzylamine
hydrochloride, Type HP-2b-glucuronidase from Helix
pomatia, 4,5-dimethylthiazol-2-yl-2,5-diphenyl tetrazolium
bromide (MTT), Triton X-100, 3,4-methylenedioxyamph-
etamine (MDA) and (?)-METH hydrochloride (98 % purity)
were obtained from Sigma–Aldrich Co. (St. Louis, MO,
USA). Sodium acetate, n-hexane, ethyl acetate, ammonium
hydroxide, methanol, dimethyl sulfoxide (DMSO), ethanol
and all other chemicals were purchased from Merck (VWR,
Leicestershire, UK).
All amphetamines were used as supplied and stock
solutions made up in deionized sterile water. Stock solu-
tions were at least 20 times more concentrated than the
highest concentration tested, in order to prevent media
dilution. Subsequent dilutions were freshly prepared before
each experiment. All solutions were stored at -20 °C.
Hep G2 routine cell culture
As the liver is known to be one of the main targets for
amphetaminic toxicity in humans (Carvalho et al. 2010), the
immortalized human hepatoma cell line Hep G2 was chosen
for the cytotoxicity studies. These cells have been widely
used to assess the chemical liver toxicity (Chen and Ceder-
baum 1997; Tang et al. 2012) and were, therefore, considered
an appropriate model for the work described here.
Hep G2 cells were kindly provided by Dr Maryam
Modarai from UCL School of Pharmacy, London, UK.
Cells were routinely cultured in 75 cm
2
flasks in MEM
alpha medium supplemented with 10 % heat-inactivated
FBS, 1 % antibiotic, 1 % fungizone and 6 lg/ml transfer-
rin (complete culture medium) and maintained in a
humidified atmosphere of 5 % CO
2
at 37 °C. The medium
was changed every 4 days. When cells reached 80 %
confluence, cells were detached by trypsinization and
subcultured over a maximum of 10 passages. Hep G2 cells
were routinely tested for mycoplasma contamination.
MTT reduction assay
To produce reliable additivity expectations, concentration–
response relations of the individual mixture constituents
had to be accurately recorded, which required the use of a
reproducible and robust system, enabling high throughput,
with minimum variability. To meet those requirements, the
cytotoxic effects of the amphetamine-like drugs were
determined using the MTT reduction assay, which mea-
sures succinate dehydrogenase activity, an indicator of
metabolically active mitochondria and, therefore, an indi-
cator of cell viability. A previously described protocol
(Silva et al. 2011b) was adopted and optimized to a 96-well
plate format (Falcon; BD Biosciences, Oxford, UK).
Briefly, Hep G2 cells were seeded onto the central 60
wells of 96-well plates, at a density of 100,000 cells per
well, in a volume of 200 ll of complete culture medium, to
obtain confluent monolayers within 2 days. Peripheral
wells on the plate were filled with sterile water. On the day
of the experiment, the media was gently aspirated and the
cells exposed to MDMA, METH, 4-MTA, d-AMP or
mixture solutions in fresh cell culture medium, for 48 h.
Each individual plate also included six replicates of neg-
ative controls (i.e. no test agents) and six replicates of
positive controls (culture media containing 1 %Triton
X-100).
At the selected time point, the culture medium was
aspirated and frozen at -80 °C for future metabolic profile
analysis. The attached cells were rinsed with 200 ll HBSS,
followed by the addition of fresh culture medium con-
taining 0.25 mg/l MTT and incubation at 37 °Cina
humidified 5 % CO
2
atmosphere for 30 min. The formed
intracellular formazan crystals were then dissolved in
100 ll 100 % DMSO and absorbance measured at 570 nm,
using a multi-well plate reader (Labsystems Multiskan,
Basingstoke, UK). To reduce inter-experimental variabil-
ity, data were normalized on a plate-by-plate basis and
scaled between 0 % (negative controls) and 100 % effect
(positive controls). Results were graphically presented as
percentage of cell death versus concentration (mM).
All individual compounds were tested in nine indepen-
dent experiments, run on up to two plates per experiment,
with each plate containing eight increasing concentrations
of the test chemical in triplicates.
Mixture testing
In this work, three mixtures containing the same four
selected amphetamines but combined in different ratios were
tested. In mixture A, all chemicals were combined at their
EC
50
, such that they were present at concentrations that
produced the same effect, that is, individual compounds were
combined at equipotent concentrations. For this, a master
solution of mixture A was prepared containing the individual
components at the concentrations presented in Table 1
(corresponding to their individual EC
50
) and a range of
concentrations for testing was subsequently prepared by
Arch Toxicol (2013) 87:111–122 113
123
employing the fixed mixture ratio design, as described by
Altenburger et al. (2000) and Backhaus et al. (2000a).
