Chemoavailability of Organic Electrophiles: Impact of Hydrophobicity

Article pubs.acs.org/crt

Chemoavailability of Organic Electrophiles: Impact of Hydrophobicity and Reactivity on Their Aquatic Excess Toxicity Alexander Böhme,† Anja Laqua,†,‡,§ and Gerrit Schüürmann*,†,‡ †

UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany Institute for Organic Chemistry, Technical University Bergakademie Freiberg, Leipziger Str. 29, 09596 Freiberg, Germany

‡

S Supporting Information *

ABSTRACT: Organic electrophiles have been recognized as important components of the exposome that can be characterized as cumulative totality of exposure in the organism in response to environmental perturbation. For such compounds, chemical reactivity may contribute significantly to the toxicological profile through covalent attacks at nucleophilic sites of peptides such as glutathione (GSH), proteins, lipid components, and the DNA and RNA. Employing a Michael acceptor set of 58 α,β-unsaturated carbonyls with 15 ketones, 18 aldehydes, and 25 esters, the hydrophobicity and reactivity contributions to their toxicity enhancement Te over baseline narcosis with the ciliates Tetrahymena pyriformis is analyzed through a conceptual model, featuring toxicokinetic phase transfer steps and the reactive molecular initiating event (MIE) at endogenous target sites exposed to water-rich or water-poor compartments. To this end, hydrophobicity was quantified by the octanol/water partition coefficient, Kow, electrophilic reactivity through second-order rate constants of reaction with GSH in a kinetic chemoassay, kGSH, and Te as the ratio of narcosis-level vs experimental concentration yielding 50% growth inhibition of the ciliates within 48 h of exposure. The observed decrease of log Te with increasing log Kow can be traced back to a rate-determining impact of the toxicant transfer from the membrane to the intracellular cytosol. Moreover, the recently introduced concept of chemoavailability is shown to enable, from knowledge of log Kow and log kGSH alone, a screening-level discrimination between reactive and hydrophobic MIEs triggering predominantly alone or in parallel respective adverse outcome pathways (AOPs) including the diffusion-control limit of reactive MIE saturation. As such, chemoavailability may aid in evaluating prevalent MIEs expected for a given organic electrophile and in assessing its toxicological profile within AOP schemes addressing aquatic toxicity.

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link to Alzheimer’s disease,18 and quinones form a prominent compound class with a toxicological profile governed by both electrophilic reactivity and ROS-inducing redox cycling.19 In aquatic toxicology, research into electrophilic modes of action has been undertaken for three decades,15−17,20,21 employing the concept of excess toxicity as a pragmatic potency criterion for their identification.20 From this viewpoint, chemical reactivity would translate into a toxicity increase as compared to baseline narcosis, the latter of which is typically proportional to hydrophobicity in terms of the logarithmic octanol/water partition coefficient, log Kow. Here, aquatic narcosis is understood as resulting from a nonspecific impairment of cellular membranes that increases with increasing membrane affinity, allocating to Kow the two roles of capturing the uptake efficiency of waterborne chemicals into aquatic species, and the chemical affinity for accumulating in and thus interfering with nerve membranes. Interestingly enough, the toxicity enhancement Te (quantified as ratio of the median effect concentrations of narcosis-

INTRODUCTION Electrophilic compounds have been recognized as priority components of the exposome, which is the cumulative totality of chemical exposure in the organism in response to chemical and nonchemical stress exerted by the environment.1−3 More specifically, both exogenous and endogenous electrophiles belong to the systems chemistry of the organism that triggers adverse outcome pathways4 (AOPs) in the systems biology context of the organism. For such substances, the molecular initiating event (MIE) leading to toxicity often proceeds through a covalent attack at nucleophilic sites of peptides, proteins, lipids, and the DNA. The resultant variety of pathological processes includes dermal and respiratory sensitization,5−8 mutagenicity,6,9,10 and impairment of cellular energy metabolism through inhibiting glycolysis. The latter may also generate reactive oxygen species (ROS) that in turn trigger indirect genotoxicity and phospholipid degeneration.11,12 Organic electrophiles are also reported to contribute to neurotoxicity and teratogenicity,13,14 and a typical impact on aquatic organisms is excess toxicity.15−17 Moreover, electrophilic metabolites may be formed through lipid peroxidation providing a mechanistic © XXXX American Chemical Society

Received: September 21, 2015

A

DOI: 10.1021/acs.chemrestox.5b00398 Chem. Res. Toxicol. XXXX, XXX, XXX−XXX

Article

Chemical Research in Toxicology

Table 1. Data Set of 58 α,β-Unsaturated Carbonyls Covering 15 Ketones, 18 Aldehydes, and 25 Esters with Information about Their Electrophilic Reactivity (kGSH), Hydrophobicity (Kow), Chemoavailability (DkK), Ciliate Toxicity (EC50), and Toxicity Enhancement over Baseline Narcosis (Te)a compound

CAS number

no.

