Introduction

Amphetamine designer drugs are widely abused addictive psychostimulants. 3,4-methylenedioxymethamphetamine (MDMA), commonly known as ecstasy, is the most popular 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 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 consumption 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), dextroamphetamine (d-AMP), methamphetamine (METH), ketamine, 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 toxicology 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 contributes 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 mixture 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 amphetaminic 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 significant joint effects to occur, even when these individual components were combined at low concentrations, representative 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 occurrence of extreme toxicity and even fatalities after consumption 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 μg/ml streptomycin), fungizone (250 μg/ml amphotericin B), human transferrin (4 mg/ml) and Hanks balanced salt solution (HBSS) without Ca or Mg were purchased from Invitrogen Corporations (Paisley, UK). 4-MTA (HCl salt) was synthesized at REQUIMTE/Toxicology 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) and mass spectrometry (MS) methodologies. d-AMP sulphate was generously provided by Dr Frederico Pereira (IBILI, Faculty of Medicine, University of Coimbra, Portugal), and 4-hydroxy-3-methoxymethamphetamine (HMMA, 3-O-Me-N-Me-α-MeDA) and 4-hydroxy-3-methoxyamphetamine (HMA, 3-O-Me-α-MeDA) were synthesized at the Chemistry Department, Faculty of Science and Technology, 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). Trifluoroacetic anhydride (TFAA), 4-hydroxy-3-methoxybenzylamine hydrochloride, Type HP-2β-glucuronidase from Helix pomatia, 4,5-dimethylthiazol-2-yl-2,5-diphenyl tetrazolium bromide (MTT), Triton X-100, 3,4-methylenedioxyamphetamine (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 solutions 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 Cederbaum 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 cm2 flasks in MEM alpha medium supplemented with 10 % heat-inactivated FBS, 1 % antibiotic, 1 % fungizone and 6 μg/ml transferrin (complete culture medium) and maintained in a humidified atmosphere of 5 % CO2 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 measures succinate dehydrogenase activity, an indicator of metabolically active mitochondria and, therefore, an indicator 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 μl 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 negative 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 μl HBSS, followed by the addition of fresh culture medium containing 0.25 mg/l MTT and incubation at 37 °C in a humidified 5 % CO2 atmosphere for 30 min. The formed intracellular formazan crystals were then dissolved in 100 μl 100 % DMSO and absorbance measured at 570 nm, using a multi-well plate reader (Labsystems Multiskan, Basingstoke, UK). To reduce inter-experimental variability, 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 independent 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 EC50, 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 EC50) and a range of concentrations for testing was subsequently prepared by 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 EC01 (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.

Table 1 Parameters for the test amphetaminic agents in the MTT reduction assay
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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 concentrations. In order to achieve this, a 2× concentrated 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-component 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 μl 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 presence of other molecules (including metabolites) was not detected. For this reason, further quantitative analysis was not performed for these substances. The GC/MS determination 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 software version 6.8. The capillary column VF-5 ms (30 m × 0.25 mm × 0.25 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-μl liquid syringe was used for all analysis. 2 μl 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 separation 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 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 cytotoxicity 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 normality 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 expressed as mean ± SEM (standard error of the mean). Normality 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 differences analysed by the Student’s unpaired t test. p values lower than 0.05 were considered statistically significant.

All statistical calculations were performed using GraphPad Prism software, version 5.01 (GraphPad Software, 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 predictions 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 significance to ensure consistent predictions of combination effects (Rajapakse et al. 2002).

In the MTT assay, all tested single agents yielded reproducible 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 independently 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 summary of the best-fit regression models and the concentrations which individually produce 1 and 50 % of the maximal effect (EC01 and EC50, respectively) for each drug are presented in Table 1. All drugs produced complete curves of percentage of cell death versus drug concentration. Individual concentration–response curves for the tested chemicals were relatively similar, as were their maximal effects. Differences were observed essentially in the EC50 values and the slopes. MDMA with an EC50 of 2.23 mM shared similar potency with d-AMP (EC50 2.42 mM). Comparatively, 4-MTA showed considerably higher potency (EC50 0.74 mM), whilst METH was the least potent chemical tested (EC50 5.26 mM). These differences between EC50s 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).

Fig. 1
figure 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: 1 MDMA, 2 METH, 3 4-MTA and 4 d-AMP. Data were from a minimum of nine independent experiments run in triplicate

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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 EC50, 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 contribution 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 experimental observations, especially in the low effect range. At the higher effect range, a slight deviation of the curve was observed towards higher concentrations.

Fig. 2
figure 2

Predicted and observed effects of a mixture of the four tested amphetamine-designer drugs in the MTT assay—mixture A. Individual 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 EC50 for each response curve (for values see Table 1). Experimental data derive from five independent experiments run in triplicate (color figure online)

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In contrast, the IA prediction clearly underestimated the mixture effects. Comparing the relative concentrations for the EC50, 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 EC01, 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 individually 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 statistically differentiated from negative controls. Nonetheless, 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 %).

Fig. 3
figure 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 EC01). 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)

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Combination effects of a mixture representative of a ‘realistic’ exposure scenario (mixture C)

Finally, mixture C was conceived with the aim of confirming 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 substitutes 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 was seen at higher concentrations (Fig. 4). The observed deviation was indicative of a weak antagonism, as concentrations higher than expected were necessary to produce the same effects, experimentally.

Fig. 4
figure 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)

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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 additivity 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.

Fig. 5
figure 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 t test. *p < 0.05, **p < 0.01 and ***p < 0.001 (color figure online)

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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 predicted 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.

Conversely, as depicted in Fig. 5, MDA was detected in the extracellular samples, an indicator of MDMA metabolism. 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 metabolism 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 significant 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 combination effects requires an exhaustive characterization of the concentration–effect relationships of individual mixture components, in terms of shape, position (along the concentration axis) and maximal effect (Drescher and Boedeker 1995).

The MTT assay proved to be an effective method to meet these requirements, allowing the performance of high-throughput experiments with relative small variability. It produced reproducible results and complete curves that span a wide concentration–effect range.

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 (EC50 0.75 mM) yielding more potent responses than any other tested amphetamine, including MDMA (EC50 2.19 mM). In contrast, METH presented the least cytotoxic profile (EC50 4.69 mM) corroborating previous studies, which identified it as a less effective drug than MDMA and d-AMP (EC50 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 amphetamines, 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 inappropriate 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 concentrations that individually yield undetectable effects.

As shown, when each component of the mixture was individually tested at its EC01, very low effects were produced, which could not be statistically differentiated from untreated controls. Others also confirmed these noncytotoxic concentrations in the immortalized human choriocarcinoma 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 crucial 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 amphetamine-related drugs have a close structural and functional relationship and use the same pharmacological and detoxification pathways (de la Torre et al. 2004). Therefore, it is plausible that all four amphetamines will compete and consequently interact with the metabolism and detoxification 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 complications (Carvalho et al. 2012). Hyperthermia is thought to greatly contribute to liver toxicity. However, in some cases, 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-α-methyldopamine, N-Me-α-MeDA) and 3,4-dihydroxyamphetamine (HHA, α-methyldopamine, α-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 indication 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 displaced from the binding sites by the remaining mixture components. This would increase the levels of free MDMA available for metabolism. Then, a preferential overexpression 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 interindividual 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 μM) (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 bioavailability 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 isoforms (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 polymorphisms, 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.