Briefly, the master stock was serially diluted maintaining the
ratio between each constituent unchanged. Serial dilutions
covered a wide range of concentrations; so that a complete
concentration–response relationship could be recorded.
Mixture B was prepared in a similar way, but this time, the
four-mixture components were combined at their individual
EC
01
(individual concentrations presented in Table 1). A
serial dilution of this mixture covering a wide range of
concentrations was also prepared using the fixed mixture
ratio design.
The final mixture, mixture C, was prepared by fixing the
concentration of MDMA at 0.5 mM and modifying the
remaining components over a wide range of concentra-
tions. In order to achieve this, a 29concentrated master
stock solution containing METH, 4-MTA and d-AMP in
equal proportions was prepared following the fixed mixture
ratio design and then serially diluted in a large range of
concentrations, as described above. This ensures the ratio
between the three components is kept constant. An equal
volume of 1 mM MDMA was then added to each three-
component mixture concentration previously prepared, in
order to obtain a final concentration of MDMA of 0.5 mM
in all tested concentrations of mixture C.
In other words, for all concentrations of the four-com-
ponent mixture tested, the concentration of MDMA
remained 0.5 mM, whereas the concentrations of the
remaining three amphetamines increased throughout the
tested range, which means that the ratio between the four
amphetamines differs for each mixture concentration tested.
Calculation of predicted mixture effects
Based on the complete concentration–response curves of
the single agents, the overall effect of each mixture with
defined composition was predicted applying both CA and
IA models, as described in Payne et al. (2000).
Determination of MDMA and metabolites (MDA,
HMA, HMMA), METH, 4-MTA and d-AMP
by GC/MS
MDMA, METH, 4-MTA and d-AMP, as well as the
MDMA metabolites MDA, HMA and HMMA, were
quantified in the extracellular media and in the cellular
content after cell cleavage by adding 200 ll water with
overnight incubation at 0 °C. For METH, 4-MTA and
d-AMP, a qualitative analysis was carried out, where the
chromatograms of individual and mixture samples were
compared to the chromatograms of controls and the pres-
ence of other molecules (including metabolites) was not
detected. For this reason, further quantitative analysis was
not performed for these substances. The GC/MS determi-
nation was carried out as previously described (da Silva
et al. 2010; Pontes et al. 2010).
Quantitative GC/MS analysis was performed with a
Varian CP-3800 gas chromatograph (USA) equipped with
a VARIAN Saturn 4000 Ion Trap (IT) mass selective
detector (USA) and a Saturn GC/IT-MS workstation soft-
ware version 6.8. The capillary column VF-5 ms
(30 m 90.25 mm 90.25 m) was from VARIAN. The gas
chromatography was conducted with high purity helium
C-60 (Gasin, Portugal) at a constant flow of 1 ml/min with
a 1:30 split ratio. A CombiPAL automatic autosampler
(Varian, Palo Alto, CA) equipped with a 10-ll liquid syr-
inge was used for all analysis. 2 ll of sample was injected
into the system, in splitless mode. The injection port was at
220 °C. An initial column temperature of 100 °C was held
for 1 min, followed by a ramp of 15 °C/min to 300 °C,
with a 10 min post-run hold. The injection port temperature
was maintained at 250 °C. Total chromatographic separa-
tion was achieved in 9 min. The IT detector was set as
follows: the transfer line, manifold, and trap temperatures
were 280, 50 and 180 °C, respectively. All mass spectra
were acquired in the electron impact (EI) mode. To avoid
solvent overloading, ionization was maintained off during
Table 1 Parameters for the test amphetaminic agents in the MTT reduction assay
Estimated parameters for the best-fit
regression model of each individual agent
EC
50
(mM) EC
01
(mM) Relative proportion (%)
Regression
model
h1h2h3hmax Mixture A Mixture B
MDMA Logit -1.616E?00 4.644E?00 – 1.019E?02 2.228E?00 2.283E-01 20.94 20.93
METH Logit -3.328E?00 4.618E?00 – 1.129E?02 5.256E?00 5.316E-01 49.39 48.75
4-MTA GL-1 -3.622E?01 4.338E?02 7.471E -03 9.837E?01 7.407E-01 4.598E-02 6.96 4.22
d-AMP GL-1 -4.182E?01 7.635E?01 5.516E -02 9.944E?01 2.416E?00 2.847E-01 22.71 26.10
Mixture A Logit -2.360E?00 4.477E?00 – 1.167E?02 2.90E?00 2.924E-01 100 –
Mixture C Logit -1.652E?00 4.610E?00 – 1.131E?02 2.03E?00 2.161E-01 – –
114 Arch Toxicol (2013) 87:111–122
123
the first 4 min. The mass range was 50–600 m/z, with a
scan rate of 6 scan/s. The emission current was 50 A, and
the electron multiplier was set in relative mode to autotune
the procedure. The maximum ionization time was 25,000 s,
with an ionization storage level of 35 m/z.