1-pentene-3-one 1-hexene-3-one 1-octene-3-one 3-hexyne-2-one 3-pentene-2-one 2-octene-4-one 2-cyclopentene-1-one 4-hexene-3-one 3-heptene-2-one 3-octene-2-one 3-nonene-2-one 3-methyl-3-pentene-2-one 4-methyl-3-pentene-2-one 2-methyl-2-cyclopentene-1-one 3-methyl-2-cyclopentene-1-one

1629−58−9 1629−60−3 4312−99−6 1679−36−3 625−33−2 4643−27−0 930−30−3 2497−21−4 1119−44−4 1669−44−9 14309−57−0 565−62−8 141−79−7 1120−73−6 2758−18−1

A1 A2 A3 A4 A5 A6 A7 A8 A9 B1 B2 B3 B4 B5 B6

2-octynal phenyl propiolaldehyde methyl acrolein 2-ethyl acrolein trans-2-pentenal trans-2-hexenal trans-2, cis-6 nonadienal trans-2-octenal trans-2-nonenal 4-methyl-2-pentenal trans-2-decenal trans,trans-2,4-hexadienal trans,trans-2,4-heptadienal cinnamaldehyde trans,trans-2,4-nonadienal 3-methyl-2-butenal trans-2-methyl-2-butenal trans-2-methyl-2-pentenal

1846−68−0 2579−22−8 78−85−3 922−63−4 1576−87−0 6728−26−3 557−48−2 2548−87−0 18829−56−6 5362−56−1 3913−81−3 142−83−6 4313−03−5 104−55−2 5910−87−2 107−86−8 497−03−0 623−36−9

C1 C2 C3 C4 C5 C6 C7 C8 C9 D1 D2 D3 D4 D5 D6 D7 D8 D9

methyl propiolate ethyl propiolate tert-butyl propiolate propargyl acrylate 2-hydroxyethyl acrylate allyl acrylate 2-hydroxypropyl acrylate methyl acrylate ethyl acrylate n-propyl acrylate iso-butyl acrylate n-butyl acrylate methyl-2-hexynoate methyl-2-octynoate tert-butyl acrylate ethyl-2-butynoate methyl-trans-2-hexenoate methyl-trans-2-octenoate acrylamide propargyl methacrylate methyl crotonate ethyl crotonate methyl methacrylate

922-67-8 623-47-2 13831-03-3 10477-47-1 818-61-1 999-55-3 999-61-1 96-33-3 140-88-5 925-60-0 106-63-8 141-32-2 18937-79-6 111-12-6 1663-39-4 4341-76-8 2396-77-2 7367-81-9 79-06-1 13861-22-8 623-43-8 623-70-1 80-62-6

