Environ. Sci. Technol. 2006, 40, 7402-7408
Comparative Ecotoxicological Hazard Assessment of Beta-Blockers and Their Human Metabolites Using a Mode-of-Action-Based Test Battery and a QSAR Approach† B E A T E I . E S C H E R , * ,‡ N A D I N E B R A M A Z , ‡ MANUELA RICHTER,‡ AND JUDIT LIENERT§ Department of Environmental Toxicology, Department of Urban Water Management, Swiss Federal Institute of Aquatic Science and Technology (Eawag), CH-8600 Du ¨ bendorf, Switzerland
We analyzed nontarget effects of the β-blockers propranolol, metoprolol, and atenolol with a screening test battery encompassing nonspecific, receptor-mediated, and reactive modes of toxic action. All β-blockers were baseline toxicants and showed no specific effects on energy transduction nor endocrine activity in the yeast estrogen and androgen screen, and no reactive toxicity toward proteins and DNA. However, in a phytotoxicity assay based on the inhibition of the photosynthesis efficiency in green algae, all β-blockers were 10 times more toxic than their modeled baseline toxicity. Baseline- and phytotoxicity effects increased with hydrophobicity. The β-blockers showed concentration addition in mixture experiments, indicating a mutual specific nontarget effect on algae. Using literature data and quantitative structure-activity relationships (QSAR), we modeled the total toxic potential of mixtures of the β-blockers and their associated human metabolites for the phytotoxicity endpoint with two scenarios. The realistic scenario (I) assumes that the metabolites lose their specific activity and act as baseline toxicants. In the worst-case scenario (II) the metabolites exhibit the same specific mode of action as their parent drug. For scenario (II), metabolism hardly affected the overall toxicity of atenolol and metoprolol, whereas propranolol’s hazard potential decreased significantly. In scenario (I), metabolism reduced the apparent EC50 of the mixture of parent drug and metabolite even further. The proposed method is a simple approach to initial hazard assessment of pharmaceuticals and can guide higher tier testing. It can be applied to other classes of pollutants, e.g., biocides, as well as to environmental transformation products of pollutants.
Introduction Since the first reports about the occurrence of human pharmaceuticals in wastewater and surface waters have * Corresponding author phone: 0041-44-823 5068; fax: 0041-44823 5471; e-mail:
[email protected]. † This article is part of a special issue on Emerging Contaminants. ‡ Department of Environmental Toxicology, Swiss Federal Institute of Aquatic Science and Technology (Eawag). § Department of Urban Water Management, Swiss Federal Institute of Aquatic Science and Technology (Eawag). 7402
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appeared some years ago (for literature reviews see, e.g., refs 1 and 2, for more recent data, see refs 3-6), much progress has been made on their environmental risk assessment (7, 8). The European Agency for the Evaluation of Medicinal Products (EMEA) has proposed a draft guideline for the environmental risk assessment of human pharmaceuticals (9). It is based on the accepted risk assessment paradigms for industrial chemicals and biocides (10), but it also considers the specific features of pharmaceuticals, e.g., the use of available pharmacological information, especially at higher tiers. Risk assessments performed so far have relied mainly on acute ecotoxicity data, and they have usually concluded that there is no immediate risk (11-13) but the most recent EMEA draft proposed to omit acute toxicity tests altogether (9). Pharmaceuticals target specific receptors in humans, and many of these receptors occur also in other mammals, vertebrates, and sometimes even in invertebrates. Examples include β-blockers that bind to the β-adrenergic receptors in humans and block the action of catecholamines and can thus be used to treat hypertension and related heart problems (14). A variety of β-adrenoreceptors are also present in fish, some of which show striking sequence homology with other vertebrates (15 and references therein). Even in invertebrates such as Daphnia magna β-blockers were shown to exhibit a specific effect on the heart rate (16). Therefore, one approach to assess the hazard potential of β-blockers (and pharmaceuticals in general) is to identify and quantify their target effects on nontarget organisms (17-19). The EMEA draft guideline proposes that pharmacokinetic and pharmacodynamic data should inform environmental risk assessment (9). Such an approach is presently also taken within the European Union project ERAPharm (20). Additionally, it is possible that β-blockers and other pharmaceuticals show specific nontarget effects that are unrelated to their therapeutic effects. The first step to