The data analysis was performed in full scan mode, and
the chromatograms were reprocessed by selecting the
characteristic ions for each molecule. The selected ions
were as follows: IS m/z =232 and m/z =345; MDMA
and MDA m/z =135 and m/z =162; HMMA, m/z =154
and m/z =260; HMA m/z =140 and m/z =260; METH
m/z =118, m/z =154 and m/z =246; 4-MTA m/z =
137, m/z =164 and m/z =277; d-AMP m/z =118,
m/z =140 and m/z =232. Standard curves were plotted
for each compound. Linearity, precision, accuracy and
recovery were all within the accepted values for these
parameters (da Silva et al. 2010).
Regression modelling and statistical analysis
Nonlinear regression analysis of all 4 amphetamines,
individually or in mixture, was carried out using a best-fit
approach as described by Scholze et al. (2001). The cyto-
toxicity data obtained with the MTT reduction assay
(% cell death) were fitted to appropriate dosimetric models
(Gompertz, Logit, Probit, Weibull, Langmuir, General
Logit I and II) by using the specialized software program
NLREG—Nonlinear Regression, version 5.4 (Phillip H.
Sherrod, USA). All of the nonlinear regression models
describe sigmoidal concentration–response relationships. A
suitable best-fit model was selected based on a statistical
goodness-of-fit principle, after independently fitting each
equation to the same data set (Table 1), and the results
presented including the 95 % confidence intervals (CI).
MTT data from mixture B are presented as mean ±95 %
CI and are from five independent experiments. Normality of
the data distribution was assessed by three tests (KS nor-
mality test, D’Agostino and Pearson omnibus normality test
and Shapiro–Wilk normality test), and statistical comparison
between groups was estimated using the nonparametric
method of Kruskal–Wallis [one-way analysis of variance
(ANOVA) on ranks] followed by Dunn’s post hoc test.
Data from GC–MS analysis are from at least three
independent experiments, run in triplicate and are expres-
sed as mean ±SEM (standard error of the mean). Nor-
mality of the data distribution was assessed by three tests
(KS normality test, D’Agostino and Pearson omnibus
normality test and Shapiro–Wilk normality test) and dif-
ferences analysed by the Student’s unpaired ttest. pvalues
lower than 0.05 were considered statistically significant.
All statistical calculations were performed using
GraphPad Prism software, version 5.01 (GraphPad Soft-
ware, San Diego California, USA).
Results
Concentration–response relationship of individual
mixture agents
One of the main aims of this work was to investigate
potential interactions between four amphetaminic drugs
and evaluate whether these interactions could be accurately
predicted using the mathematical CA and IA models. In
order to produce the data required for calculating predic-
tions of mixture effects, extensive concentration–response
analyses of all the individual mixture components had to be
carried out. Moreover, this toxicity information for the
single drugs must be of reliable and reproducible signifi-
cance to ensure consistent predictions of combination
effects (Rajapakse et al. 2002).
In the MTT assay, all tested single agents yielded repro-
ducible effects in a concentration-dependent fashion,
resulting in decreased cell viability with the rising of
chemical concentration (increased percentage of cell death).
The data were produced on several occasions, using inde-
pendently prepared serial dilutions of all chemicals. There
was always good agreement between experiments. The
cytotoxicity curves for each of the tested drugs, including the
upper and lower 95 % CI, are displayed in Fig. 1. A sum-
mary of the best-fit regression models and the concentrations
which individually produce 1 and 50 % of the maximal
effect (EC
01
and EC
50
, respectively) for each drug are pre-
sented in Table 1. All drugs produced complete curves of
percentage of cell death versus drug concentration. Indi-
vidual concentration–response curves for the tested chemi-
cals were relatively similar, as were their maximal effects.
Fig. 1 Regression models for the cytotoxicity effects of all four-
mixture components in Hep G2 cells. The grey solid lines represent
the regression models for 4-methylthioamphetamine (4-MTA),
d-amphetamine (d-AMP), 3,4-methylenedioxymethamphetamine
(MDMA) and methamphetamine (METH) obtained in the MTT
assay, following 48 h incubations. The dashed grey lines are the
upper and lower 95 % CI of the best estimate of mean responses. The
labels are as follows: 1MDMA, 2METH, 34-MTA and 4d-AMP.