E1 E2 E3 E4 E5 E6 E7 E8 E9 F1 F2 F3 F4 F5 F6 F7 F8 F9 G1 G2 G3 G4 G5

kGSH(±)ΔkGSH [L·mol−1·min−1] 15 α,β-Unsaturated Ketones 1261 ± 63 1173 ± 30 1074 ± 13 80.0 ± 1.3 26.7 ± 0.9 26.1 ± 0.5 25.6 ± 0.1 24.2 ± 0.2 12.5 ± 0.3 11.4 ± 0.2 10.8 ± 0.2 0.779 ± 0.018 0.208 ± 0.007 0.200 ± 0.001 0.074 ± 0.002 18 α,β-Unsaturated Aldehydes 487 ± 26 446 ± 28 203 ± 6 59.4 ± 0.9 28.3 ± 1.7 25.1 ± 1.9 22.8 ± 2.2 18.0 ± 0.1 15.1 ± 0.3 10.6 ± 0.5 10.1 ± 0.1 6.74 ± 0.34 5.65 ± 0.21 5.49 ± 0.17 3.49 ± 0.43 1.71 ± 0.07 0.474 ± 0.022 0.274 ± 0.035 25 α,β-Unsaturated Esters 117 ± 8 105 ± 5 62.6 ± 1.9 51.4 ± 2.9 19.9 ± 0.8 19.6 ± 1.1 18.0 ± 1.1 11.4 ± 0.3 10.6 ± 0.1 10.2 ± 0.3 9.40 ± 0.41 8.54 ± 0.20 5.70 ± 0.15 2.76 ± 0.14 2.50 ± 0.09 2.42 ± 0.20 2.18 ± 0.32 0.785 ± 0.030 0.556 ± 0.008 0.220 ± 0.020 0.164 ± 0.005 0.161 ± 0.002 0.072 ± 0.004 B

log kGSH

log Kow

DkK

log EC50 [mol/L]

log Te

3.10 3.07 3.03 1.90 1.43 1.42 1.41 1.38 1.10 1.06 1.03 −0.11 −0.68 −0.70 −1.13

0.90 1.39 2.37 0.52 0.82 2.29 0.71 1.31 1.80 2.29 2.79 1.37 1.37 1.26 1.26

2.20 1.68 0.66 1.38 0.61 −0.87 0.70 0.07 −0.70 −1.23 −1.76 −1.48 −2.05 −1.96 −2.39

−4.53 −4.69 −4.99 −4.34 −3.56 −4.08 −3.64 −3.95 −3.75 −3.82 −4.07 −2.71 −2.38 −2.21 −1.70

2.76 2.49 1.93 2.90 1.86 1.10 2.04 1.82 1.19 0.84 0.65 0.53 0.20 0.12 −0.39

2.69 2.65 2.31 1.77 1.45 1.41 1.36 1.26 1.18 1.03 1.00 0.83 0.75 0.74 0.54 0.23 −0.32 −0.56

2.07 1.32 0.74 1.23 1.09 1.58 2.84 2.57 3.06 1.51 3.55 1.37 1.86 1.82 2.84 1.15 1.15 1.64

0.62 1.33 1.57 0.54 0.36 −0.17 −1.48 −1.31 −1.88 −0.48 −2.55 −0.54 −1.11 −1.08 −2.30 −0.92 −1.47 −2.20

−4.78 −4.36 −3.69 −3.94 −3.69 −3.82 −4.41 −4.28 −4.73 −3.90 −4.98 −3.81 −3.98 −3.74 −4.32 −3.16 −2.90 −3.03

1.99 2.22 2.05 1.88 1.75 1.46 0.95 1.06 1.07 1.59 0.91 1.63 1.37 1.17 0.86 1.17 0.91 0.61

2.07 2.02 1.80 1.71 1.30 1.29 1.26 1.06 1.03 1.01 0.97 0.93 0.76 0.44 0.40 0.39 0.32 −0.11 −0.25 −0.66 −0.79 −0.79 −1.14

0.09 0.58 1.45 0.94 −0.25 1.57 0.17 0.73 1.22 1.71 2.13 2.20 1.62 2.6 2.09 1.13 2.12 3.1 −0.81 1.49 1.44 1.63 1.28

1.98 1.44 0.35 0.77 1.55 −0.28 1.09 0.33 −0.19 −0.70 −1.16 −1.27 −0.86 −2.16 −1.69 −0.74 −1.80 −3.21 0.56 −2.15 −2.23 −2.42 −2.42

−4.79 −4.75 −4.67 −4.06 −3.71 −3.75 −3.66 −3.57 −3.56 −3.62 −4.24 −3.62 −3.56 −3.98 −3.33 −3.67 −3.28 −3.90 −2.23 −2.76 −2.11 −2.31 −1.83

3.72 3.25 2.42 2.25 2.94 1.39 2.52 1.95 1.51 1.14 1.39 0.72 1.16 0.73 0.52 1.70 0.45 0.21 1.94 0.48 −0.13 −0.10 −0.27


Article

Chemical Research in Toxicology Table 1. continued compound ethyl methacrylate methyl tiglate