identify nontarget effects is to assess possible interactions between drugs and biological molecules (21). From these interactions, primary toxic mechanisms can be derived (21). Based on this approach, we have proposed a test battery to identify nontarget modes of toxic action of pharmaceuticals in aquatic life (22). In this test battery, the β-blocker propranolol specifically affected the efficiency of photosynthesis (22). In the present study, we applied this mode-of-actionbased test battery to further β-blockers, metoprolol, atenolol, and sotalol, to assess if this specific effect is limited to propranolol or if it is a general feature of all β-blockers. β-Blockers are secondary amines and are, therefore, fully protonated at environmental pH. We accounted for their positive charge in the QSAR analysis (quantitative structure activity relationship) and have experimentally determined the liposome-water partition ratios at pH 7 to make our QSAR analysis more robust. In environmental risk assessments of human pharmaceuticals, the focus has so far been mainly on the parent drugs (12, 13). However, most pharmaceuticals are extensively metabolized by the human body and only a small fraction enters the wastewater stream via excretion in its nonmetabolized form, while the remainder is excreted as a variety of different metabolites (23-25). Little is known about the ecotoxic potential of the mixture of metabolites formed apart from the fact that metabolism usually renders the parent compound more hydrophilic and thus less toxic. We propose a method to account for the metabolites by predicting their ecotoxic potential. The ultimate goal of this study is a comparative ecotoxicological hazard assessment of the 10.1021/es052572v CCC: $33.50
2006 American Chemical Society Published on Web 06/21/2006
different β-blockers under consideration of the different pathways of metabolism. This is particularly relevant for β-blockers because they cover a wide range of hydrophobicity and extent of metabolism (14).
Experimental Section Chemicals. The β-blockers propranolol (CAS RN 525-66-6, >98%, hydrochloride), metoprolol (CAS RN 56392-17-7, 99%, tartrate salt), atenolol (CAS RN 29122-68-7, 98%), and sotalol (CAS RN 959-24-0, 98%) were obtained from Sigma (Buchs, Switzerland). All buffers and medium components were purchased from Fluka (Buchs, Switzerland). The concentrations of the β-blockers were quantified with HPLC as described in the Supporting Information. Liposome-Water Partitioning. Liposome-water distribution ratios at pH 7, Dlipw(pH 7), were determined for the parent drugs using the equilibrium dialysis method described earlier (26). Details of the experiments can be found in the Supporting Information. Mode-of-Action-Based Test Battery. To assess baseline toxicity and specific interference of the energy metabolism, we used the 30-min bioluminescence inhibition test with the marine bacterium Vibrio fischeri, performed according to ISO guideline 11348-3 (27) with modifications as described in ref 28. Direct and indirect effects on photosynthesis were evaluated with the 24-h chlorophyll fluorescence test with the green algae Desmodesmus subspicatus using the chlorophyll fluorometer ToxY-PAM according to (22). Estrogenic and androgenic receptor-mediated effects were assessed using the yeast estrogen screen (YES) (originally developed by ref 29 with the modifications reported in ref 22) and the yeast androgen screen (YAS) (30). All tests listed above were performed as described previously (22) with exception of the biosensors specific for protein and DNA damage that were replaced by the umuC test (31), which was performed as indicated in the test guideline. The Salmonella typhimurium strain TA 1535/pSK1002 was obtained from the German Collection of Microorganisms and Cell Cultures (DSMZ, www.dsmz.de, Braunschweig, Germany) and the S9 liver homogenate was obtained from MP Biomedicals, Eschwege, Germany. The concentrations resulting in 50% effect (EC50)were derived from a log-logistic fit of the concentration-effect curves (22). Mixture experiments in the chlorophyll fluorescence test were performed in a fixed ratio design (22) with the three β-blockers mixed in the ratio of their EC50. Data evaluation and comparison with the predictions for concentration addition and independent action were performed as described in ref 22. Baseline Toxicity Versus Specific Mode of Toxic Action. The toxic ratio TRi (eq 1) is defined as the ratio of the predicted baseline effect concentration EC50baseline,i of a given compound i to the experimentally determined EC50experimental,i. TR indicates whether a compound acts according to baseline toxicity or a specific mode of toxic action (32). TRi < 10 corresponds to baseline toxicity, and TR g 10 indicates a specific mode of toxic action (32).