Data were from a minimum of nine independent experiments run in
triplicate
Arch Toxicol (2013) 87:111–122 115
123
Differences were observed essentially in the EC
50
values and
the slopes. MDMA with an EC
50
of 2.23 mM shared similar
potency with d-AMP (EC
50
2.42 mM). Comparatively,
4-MTA showed considerably higher potency (EC
50
0.74 mM), whilst METH was the least potent chemical tes-
ted (EC
50
5.26 mM). These differences between EC
50
s of the
test chemicals were deemed statistically significant, as there
was no overlap between the corresponding 95 % CI of the
concentration–response curves (Fig. 1).
In order to obtain an in-depth understanding of the
potential interactions between the tested amphetamines,
three different mixtures combining MDMA, METH,
4-MTA and d-AMP at varying ratios were tested.
Effects of a mixture prepared at a combination ratio
proportional to the potency of each individual
component (mixture A)
As described previously, in mixture A, all chemicals were
combined at their EC
50
, such that they were present at
equieffective concentrations (2.23 mM MDMA, 5.26 mM
METH, 0.74 mM 4-MTA and 2.42 mM d-AMP) (Table 1),
ensuring that each drug contributed equally to the overall
mixture effect and avoiding the disproportionate contribu-
tion of any one single agent. This mixture was designed with
the main aim of assessing the validity of the two competing
prediction models. Based on the concentration–response
relationships of the individual chemicals, the concepts of IA
and CA were used to predict the additive joint effects of the
four drugs. As seen in Fig. 2, the slope for each prediction
model was similar, both ranging the same order of magnitude
from minimal to maximal mortality. However, the curve
according to CA was shifted to lower concentrations,
assuming stronger mixture effects than IA. The combined
effects were then tested experimentally (Fig. 2). The
obtained data revealed low variability and led to a complete
concentration–effect curve. As shown in Fig. 2, additive
expectations according to CA agreed well with the experi-
mental observations, especially in the low effect range. At
the higher effect range, a slight deviation of the curve was
observed towards higher concentrations.
In contrast, the IA prediction clearly underestimated the
mixture effects. Comparing the relative concentrations for
the EC
50
, the median effect concentrations were 2.9 and
2.51 mM for the mixture and CA, respectively, whereas for
IA this value was much higher (4.68 mM).
Combination effects at low, ineffective concentrations
(mixture B)
In mixture B, chemicals were mixed in a similar manner to
mixture A, but this time, in proportion to their EC
01
, to test
possible joint effects when individual components are
present at statistically ineffective concentrations. As shown
in Fig. 3, when each component of the mixture was indi-
vidually tested at the concentrations of 0.21 mM MDMA,
0.5 mM METH, 0.047 mM 4-MTA and 0.29 mM d-AMP,
they produced very low effects, which could not be sta-
tistically differentiated from negative controls. Nonethe-
less, when mixed at those ineffective concentrations, they
were able to act together to produce significant additive
responses, which were accurately predicted by CA (Fig. 3).
As shown, 1.058 mM of mixture B was responsible for
13.98 ±2.34 % of cell killing. CA predicted effect was
slightly lower but not significantly different (11.57 %).
Combination effects of a mixture representative
of a ‘realistic’ exposure scenario (mixture C)
Finally, mixture C was conceived with the aim of con-
firming the CA additivity expectations in a more realistic
exposure scenario associated with the consumption of
ecstasy pills containing MDMA as a main constituent and
the remaining amphetamines as contaminants.
It is often reported that amphetamine-related compounds
can appear as contaminants in MDMA pills or even as sub-
stitutes of the supposed main component (Milroy 1999;
Tanner-Smith 2006). To recreate these eventual situations,
mixture C was prepared by fixing the MDMA concentration
at 0.5 mM and gradually increasing the levels of the
remaining components over a wide range of concentrations,
while ensuring the ratio between them remained unaltered.
Although CA accurately predicted mixture effects in the
lower concentration range (up to concentrations producing
about 40 % effect), a deviation from additive expectations
Fig. 2 Predicted and observed effects of a mixture of the four tested
amphetamine-designer drugs in the MTT assay—mixture A. Individ-
ual data points are represented by grey circles, and the best-fit
regression model is shown by the black line, labelled ‘observed
effect’. Black dashed lines represent the upper and lower 95 % CI for
the regression fit. The dashed blue line shows the predicted combined
effects derived from independent action (IA). The solid red line
shows the prediction according to concentration addition (CA). The
green dotted lines show the EC
50
for each response curve (for values
see Table 1). Experimental data derive from five independent
experiments run in triplicate (color figure online)
116 Arch Toxicol (2013) 87:111–122
123
was seen at higher concentrations (Fig. 4). The observed
deviation was indicative of a weak antagonism, as con-
centrations higher than expected were necessary to produce
the same effects, experimentally.