CAS number 97-63-2 6622-76-0

no. G6 G7

kGSH(±)ΔkGSH [L·mol−1·min−1] 25 α,β-Unsaturated Esters 0.058 ± 0.005 0.007 ± 0.001

log kGSH −1.24 −2.15

log Kow 1.77 1.69

DkK

log EC50 [mol/L]

log Te

−3.01 −3.84

−2.13 −2.47

−0.40 0.01

a Compound numbering refers to Scheme S1 of the Supporting Information that provides the chemical structures of all 58 test compounds. Electrophilic reactivity toward glutathione, GSH, in terms of the 2nd-order rate constant, kGSH,25,26,32 was determined newly for compounds C6, C9, D5, E3, E5, F2, F4, F5, F7, F8, and G1 using our kinetic GSH chemoassay protocol.25 Log Kow was calculated using KOWWIN v 1.68;39 chemoavailability DkK was calculated according to eq 3; aquatic toxicity toward the ciliates Tetrahymena pyriformis in terms of 48-h log EC50 (effective concentration yielding 50% growth inhibition,24−26,33−35 corrected for exposure loss as outlined in the Supporting Information) was determined newly for compounds E3, F2, F4, F5, F7, and F8 employing our bioassay protocol;16 and toxicity enhancement, Te, was defined through eq 1, employing eq 2 as a baseline narcosis model corrected for concentration loss through volatilization and sorption.16

toward glutathione (GSH) was quantified in terms of second-order rate constants kGSH [L mol−1 min−1] including corrections for oxidative GSH loss (GSSG formation) through DMSO and solution-phase oxygen.25 To this end, previously published experimental data25,26,32 have been augmented by newly measured kGSH values for nine Michael-acceptor esters and three respective aldehydes (see Table 1, with chemicals ordered by compound class and decreasing reactivity). The associated chemical structures and further experimental details are given in the Supporting Information. Since no reaction showed an equilibrium-like situation, significant contributions from retro-Michael additions (loss of GSH-electrophile conjugate) can be excluded. Ciliate Toxicity Bioassay. Experimental toxicity toward the ciliates Tetrahymena pyriformis was quantified as effective compound concentration resulting in 50% growth inhibition within 48 h exposure, EC50. Besides 52 literature data,24−26,33−35 our respective protocol16 has been used for determining the six remaining EC50 values. All log EC50 values have been corrected for compound loss through sorption and volatilization, and are provided in Table 1 (see the Supporting Information for more details). Toxicity Enhancement. The ratio of predicted narcosis-level over experimental (actually observed) toxicity, EC50(narc)/EC50(exp), provides a convenient measure of the reactive component of toxicity,20 although the magnitude of this ratio is not directly proportional to reactivity but depends also on hydrophobicity as discussed in detail below. Originally termed excess toxicity, we prefer the term toxicity enhancement, Te, because it is a dimensionless ratio.16,17,38 The respective logarithmic form reads

level over experimental toxicity; see below) of electrophiles decreases with increasing hydrophobicity,16,17,22,23 challenging the traditional view that hydrophobic membrane irritation and reactive toxicity would contribute independently to the overall effect. At the same time, the aquatic toxicity of organic electrophiles sometimes appears to depend solely on their reactivity,24,25 sometimes on both reactivity and hydrophobicity,23,26,27 and sometimes only on hydrophobicity.22,27 A similar situation holds for skin sensitization, where the respective potency has been reported to either depend on reactivity alone (for SNAr electrophiles more pronounced than for Michael acceptors)28 or on both reactivity and hydrophobicity (for Schiff base formers).29,30 Moreover, hydrophobic contributions to sensitization have been interpreted to indicate a predominantly water-poor target site.31 As noted long ago,22,23 the decreasing toxicity relevance of chemical reactivity with increasing hydrophobicity suggests that the nucleophilic sites attacked by the electrophile are located in aqueous compartments of the organism. In the present study, this reasoning is developed further, employing the concept of chemoavailability27 for quantifying the readiness of compounds to covalently attack waterborne nucleophilic sites of endogenous molecules. The goal was to demonstrate how chemoavailability triggers the hydrophobic and reactive components of toxicity in a predictive manner, and that this may support unravelling prevalent MIEs of organic electrophiles. To this end, 58 α,β-unsaturated carbonyls (Michael acceptors) comprising 15 ketones, 18 aldehydes, and 25 esters with experimental second-order rate constants of reaction with glutathione (GSH), kGSH,25,26,32 have been analyzed. These test compounds have been selected because they can arise from natural as well as industrial sources and are known for the potential impact of their reactivity on toxicity.5,17,24−33 As aquatic model organism, the ciliates Tetrahymena pyriformis were taken because of their known sensitivity toward thiolreactive toxicants.16,24−27,32−35 The data set includes 12 newly determined kGSH values employing our kinetic chemoassay25 and experimental ciliate toxicity values in terms of 48-h effective compound concentrations that yield 50% growth inhibition, EC50. The latter cover 52 literature data24−26,33−35 and six values determined with our respective protocol.16 The resultant model may contribute to unravelling prevalent MIEs of organic electrophiles as part of a respective AOP assessment and thus provide a mechanism-oriented nonanimal component along the lines promoted by the 21st century vision of toxicology.36,37