TRi )
EC50baseline,i EC50experimental,i
(1)
EC50baseline,i can be derived from a QSAR of baseline toxicity in the respective test system. The baseline QSAR for the 24-h chlorophyll fluorescence assay is given by eq 2 (22).
log(1/EC50baseline,i(M)) ) (0.91 ( 0.09) . log Dlipw,i (pH7) + (1.10 ( 0.28) (2)
Estimation of the Metabolite Toxicity. The identity of the human metabolites and the fractions excreted were compiled from the literature (14, 23-25, 33). We defined ranges of fractions of metabolites with the rules detailed in the Supporting Information. Dlipw,i(pH 7) was used as descriptor of lipophilicity because many metabolites are ionized (34) and was calculated from the liposome-water partition coefficient of the different chemical species j of metabolite i, Klipw,ji with eq 3.
Dlipw,i(pH7) )
∑f ‚K j
(3)
lipw,ij
j
The fractions of the different charged and neutral species j, fj, were calculated from the acidity constants of the given metabolite according to (34). If metabolism did not change the basic functional group of the β-blockers, which are aliphatic amines with acidity constants above 9, the same speciation was assumed for the metabolites as for the parent compounds. If an acidic function was formed, the acidity constant was researched in databases (e.g., Physprop, http://www.syrres.com/esc/physprop.htm) or derived from structurally related compounds using fragment methods (35). The liposome-water partition coefficient of the neutral species of each metabolite i, Klipw,i neutral (j ) neutral) was calculated from the octanol-water partition coefficient of the metabolite i with eq 4.
log Klipw,i neutral ) 0.904 ‚ logKow + 0.515
(4)
Equation 4 was derived by Vaes et al. (36) for polar baseline toxicants and is used here because all investigated compounds are polar. The corresponding Klipw,ij for charged species (j ) anionic or cationic) was assumed to be approximately 1 order of magnitude lower than that of the corresponding neutral species (eq 5) (37).
log Klipw,ij ) log Klipw,i neutral - 1
(5)
The Kow values for the neutral species of the metabolites were estimated from structural information and data from the parent drug or structurally similar compounds using fragment methods (35, 38). The Dlipw,i(pH 7) values were used to predict baseline effect concentrations of the metabolites EC50baseline,i with the QSAR given in eq 2. These values describe the minimum toxicity of the metabolites. If a metabolite has the same toxic effect as the parent drug, its EC50specific,i was computed with eq 6 using the toxic ratio of the parent drug identified in the chlorophyll fluorescence assay, TRparent (eq 1, i ) parent).
log(1/EC50specific,i) ) log(1/EC50baseline,i) + logTRparent (6) The range of relative potencies of the metabolite i in relation to the parent drug was derived with eqs 7 and 8. The EC50specific,parent corresponds to the experimentally determined EC50. The minimum relative potency RPmin,i (eq 7) applies if the metabolite is a baseline toxicant (realistic scenario I) and the maximum relative potency RPmax,i (eq 8) assumes that the metabolite has the same specific effect as the parent drug (worst case scenario II). Note that this computation is mathematically equivalent to both parent and metabolite being baseline toxicants. Additionally, for the parent drugs, we define RPmin, parent ) RPmax,parent )1.
RPmin,i )
EC50specific,parent EC50baseline,i
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RPmax,i )
EC50specific,parent EC50specific,i
(8)
Figure 1 illustrates the derivation of the different parameters. In principle, it is also possible that metabolites elicit a higher intrinsic toxicity than the parent drugs (i.e., TRi > TRparent). Examples are known for environmental degradation of pesticides (39). However, in the case of human metabolism of pharmaceuticals (with the exception of pro-drugs), we believe that this is less likely, and therefore such a scenario was not considered. The total toxic potential TPmin/max sums up the relative potencies normalized to the fractions excreted fexcreted,i (eq 9).
TPmin/max ) fexcreted,parent +
∑f
excreted,i‚RPmin/max,i
(9)
i
TPmin is an estimate under the assumption that all metabolites are baseline toxicants and TPmax corresponds to the metabolites acting specifically. If TPmin or TPmax are above 1, the metabolite mixture released into the wastewater is more potent than the parent drug, and if they are below 1, the mixture of metabolites is less potent. The apparent EC50, EC50apparent,specific ,and EC50apparent,baseline are the EC50 of the parent drugs, EC50specific,parent normalized by the TP (eqs 10 and 11). These values represent concentrations of parent drugs before metabolism that would elicit 50% effect after metabolism.