Evaluation of the metabolism of MDMA
in the presence of other amphetamines (impact
of mixtures)
As mentioned earlier, by evaluating the effects of mixtures
A and C, it became clear that the effect concentrations
predicted by CA agreed well with those experimentally
observed throughout most of the effect range, except at
higher effect levels, where a small deviation from addi-
tivity was seen. This deviation was particularly obvious in
mixture C, for effects above 40 % cell death.
It is widely reported that the amphetamines tested in this
study share common mechanisms of metabolism and
detoxification and, consequently, can compete with each
other in these processes (de la Torre et al. 2004). For that
reason, it is conceivable that a different metabolic profile of
the mixture components could occur in a combination
setting. However, such interactions would not be accounted
for when calculating mixture expectations according to
CA, as the concept assumes the compounds do not interact
in a pharmacological manner. This could explain the
deviations found between the predicted and experimentally
mixture effects and the weak antagonistic effects observed.
We tested this hypothesis by investigating potential
unexpected changes in the metabolic profile of the individual
compounds, when these were present in the mixture, by GC/
MS analysis. For that, parent compounds and corresponding
MDMA metabolites were quantified in the extracellular
media and in Hep G2 cells, after incubation with two selected
concentrations of both mixture A and C: For mixture A, a
concentration of 1.75 mM (A1; composed of 0.383 mM
MDMA, 0. 820 mM METH, 0.131 mM 4-MTA and 0.
424 mM d-AMP) was chosen, as it induced an effect that fell
within the range accurately predicted by CA (effect level
approximately 24.99 %). A second tested concentration of
mixture A was 4.0 mM (A2; constituted by 0.871 mM
MDMA, 1.87 mM METH, 0.298 mM 4-MTA and
0.965 mM d-AMP), as this was shown to produce effects that
deviated from additivity, as observed in Fig. 5.
The tested concentrations of mixture C were selected on
the same basis as for mixture A. Therefore, a concentration
of 1.64 mM (C1, containing 0.5 mM MDMA plus
0.381 mM of each one of the other components METH,
4-MTA and d-AMP), which yielded an effect well pre-
dicted by CA, and a concentration of 2.33 mM (C2,
0.5 mM MDMA and 0.6104 mM of each one of the
remaining amphetamines), which produced an effect that
deviated from that predicted by CA, were chosen. For all
mixture concentrations tested for metabolic profiling (A1,
A2, C1 and C2), the corresponding concentrations of the
individual components were also evaluated.
Under our experimental model, intracellular levels of
parent compounds and MDMA metabolites were below the
quantification limit (3.5 ng/ml) of the analytical method for
all the tested samples. Analysis of the extracellular media
showed no significant alterations of METH, 4-MTA and
d-AMP individual profiles for either mixture tested (data
not shown). Also, no biotransformation of MDMA into
HMA or HMMA was detected in either media.
Fig. 3 Individual effects of MDMA, METH, 4-MTA and d-AMP at
the concentrations present in 1.058 mM of mixture B (1.058 mM is
the concentration of the mixture when all compounds are mixed at
their EC
01
). CA: Concentration addition prediction. MIX: observed
effect of 1.058 mM of mixture B. The concentrations of the four-
mixture components in 1.058 mM of the mixture B are 0.21 mM
MDMA, 0.5 mM METH, 0.047 mM 4-MTA and 0.29 mM d-AMP.
Data are from five independent experiments run in triplicate. The
dashed red line corresponds to the sum of the individual effects of all
mixture components. Error bars represent the 95 % CI. Statistical
comparisons were made using the Kruskal–Wallis test followed by
the Dunn’s multiple comparison post hoc test. *** show statistically
significant differences between the mixture and all other treatments.
(***p\0.001)
Fig. 4 Predicted and observed effects of mixture C in the MTT assay.
Individual data points (grey circles) are from four independent
experiments run in six replicates. Best-fit regression model is
illustrated by the black line, labelled ‘observed effect’. Black dashed
lines represent the upper and lower 95 % CI for the regression fit. The
solid red line (CA) shows the prediction based on concentration
addition. The horizontal green dashed line represents the effect of
0.5 mM of MDMA when tested alone (color figure online)
Arch Toxicol (2013) 87:111–122 117
123
Conversely, as depicted in Fig. 5, MDA was detected in
the extracellular samples, an indicator of MDMA metab-
olism. The analysis of mixture A showed an increase in the
metabolic rate of MDMA when the chemical was in the
presence of other amphetamines. Moreover, the metabo-
lism of this amphetamine increased even further in the
concentrations where a deviation from CA was observed
(A2). When MDMA was tested alone, there were no sig-
nificant differences in its metabolic profile between the
concentration present in A1 (0.383 mM) and A2
(0.871 mM). Here, the MDA/MDMA ratios were 0.0034
and 0.007, respectively. However, when combined, the
metabolism of the same concentrations of MDMA was
significantly higher in A2 (MDA/MDMA ratio 0.3045)
than in A1 (MDA/MDMA ratio 0.0968).