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log Te = log EC50(narc) − log EC50(exp)

(1)

Prediction of log EC50(narc) proceeded through application of a baseline narcosis model calibrated for Tetrahymena pyriformis including a correction for exposure loss through sorption and volatilization:16 log EC50(narc)[mol/L] = −0.87(± 0.02)log Kow − 0.99( ±0.08) (2) In eq 2, the decadic logarithm of the octanol/water partition coefficient, log Kow, was calculated using the EPISuite software.39 The resultant log Te values are listed in Table 1. Chemoavailability. The chemoavailability of organic electrophiles characterizes their opportunity to covalently attack waterborne nucleophilic sites of endogenous molecules and is quantified as trade-off between electrophilic reactivity characterized through log kGSH and hydrophobicity in terms of log Kow.27 In contrast to the previous definition, however, the sign was changed such that the value of the chemoavailability parameter in its present form, DkK (as difference between the logarithmic values of a kinetic rate constant and an equilibrium constant) now increases with increasing chemoavailability (as discussed below): DkK = log k GSH − log Kow

(3)

With this parametrization, electrophiles with an aqueous-phase concentration sufficient for covalent binding to waterborne sites of endogenous nucleophiles feature a large DkK (large kGSH, small Kow). By contrast, small DkK (small kGSH, large Kow) identifies electrophiles

MATERIALS AND METHODS

Kinetic GSH Chemoassay. The electrophilic reactivity of the 58 α,β-unsaturated carbonyls (15 ketones, 18 aldehydes, and 25 esters) C


Article


Scheme 1. 1,4-Michael Addition of α,β-Unsaturated Ketones (R = alkyl), Aldehydes (R = H), and Esters (R = O-alkyl) to Glutathione, GSHa

a

The initially formed enol eventually tautomerizes to the ketone as the ultimate product.

Figure 1. Aquatic toxicity of 58 Michael-acceptor α,β-unsaturated carbonyls covering 15 ketones (top, blue squares), 18 aldehydes (middle, red triangles), and 25 esters (bottom, green dots) in terms of log EC50 (48-h effective concentration inhibiting population growth of the ciliates Tetrahymena pyriformis; left) or log Te (toxicity enhancement over baseline narcosis, eq 1; middle and right) vs hydrophobicity quantified through log Kow (octanol/water partition coefficient; left and right) or log kGSH (second-order rate constants of reaction with glutathione; middle). The left plots are augmented by the narcosis regression line (eq 2). All logarithmic experimental EC50, Te, and kGSH, and calculated Kow data are listed in Table 1.

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essentially trapped in the lipid phase of the organism so that they cannot arrive at and attack aqueous-phase nucleophilic sites. Thus, DkK represents the trade-off between aqueous-phase concentration and reactivity, both of which trigger the turnover rate of the electrophile− nucleophile reaction (according to pseudo-first-order reactions kinetics reading dcw(t)/dt = k·cw(t) with waterborne electrophile concentration cw and rate constant k, respectively). Statistical Parameters. To evaluate the statistical performance of the derived regression equations, the following parameters have been employed: squared correlation coefficient, r2; predictive squared correlation coefficient evaluated through leave-one-out cross validation, q2cv; root-mean-square error of calibration, rms; cross-validated root-mean-square error of prediction, rmscv; F-test value, Fi,n−(i+1) (with i = number of variables, and n = number of compounds).