EC50apparent,specific )
EC50specific,parent TPmax
(10)
EC50apparent,baseline )
EC50specific,parent TPmin
(11)
Results and Discussion Liposome-Water Distribution Ratio at pH 7. The reported literature data of Dlipw(pH7) varied by more than 2 orders of magnitude for one given compound (Table 1). They stem from different sources and were measured with different liposome compositions, which might explain the differences, and were, thus, not suitable for our analysis. Our experimentally determined log Dlipw(pH7) (Table 1) correlate linearly with logKow (eq 12).
log Dlipw (pH7) ) (0.76 ( 0.14)‚logKow + (0.26 ( 0.32) n ) 3, r2 ) 0.968 (12) Equation 12 cannot directly be compared with eq 4 because at pH 7, all β-blockers are fully protonated and therefore cationic, while eq 4 holds only for neutral compounds. Cations typically partition about 1 order of magnitude less into liposomes than their corresponding neutral species (37) but for propranolol this difference is slightly smaller (40). If we predict the neutral Klipw from the experimental data at pH 7, i.e., we estimate log Klipw ) log Dlipw (pH7) + 1 with a combination of eq 3 and 5 (using the experimental Dlipw (pH7); Table 1), the resulting log Klipw values fall on the line of eq 4 for propranolol and metoprolol. Atenolol partitions stronger into liposomes than expected from this comparison, but with its low hydrophobicity it is out of the validity range of eq 4. Effects in the Test Battery: Baseline Toxicity and Inhibition of Energy Transduction. In the Kinspec test for baseline toxicity and uncoupling of oxidative and photophosphorylation, propranolol turned out to be a baseline toxicant (22, 41). The 30-min bioluminescence inhibition test with the marine bacterium Vibrio fischeri also yields information about the inhibition of energy transduction. The 7404
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FIGURE 1. Derivation of the relative potencies of the metabolites in relation to the parent compound. For definition of terms please refer to the Experimental Section. 30-min EC50 values for the β-blockers in this test system ranged from 81 to 1304 mg/L (Table 1), sotalol did not show an effect up to the highest concentrations tested (1 g/L). The EC50 of propranolol with 81 mg/L is very similar to literature data (61 mg/L, ref 12). A comparison using a rescaled Kow-based QSAR from the literature (34 adapted from ref 42) indicates that the three β-blockers act as baseline toxicants in this test with TR-values between 0.3 and 2.5 (Table 1, also see Figure S-1 in the Supporting Information). Estrogenic and Androgenic Effects. There was no indication for a receptor-mediated estrogenic or androgenic effect as tested with the YES and the YAS. Concentrations tested ranged from 10-7 to 10-3 M for metoprolol and propranolol and 10-5 to 10-4 M for atenolol, and induction of the estrogen/ androgen receptor followed by production of the β-galactosidase never exceeded the variability of the controls. There was a strong growth inhibition of the yeast cells at concentrations above 5 × 10-4 for metoprolol and propranolol indicating nonspecific cytotoxicity. Additionally, the β-blockers did not affect the potency of the reference compounds estradiol (in the YES) and dihydroxytestosterone (in the YAS), indicating no antiestrogenic or antiandrogenic effects. Reactive Modes of Action. In the umuC test, none of the β-blockers showed any effect up to concentrations of 10-3 M. However, at 10-3 M, propranolol showed a significant induction of the DNA repair mechanisms after metabolic activation with the commercially available metabolic enzymes (S9 extract) (see Figure S-2 in the Supporting Information for details). This concentration is not environmentally relevant. It is known that polycyclic aromatic hydrocarbons can be activated by mixed function oxidases, but activity typically starts at three condensed aromatic rings, and naphthalene is not considered active (43). However, propranolol is a naphthalene with hydrophobic substituents, which enhance solubility (naphthalene would be too hydrophobic to be soluble at 10-3 M), which could explain our observation. Additionally, propranolol did not show any reactive toxicity in the E. coli bioassays for protein and DNA damage (22). Chlorophyll Fluorescence Test. In the test for photosynthesis inhibition after 24-h growth using the ToxY-PAM, all β-blockers showed an activity that was clearly higher than expected from baseline toxicity (Table 1; Figure 2, for concentration-effect curves see Figure S-3, Supporting Information). Sotalol did not shown any effect up to a concentration of 10-2 M. The EC50 value of propranolol is slightly higher than reported earlier for this test system (22), but the concentrations in the test were confirmed by chemical analysis in the present study.