A similar observation was made with mixture C (Fig. 5).
In this case, the concentration of MDMA in both C1 and
C2 remained constant at 0.5 mM. The MDA/MDMA ratio
of this concentration tested alone was 0.0052. When in
combination, the metabolism of MDMA increased with an
increase in the concentrations of the remaining three
amphetamines, which was clearly seen by the differences
in MDA/MDMA ratios. For concentration C1, the MDA/
MDMA ratio was 0.0917, whereas for C2, this was 0.1668
(*p\0.05).
Discussion
The use of CA and IA models to predict additive combi-
nation effects requires an exhaustive characterization of the
concentration–effect relationships of individual mixture
components, in terms of shape, position (along the con-
centration axis) and maximal effect (Drescher and Boede-
ker 1995).
The MTT assay proved to be an effective method to
meet these requirements, allowing the performance of
high-throughput experiments with relative small variabil-
ity. It produced reproducible results and complete curves
that span a wide concentration–effect range.
Fig. 5 Metabolism of MDMA alone and in combination as tested by
GC–MS. a. A1 is the concentration of mixture A tested (1.75 mM)
where the effect coincides with the CA prediction. A1 is composed of
0.383 mM of MDMA, 0. 820 mM of METH, 0.131 mM of 4-MTA
and 0.424 mM of d-AMP; A2 is a concentration of mixture A
(4.0 mM) that deviates from CA expectations and is constituted by
0.871 mM of MDMA, 1.87 mM of METH, 0.298 mM of 4-MTA and
0.965 mM of d-AMP. b. Concentration C1 of mixture C induces an
effect that falls within the range estimated by CA and corresponds to
1.64 mM. It contains 0.5 mM of MDMA and 0.3815 mM of each one
of the components METH, 4-MTA and d-AMP, while C2 corresponds
to a concentration of mixture C (2.33 mM) that fails to meet the
prediction by CA and is constituted by 0.5 mM MDMA and
0.6104 mM of each one of the remaining amphetamines. Solid red
lines in the graph plots represent predictions by CA, while the solid
and dashed blue lines are the experimental effects with the
correspondent 95 % CI, respectively. Data are mean ±SEM and
were obtained from three independent experiments run in duplicate.
Dotted lines, in the bar graphs, represent comparisons between
groups of two. Differences between groups were analysed by
Student’s unpaired ttest. *p\0.05, **p\0.01 and ***p\0.001
(color figure online)
118 Arch Toxicol (2013) 87:111–122
123
In line with earlier reports (Carmo et al. 2004; Cloonan
et al. 2010; Custodio et al. 2010), in this work, 4-MTA
revealed to be a powerful cytotoxic agent (EC
50
0.75 mM)
yielding more potent responses than any other tested
amphetamine, including MDMA (EC
50
2.19 mM). In
contrast, METH presented the least cytotoxic profile (EC
50
4.69 mM) corroborating previous studies, which identified
it as a less effective drug than MDMA and d-AMP (EC
50
2.42 nM) in inducing in vitro cell death (Stumm et al.
1999b; Jimenez et al. 2004).
Considering the comparisons between computed and
experimentally observed effects, the CA model proved to
be a valuable tool for the assessment of additive joint
effects of mixtures of amphetamines in this in vitro system.
The overlap between the predicted data and the 95 % CI of
the best-fit regression model showed good conformity,
particularly at low effect levels. The work presented here
demonstrates, for the first time, the excellent prediction
power of CA when applied to combinations of ampheta-
mines, proving that the four tested chemicals act in an
additive fashion to produce the overall mixture effect. The
concept of IA, on the other hand, is undoubtedly inappro-
priate for the assessment of the joint effects of these
compounds in the MTT assay, suggesting that there is a
possible similarity in the way in which these agents lead to
Hep G2 cell death. So, assuming that all of our mixture
components operate in a similar manner, we can expect the
same mixture effect being produced by replacing one
constituent totally, or in part, by other, at an equieffective
concentration. For that reason, each individual component
is thought to contribute to the overall mixture effect by
acting proportionally to its concentration, even at concen-
trations that individually yield undetectable effects.
As shown, when each component of the mixture was
individually tested at its EC
01
, very low effects were pro-
duced, which could not be statistically differentiated from
untreated controls. Others also confirmed these noncyto-
toxic concentrations in the immortalized human chorio-
carcinoma JAR cells at the same time point (Hayat et al.