RESULTS AND DISCUSSION Data Set Profile. The 58 α,β-unsaturated carbonyls span a Kow range of 4.34 log units (from −0.81 for the ester acrylamide, compound G1 in Table 1, to 3.55 for the aldehyde trans-2-decenal, D2). For the three subsets of 15 ketones, 18 aldehydes, and 25 esters, however, the individual log Kow ranges differ significantly with values of 2.27, 2.81, and 3.91, respectively, but are still sufficient for employing log Kow as regression parameter to unravel the potential impact of hydrophobicity on the toxicity enhancement Te as discussed below. The Michael-acceptor reaction with glutathione (GSH) as nucleophile is outlined in Scheme 1, and triggered by the second-order rate constant kGSH [L mol−1 min−1] that is also D


Article

Chemical Research in Toxicology Scheme 2. Imine Formation through the Reaction of Protein Amino Groups with α,β-Unsaturated Aldehydes

comparison confirms that the Te magnitude is not directly proportional to chemical reactivity but depends also on Kow (that in turn models the aqueous-phase toxicant concentration as shown in the section Kinetic Basis of Chemoavailability below). As a further illustration of this feature, the most reactive compounds do not necessarily show the highest Te; the subsetspecific log Te vs log kGSH data distributions are visualized at the right-hand-side of Figure 1. Taking 1-pentene-3-one as an example, this ketone with the largest kGSH has the secondlargest Te just below the less hydrophobic 3-hexyne-2-one that is significantly less reactive (log Te 2.76 vs 2.90, log Kow 0.90 vs 0.52, and log kGSH 3.10 vs 1.90). Among the aldehydes (middle right of Figure 1), the toxicity enhancement of 2-octynal with the highest electrophilic reactivity is again not the maximum value observed for this compound class and in fact similar to the one of the significantly less reactive and less hydrophobic 2ethyl acrolein (log Te 1.99 vs 1.88, log Kow 2.07 vs 1.23, log kGSH 2.69 vs 1.77). Moreover, the notable difference in Te between the esters methyl propiolate and the almost equally reactive ethyl propiolate is apparently caused by a corresponding difference in hydrophobicity (log Te 3.72 vs 3.25, log Kow 0.09 vs 0.58, and log kGSH 2.07 vs 2.02). Nevertheless, Te broadly increases with reactivity despite some substantial scatter as illustrated in the middle of Figure 1 (from top to bottom: ketones, aldehydes, and esters). The log− log dependence of Te on Kow is shown in the three plots on the right of Figure 1. The overall Te decrease with increasing hydrophobicity is accompanied by significantly outlying compounds and compound clusters. Since in most cases Te is governed by both chemical reactivity and hydrophobicity, regression of log Te on both log kGSH and log Kow offers a route to analyzing the respective contributions in a quantitative manner, which is subject of the following section. Michael-Acceptor log Te Regression. For 57 α,βunsaturated carbonyls (excluding methyl tiglate because its GSH reactivity is too low for a linear relationship;26 see also below), regression of log Te on both hydrophobicity and reactivity yields