TABLE 1. Physicochemical Descriptors and Effect Concentrations of the β-blockers in the Mode-of-action-Based Test Battery. No Effect Concentrations Could Be Derived in the YES and YAS, and in the Assays for Reactive Toxicity liposome -water octanolmolecular distribution water weight of ratio at pH 7 the free acidity partition constant coefficient base logDlipw Dlipw (g/mol) pKa log Kow at pH 7 at pH 7 propranolol metoprolol atenolol sotalol
295.8 267.3 266.3 308.8
9.24a 9.7b 9.55b n.a.
3.48a 1.88b 0.16b 0.24c
3.06 1.43 0.51 n.d.
1150(19 27.0 ( 1.0 3.2 ( 0.3 n.d.
literature data
bioluminescence inhibitione
logDlipw at pH 7
log (1/EC50(M))g
4.73b,2.5d,2.77a 3.43c 3.18c, 1.0d n.a.
3.56 (3.61-3.52) 3.27 (3.32-3.18) 2.31 (2.36-2.27) 1000
0.3 2.5 1.5 n.d.
log (1/EC50(M))g 4.86 (4.89-4.82) 3.82 (3.85-3.80) 2.30 (2.31-2.29) 3000
9.6 26.6 5.5 n.d.
a Reference 45. b Reference 46. c The data is from the Physprop database, accessible at http://www.syrres.com/esc/physprop.htm. d Reference 47. e The data is from a 30-min bioluminescence inhibition test with the marine bacterium Vibrio fischeri. f Chlorophyll fluorescence test for photosynthesis inhibition after 24-h growth using the ToxY-PAM with the endpoint “inhibition of PSII quantum yield”. g The numbers in parentheses are 95% confidence intervals. h The EC50 in mass units are defined with reference to the free base. i The toxic ratio derived with eq 1 and the baseline QSAR log(1/EC50baseline,i(M)) ) 0.79‚log Dlipw,i (pH7) + 1.54 (34 adapted from ref 42). j The toxic ratio derived with eq 1 and EC50baseline,I calculated with eq 2. N.d. ) not determined; n.a. ) not available.
FIGURE 2. Effect concentrations EC50 of the parent drugs in the chlorophyll fluorescence test (ToxY-PAM) as a function of lipophilicity expressed as logDlipw(pH7). The drawn line represent the baseline QSAR (eq 2), the arrows indicate the toxic ratios TRi (eq 1). The chlorophyll fluorescence test was more sensitive than the bioluminescence inhibition test with Vibrio fischeri. Toxicity increased with hydrophobicity from atenolol via metoprolol to propranolol. The toxic ratio in algae ranged from 6 to 27 (Table 1), clearly indicating a specific mode of toxic action. The EC50 values are by a factor of 2 to 8 higher that those reported by Cleuvers for the same algal species in a 72-h growth inhibition test (44). This difference in sensitivity was presumably caused by the exposure time, which was three times shorter in our test and the endpoint, efficiency of photosynthesis, is very sensitive to PSII-inhibitors but not as sensitive to compounds with other modes of action. A more detailed comparison with various literature ecotoxicity data is given in Tables S-1 to S-3 in the Supporting Information. We mixed the three β-blockers in the ratio of their EC50values and the resulting mixture toxicity clearly followed the model for concentration addition (see Figure S-4 in the Supporting Information), supporting the conclusion that the three β-blockers exhibit the same mode of toxic action in algae. Again, these results are consistent with the experiments of Cleuvers (44) at high effect levels but give a slightly different view at lower effect levels, where Cleuvers observed that the mixture effect exceeded the prediction for concentration addition. For a further elucidation of the mode of toxic action, we explored the rapid induction kinetics of chlorophyll fluorescence in dark-adapted algae induced by a saturating flash of light, which is described in the Supporting Information. We concluded that the β-blockers have an indirect effect on photosynthesis.