2006). Nevertheless, when mixed at these concentrations,
the four substances were able to act together to produce
very significant effects. In fact, the effect of 1.058 mM of
mixture B does not correspond to 4 % of cell killing, as it
could be mistakenly believed by the simple sum of the
component effects, but to 13.98 ±2.34 %. This is very
close to the value estimated by the CA prediction, once
again demonstrating the applicability of the model.
Accordingly, we do not need to invoke synergistic
combinations to prove that low levels of amphetamines
present in illicitly consumed ‘rave pills’ can produce
adverse effects, as significant mixture effects already occur
in an additive fashion. Understanding this concept is cru-
cial in the evaluation of mixture interactions, as studies
frequently rely heavily on the search for synergisms to
justify observed joint effects. A consequence of this
approach is that often conclusions of synergisms are made,
even in the absence of appropriate additive expectations.
As mentioned earlier, besides MDMA, ecstasy pills
often contain amphetamine-like products of uncontrolled
and clandestine synthetic processes. Several previous
publications highlighted the fact that many of the MDMA
pills available in illicit markets contain a number of other
substances, sometimes cheaper and easily obtained, like
METH (Camilleri and Caldicott 2005), d-AMP (Sherlock
et al. 1999; Teng et al. 2006), 4-MTA (Tanner-Smith 2006;
Teng et al. 2006) and other related derivatives. In order to
assess whether the prognostic value of CA expectations
fitted to more realistic scenarios, we studied mixture C,
where the influence of varying concentrations of a three-
component mixture (4-MTA, METH and d-AMP) was
combined with a constant concentration of MDMA. In a
similar way to the observations made with mixtures A and
B, our results demonstrated a good agreement with CA
especially at low concentrations and joint effects were
slightly lower than additivity for higher effect levels (above
40 %) indicating weak antagonisms.
For the model of CA to be applicable, it relies on the
assumption that all mixture components share the same
toxicity mechanism and do not interact with, potentiate or
antagonize each other. For this reason, this model does not
take into account potential pharmacokinetic interactions
between chemicals, such as the induction or inhibition of
metabolic pathways. However, we know that amphet-
amine-related drugs have a close structural and functional
relationship and use the same pharmacological and detox-
ification pathways (de la Torre et al. 2004). Therefore, it is
plausible that all four amphetamines will compete and
consequently interact with the metabolism and detoxifica-
tion of each other in an unexpected manner. Ultimately,
this could result in the deviations from additivity here
reported.
The effects caused by the consumption of amphetamines
can be conditioned by a plethora of factors that converge in
a certain individual, on a certain moment. The mechanisms
involved in liver damage induced by amphetamines are
complex and still not completely understood. A variety of
hypotheses have been proposed including the increased
efflux of neurotransmitters, the oxidation of biogenic
amines, mitochondrial impairment and apoptosis, and a
direct effect of amphetamines and/or reactive metabolites
(Carvalho et al. 2012). In addition, genetic polymorphism
of metabolizing enzymes (particularly CYP2D6), polydrug
abuse, and environmental features accompanying illicit
amphetamine use may increase the risk for liver compli-
cations (Carvalho et al. 2012). Hyperthermia is thought to
greatly contribute to liver toxicity. However, in some cases,
Arch Toxicol (2013) 87:111–122 119
123
liver damage appears unrelated to hyperpyrexia (Milroy
et al. 1996; Jones and Simpson 1999).
A well-known mechanism of toxicity in humans implies
hepatic MDMA bioactivation into reactive species (de la
Torre et al. 2004). The metabolism of MDMA is mainly
regulated by cytochrome P450 (CYP450) enzymes
and catechol-O-methyltransferase (COMT) in the liver.
N-demethylation to MDA is a reaction mainly catalysed by
CYP2B6. Both MDMA and MDA are then O-demethylated
by CYP2D6, and to a lesser extent by CYP1A2, CYP2B6
and CYP3A4, to 3,4-dihydroxymethamphetamine (HHMA,
N-methyl-a-methyldopamine, N-Me-a-MeDA) and 3,4-di-
hydroxyamphetamine (HHA, a-methyldopamine, a-MeDA),
respectively. These catechol intermediates can undergo
oxidation to the corresponding highly redox active ortho-
quinones, which can enter in redox cycling, originate
semiquinone radicals and lead to the generation of ROS or
RNS, which are highly toxic for the cell (de la Torre et al.
2004; Shenouda et al. 2009; Barbosa et al. 2012). In light
of this, and because all chemicals tested herein share the
same biotransformation pathways, we hypothesized that
they would promote the saturation of specific enzymes
involved in oxidative metabolism and, therefore, reduce
the formation of reactive species and so, cytotoxicity.
However, instead, we observed a statistically significant
increase in the formation of MDA (**p\0.01), an indi-
cation of increased metabolism. A possible explanation for
this might be linked to the fact that in the mixture setting,
the MDMA fraction bound to serum proteins or retained in
the lipid bilayer membrane might decrease, as it is dis-
placed from the binding sites by the remaining mixture
components. This would increase the levels of free MDMA
available for metabolism. Then, a preferential overex-
pression of CYP2B6, promoting N-demethylation with
MDA formation, in detriment of O-demethylation, which
would produce highly toxic reactive species, may play a
role. As MDA has been shown to have slightly lower toxic
effects than the parent compound MDMA in human
proximal tubular cells (Carvalho et al. 2002), an increase in
this metabolic product would justify the weak antagonisms
observed. Nevertheless, the precise molecular interactions
between amphetaminic drugs are still not fully understood,
and it is possible that additional factors are involved in
the deviations observed, requiring further biological and
molecular investigation.
The tested concentrations used in the present study are
in the range of concentrations used in several mechanistic
in vitro studies (Simantov and Tauber 1997; Stumm et al.
1999a; Carvalho et al. 2004a,b; Capela et al. 2006). They
are higher than concentrations commonly found in human
abusers. However, it should be noted that high interindi-
vidual variations of blood levels in cases of severe and
even fatal intoxications have been observed. For MDMA,
for example, blood concentrations can be as high as
13.5 mg/l (approximately 70 lM) (De Letter et al. 2004,
2006). In such cases, the autopsy findings have shown that
the tissue levels of the drug in the liver can be up to
18 times higher than blood concentrations (De Letter et al.
2006) and 30 times higher in the brain (Garcia-Repetto
et al. 2003). Amphetamines in general have low protein
binding (usually under 20 %), which confers high bio-
availability to these drugs and favours their easy diffusion
from the plasma to the extravascular compartment (de la
Torre et al. 2004). Moreover, these concentrations found at
autopsy are probably lower than the peak concentrations
that are expected to occur after drug intake, especially in
the cases where the victims are submitted to emergency-
care treatments to control the intoxications.
One final point to consider is the fact that in the present
study, we have explored combination effects in an in vitro
setting, using a hepatocarcinoma cell line model. The Hep
G2 cell line, in spite of retaining metabolic capacity and
being able to respond to metabolic inducers, as well as
bioactivate chemical substances by cytochrome P450 iso-
forms (Doostdar et al. 1993; Darroudi et al. 1996; Ripp
et al. 2003; Knasmuller et al. 2004; Donato et al. 2008), has
weaker metabolic activity than primary hepatocytes or
normal liver tissue (Donato et al. 2008). For this reason, an
important task for the future is to investigate whether our
findings hold true for biological effects at higher levels of
complexity, such as in primary hepatocyte cultures or in an
in vivo scenario, where other factors, such as metabolic
competency, immune-mediated responses and polymor-
phisms, can be taken into account. However, to the best of
our knowledge, CA and IA models are yet to be applied to
the study of mixtures of amphetamines in vivo.
In conclusion, our results emphasize the limitations of
the traditional focus on single agents, as they would
completely overlook these potentially hazardous events
and would lead to significant underestimations of toxicity.
Given this, assessing combination effects of amphetamines
is of utmost importance from a toxicological point of view,
as the majority of ecstasy users, consciously or not, take a
wide variety of distinct drugs on the same night out.
Understanding the impact of other drugs in ecstasy pills
might provide valuable information to dissect the causes
behind reported sudden and random lethal intoxications
and aid diagnostics and treatment of nonfatal cases.
We strongly believe that a better understanding of the
joint effects of these uncontrolled illicit drugs might have a
considerable influence on public health by raising the
awareness of potential severe toxicity and, consequently,
encouraging behavioural changes in consumers worldwide.
Acknowledgments The authors would like to kindly thank Renata
Silva (Faculty of Pharmacy, University of Oporto, Portugal) and
120 Arch Toxicol (2013) 87:111–122
123
Paula Guedes (Faculty of Pharmacy, University of Oporto, Portugal)
for technical support with cell culture and GC–MS techniques,
respectively. Fe
´lix Carvalho (Faculty of Pharmacy, University of
Oporto, Portugal), Martin Scholze and Andreas Kortenkamp (The
Institute for Environment, University of Brunel, London) are also
greatly acknowledged for their criticisms and suggestions. This work
was supported by the Portuguese Research Council Fundac¸a
˜o para a
Cie
ˆncia e para a Tecnologia (FCT) [SFRH/BD/45617/2008 to
D.D.S.] and cofounded by the European Community financial support
Programa Operacional Factores de Competitividade do Quadro de
Refere
ˆncia Estrate
´gico Nacional (QREN POFC).
Conflict of interest The authors declare that there are no conflicts
of interest.
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