Hazard Assessment of the Metabolites. The detailed reaction pathways of the three β-blockers atenolol, metoprolol, and propranolol are given in Figures S-5 to S-7 in the Supporting Information, together with Tables S-4 to S-6 listing the fractions of metabolites formed and their hydrophobicity estimated with eqs 3-5. Table 2 gives an overview of the metabolites, the fractions of excreted metabolite and the estimated logDlipw(pH7) condensed from Tables S-4 to S-6. All metabolites are oxidation products and/or conjugates and thus show reduced hydrophobicity. For the calculations, we assumed that all conjugates were glucoronides, which are hydrophilic but still neutral conjugates and contribute more to hydrophobicity than sulfonates and other charged conjugates would do. We did not perform this analysis for sotalol because it did not show any effect in the test battery. Propranolol is extensively metabolized and less than 10% is excreted as parent drug (Table 2), mostly in feces. The dominant metabolites are 4-hydroxypropranolol, which is a base like propranolol and consequently fully protonated at pH 7, and naphthoxylactic acid, which is a carboxylic acid and consequently fully negatively charged at pH 7. Metoprolol is less extensively metabolized and has one dominant metabolite, 4-(2-hydroxy-3-isopropylamino-propoxy)phenylacetic acid, which is a zwitterion at pH 7 (Table 2). Due to its overall neutrality we treated it as neutral compound but it is possible that zwitterions have smaller Dlipw(pH7) than their corresponding neutral species (37). In contrast to the two more hydrophobic β-blockers, the hydrophilic atenolol is hardly biotransformed (Table 2) and almost equal fractions of the parent compound are excreted via feces and urine (see S-4 in the Supporting Information). The hydroxylated and the conjugated metabolites make up less than 10%. This analysis of metabolite formation shows clearly that especially for propranolol and metoprolol an environmental hazard assessment should take internally formed metabolites into account. The hazard assessment of the metabolites was performed for the 24-h algal chlorophyll fluorescence test because algae appear to be the most sensitive species both in our test battery and when analyzing acute toxicity data for algae, daphnia, and fish (see the Supporting Information). However, this analysis can readily be applied to any ecotoxicity test, for which a baseline QSAR is available. The EC50baseline,i for each metabolite i were estimated with the baseline QSAR for this test (eq 2) and are listed in Table 2. It is very likely that the metabolites have lost their specific mode of action in algae and act merely as baseline toxicants. We define this case as scenario I. Scenario I represents the realistic case. As a more precautionary scenario, we adVOL. 40, NO. 23, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Metabolite Pattern for Propranolol, Metoprolol and Atenolol, Hydrophobicity of the Metabolites and Their Relative Potency metabolitea
fexcreted,i (%min to %max)a
logDlipw (pH7)a
propranolol propranolol-glucoronide 4-hydroxypropranolol 4-hydroxypropranolol-glucoronide norpropranolol naphthoxylactic acid naphthoxyacetic acid naphthol dihydroxynaphthalene
1.5-9 17-25 10-41 0-30 0-5 22-42 0-20 0-5 0-5
3.07 2.61 2.08 2.01 2.07 0.71 1.80 2.95 2.34
3.89 3.48 2.99 2.93 2.98 1.75 2.74 3.78 3.23
4.86 4.44 3.96 3.90 3.95 2.71 3.70 4.75 4.20
metoprolol metoprolol-glucoronide O-desmethylmetoprolol 4-(2-hydroxy-3isopropylamino-propoxy)-phenylacetic acid R-hydroxymetoprolol 2-hydroxy-3[4-(2methoxyethyl)phenoxy] propionic acid
5 to 15 0 1 65
1.43 1.17 0.63 1.50
2.40 2.16 1.67 2.47
3.82 3.58 3.09 3.88
1.00 0.58 0.19 1.16
1.00c 0.02 0.01 0.04
-0.47 0.55
0.67 1.60
2.09 3.02
0.02 0.16
0.00 0.01
atenolol atenolol-glucoronide hydroxyatenolol
70-96 0.8-4.4 1.1-4.4
10 10
log(1/EC50 baseline(M))
b
log(1/EC50 specific(M))
b
specific effect RPmax,i
baseline effect RPmin,i
1.00 1.00c 0.38 0.04 0.13 0.01 0.11 0.01 0.12 0.01 0.01 0.00 0.07 0.01 0.78 0.08 0.22 0.02 TPmax ) 0.09 to 0.34d TPmin ) 0.02 to 0.12e
TPmax ) 0.82 to 0.92d TPmin ) 0.08 to 0.18e 0.51 -0.39 -2.02
1.56 0.75 -0.74
2.30 1.39 0.00
1.00 1.00c 0.12 0.03 0.00 0.00 TPmax ) 0.70 to 0.97d TPmin ) 0.70 to 0.96e
a Chemical structures, metabolic pathways, details of the literature survey and calculations of D lipw(pH7) using eqs 3 to 5 and estimated Kow are given in Figures S5-S7 and Tables S4-S6 in the Supporting Information. b The EC50 values are expressed in molar concentrations (M) and must be divided by the concentration unit before drawing the logarithm. All data were modeled with eqs 2-6 with exception of the EC50specific of the parent compound propranolol, which is the experimental value. c See comment on eq 7: for the parent compound RPmin,parent ) RPmax,parent ) 1. d Maximum toxicity potential of metabolites under the assumption that all metabolites exhibit specific toxicity, TPmax, ranges from ∑ fexcreted,i,min × RPmax,i to ∑ fexcreted,i,max‚RPmax,i. e Minimum toxicity potential of metabolites under the assumption that all metabolites exhibit baseline toxicity, TPmin, ranges from ∑ fexcreted,i,min × RPmin,i to ∑ fexcreted,i,max‚RPmin,i.
ditionally estimated the EC50specific effect,i for each metabolite using eq 6 and the experimental relative potency of the respective parent drug (scenario IIsworst case scenario). From EC50baseline,i and EC50specific effect,i we estimated the minimum relative potency RPmin,i (eq 7) and the maximum relative potency RPmax,i (eq 8), respectively. In general RPmin,i and RPmax,i were smaller than one, confirming the detoxification by biotransformation. A notable exception was 4-(2hydroxy-3-isopropylamino-propoxy)phenylacetic acid with a RPmax,i of 1.16. As discussed above, this compound is zwitterionic, therefore its lipophilicity might have been overestimated. The total toxic potential (TP) was then summed up with eq 9. This summation method applies only for compounds with the same mode of action (48). For those compounds the mixture toxicity concept of concentration addition is applicable. Under the worst-case scenario II, the TP of the metabolite mixture stemming from the biotransformation of propranolol was 9-34% compared to the parent compound, assuming the same specific mechanism of the metabolites as propranolol in algae. The corresponding values for metoprolol and atenolol covered 82-92% and 70-97%, respectively. Consequently, the degree of detoxification by metabolism was highest for propranolol and negligible for metoprolol and atenolol. Hence, for propranolol, a hazard assessment based exclusively on the parent compound does not reflect the effective ecotoxic potential, provided the drug passed a patient before it is emitted to the wastewater stream. It is possible that patients dispose of unused medication directly via the toilet. However, considering that β-blockers are used by people with heart problems on a regular basis 7406
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(unlike drugs that fight infections), we assume this direct emission pathway to be quantitatively negligible. In contrast to propranolol, a hazard assessment of the parent drug yields quite realistic values for metoprolol and atenolol, provided that the metabolites exhibit the same specific mode of action in algae as the parent drugs. This is typical for compounds that are not strongly metabolized. However, in the realistic scenario I, assuming only baseline toxicity of the metabolites, the inclusion of the metabolites in the analysis always led to a drastic decrease of TP compared to the parent compounds. For propranolol and metoprolol, the TP was reduced to less than 5% and atenolol-metabolite mixtures had the same TP as in scenario II because 70-97% are excreted as parent drug and the metabolites contribute in neither of the scenarios to the overall toxicity. To assess the influence of metabolism and to compare the overall hazard of the three β-blockers, we rescaled the EC50 of the parent drugs with the total toxic potential of the metabolite mixture (eq 9) and derived apparent EC50 (eqs 10 and 11). Again, we assumed that all metabolites have the same specific mode of action as the parent compound (scenario II) and alternatively, that all metabolites are baseline toxicants (scenario I). These apparent EC50 quantify the amount of parent compound needed to induce 50% effect after metabolism. In Figure 3, the EC50 of the parent drugs and the two apparent EC50, EC50apparent,specific, and EC50apparent,baseline are plotted as a function of Dlipw(pH7) of the parent drug. Since the metabolite pattern covers a certain range due to data uncertainty and biological variability, also the ranges of the apparent EC50 are given.
FIGURE 3. Reduction of toxicity by metabolism. Effect concentrations EC50 in the chlorophyll fluorescence test (expressed as negative logarithm of the EC50) as a function of lipophilicity of the parent drug expressed as logDlipw(pH7). 9 EC50 of the parent drugs; O realistic scenario (I): apparent EC50 of the metabolite mixture assuming baseline toxicity of the metabolites, EC50apparent,baseline; ] worst case scenario (II): apparent EC50 of the metabolite mixture assuming that the metabolites exhibit the same mode of action as the parent compounds, EC50apparent,specific; the two points represent the range from fexcreted,imin to fexcreted,imax. For atenolol and metoprolol, metabolism did not change the potency by a significant factor in the worst-case scenario. In contrast, the EC50apparent,specific of propranolol was increased compared to the EC50parent by a factor of 3 to 11. With this reduction of toxicity by metabolism, the lower range of EC50apparent,specific of propranolol is in the same concentration range as the EC50apparent,specific of metoprolol. Thus, while in a comparative hazard assessment of the three β-blocker as parent drugs, a higher hazard potential would be assigned to propranolol than to metoprolol, a hazard assessment that accounts for metabolite distribution after human metabolism would show that both compounds pose almost equal hazards to aquatic organisms. The picture looks quite different if we assume that the metabolites act as baseline toxicants (realistic scenario; Figure 3). For metoprolol and propranolol, the apparent toxicity EC50apparent,baseline is by one to 2 orders of magnitude lower than the specific toxicity EC50apparent,specific. For propranolol the toxicity reduction is largest, with an EC50apparent,baseline 9-42 times higher than the EC50 of the parent drug. Despite the fact that the relative toxicity reduction is negligible for atenolol and higher for propranolol compared to metoprolol, the relative toxicity ranking remains the same with this analysis but the potency differences shrank. Of course such a predictive model is limited by uncertainties: First, there is variability of excretion products and inconsistency of different data sets, which also reflects biological variability. Second, the effect concentrations of the metabolites are estimated and the models for this estimation are based on a number of assumptions, each of which introduces uncertainty. The major source of uncertainty is the assignment of the mode of toxic action of the metabolite. We, therefore, reported the two scenarios I and II. We did not consider more intrinsically toxic metabolites to be formed, this case should be the exception but it is theoretically possible and adds to the uncertainty of the overall result. Nevertheless, considering the lack of experimental data and even the availability of the metabolites in their pure form, this approach is already an improvement over ignoring the metabolites formed. In future, we plan to experimentally confirm this model with one selected case study in the project ERAPharm (20). Here, we focused on hazard assessment with the modeof-action-based test battery, where the chlorophyll fluores-
cence test was found to be the most sensitive and specific test for the investigated β-blockers. However, this analysis can be extended to any pharmaceutical or other environmental pollutant in any given test system provided that the metabolite pattern and the effect concentration of the parent drug plus a baseline QSAR in the given test system are available. In Figure S-8 of the Supporting Information, we illustrate this approach additionally for the bioluminescence inhibition test with V. Fischeri, which boils down to the “realistic scenario” since all compounds acted as baseline toxicants in V. Fischeri. This analysis does not account for further biotransformation in the wastewater treatment plant and in receiving surface waters. However, if the metabolite patterns of these processes were known, they could easily be included and the analysis extended to transformation in the environment. In fact, we are presently also applying this approach to the analysis of environmental metabolites of pesticides. We hope to have demonstrated the utility of the method as a relatively simple screening approach to initial hazard assessment of pharmaceuticals. Moreover, this study shows that metabolites should be included in environmental risk assessment of pharmaceuticals.
Acknowledgments We thank Karin Gu ¨ del, Brenda Bonnici, and Timur Bu ¨ rki for literature research of pharmacokinetic data, Sibylle Rutishauser for experimental assistance, and Karen Duis, Kathrin Fenner, Jeanne Garric, and Tom Hutchinson for reviewing the manuscript. This study was partially funded by the Swiss Federal Office for the Environment (FOEN) and mainly by the European Union under the 6th framework program in the STREP ERAPharm (SSPI-CT-2003-511135).
Supporting Information Available Additional information on (1) detailed results of the biotests, (2) rules for assessing distribution of the metabolites, (3) reaction scheme of human metabolism and product distribution, (4) estimated physicochemical descriptors of the metabolites, and (5) metabolite analysis for a second toxicity endpoint. This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review December 22, 2005. Revised manuscript received May 2, 2006. Accepted May 8, 2006. ES052572V