listed in Table 1. As compared to Kow, kGSH spans a 10-fold larger range of 5.25 log units (from −2.15 for the ester methyl tiglate, G7, to 3.10 for the ketone 1-pentene-3-one, A1), decomposed differently across the three subsets. Here, the ketones (3-methyl-2-cyclopentene-1-one, B6, at the low end, with −1.13) and esters (from −2.15 for methyl tiglate, G7, to 2.07 for methyl propiolate, E1) cover similar ranges of log kGSH (4.23 vs 4.22), whereas the observed reactivity range is smaller by ca. one log unit for the aldehydes (3.25, from −0.56 for trans-2-methyl-2-pentenal, D9, to 2.69 for 2-octynal, C1). These data show that the highest reactivity observed for the largest subset of 25 esters is smaller by ca. one order of magnitude than the highest observed ketone reactivity and still 0.6 log units below the highest aldehyde reactivity. The ciliate toxicity quantified as log EC50 [mol/L] varies by 3.29 units (from −1.70 for the ketone 3-methyl-2-cyclopentene-1-one as least toxic compound, B6, to −4.99 for the ketone 1-octene-3-one as most toxic electrophile, A3). The corresponding aldehyde and ester ranges are 1.88 (from −2.90 for trans-2-methyl-2-butenal, D8, to −4.78 for 2-octynal, C1) and 2.96 (from −1.83 for methyl methacrylate, G5, to −4.79 for methyl propiolate, E1). In this case, the maximum ciliate toxicity (minimum EC50) observed is similar for all three subsets, whereas the smallest toxicity is still ca. one order of magnitude larger for the aldehydes than for the ketones and esters. As compared to log EC50, the observed range in the toxicity enhancement Te is almost 10-fold larger covering 4.12 log units driven solely by the ester subset (from −0.40 for ethyl methacrylate, G6, to 3.72 for methyl propiolate, E1). Here, the 15 ketones show a ca. 10-fold smaller log Te variation of 3.15, whereas for the 18 aldehydes the log Te range is still significantly smaller with only 1.61 units. Reactive Toxicity vs Hydrophobicity. Figure 1 (left) shows the data distributions of log EC50 vs log Kow for the α,βunsaturated ketones (top), aldehydes (middle), and esters (bottom) together with the narcosis regression line according to eq 2. Despite some variation in the data patterns observed, the overall trend is as follows: Te as (normally downward) deviation from baseline narcosis increases with decreasing hydrophobicity, resulting in the largest increase in toxicity through chemical reactivity at the low end of log Kow. Correspondingly, the EC50 values approach the narcosis level with increasing hydrophobicity of the toxicant, and at sufficiently high Kow chemical reactivity will no longer play a role in the overall toxicological potency. The observed trend thus demonstrates that the reactivity contribution to toxicity depends also on the hydrophobicity of the compound and in fact decreases with increasing hydrophobicity. The latter can be seen more explicitly when comparing methyl acrylate (E8) with 3-nonene-2-one (B2). Here, very similar reactivities (log kGSH: 1.06 vs 1.03) are accompanied by different log Kow values (0.73 vs 2.79) translating into different toxicity enhancements (1.95 vs 0.65), the latter of which are traditionally interpreted as being driven solely by chemical reactivity.20 Thus, the E8 vs B2

log Te = −0.536( ±0.052)· log Kow + 0.668(± 0.040)· log k GSH + 1.56( ±0.10) n = 57; r 2 = 0.88; rms = 0.31; qcv2 = 0.87; rmscv = 0.34; F2,54 = 206

(4)

The rationale underlying eq 4 is the observation that Te is not simply proportional to reactivity but depends also on the hydrophobicity of the toxicant (see Figure 1 and the E8 vs B2 comparison as discussed above). The regression coefficients confirm that the toxicity enhancement increases with decreasing hydrophobicity (−0.536) and with increasing reactivity (0.668). As can be seen from Table S4, the aldehyde subset yields significantly different regression coefficients (−0.303, 0.423), indicating a correspondingly smaller Te sensitivity to variations E


Article


Scheme 3. Rate-Determining Processes Governing Hydrophobic and Reactive Molecular Initiating Events (MIEs) That Result in Hydrophobic and Reactive Toxicity, Respectivelya

a

Whereas uptake of waterborne compounds into the cellular membrane (A) and the hydrophobic MIE as respective membrane impairment (F) are driven by hydrophobicity (octanol/water partition coefficient, Kow), transfer from the membrane into the aqueous-phase cytosol is inversely related to log Kow (B). Covalent attack at nucleophilic protein sites in water-rich (C) and water-poor (E) regions represent reactive MIEs (with associated reactivities kGSH and kNH2, respectively), possibly involving previous pro-electrophile bioactivation to electrophilic metabolites (here: epoxidation through the monooxygenase CYP450). Hydrophobicity-facilitated transfer from the cytosol into the intracellular nucleus (D) provides a route to electrophilic DNA damage (possibly triggered by reactivity toward guanine-N7 in terms of k=N− besides two membrane passages), and epoxide hydrolase (EH) may detoxify respective electrophiles.

of log Kow and log kGSH. At the same time, similar intercepts for the separate aldehyde, ester, and ketone subsets suggest a comparable intrinsic Te capability across these three Michaelacceptor classes. A possible though speculative explanation could be that among these three compound classes, the reactivity for Schiff base formation is most pronounced for the aldehydes, thus offering an alternative mechanism of electrophilic attack at endogenous amine functionalities (Scheme 2). Since it has been shown that Michael-type aldehydes undergo imine formation with NH2 groups of proteins in particular under water-poor conditions,40,41 we hypothesize that the aldehyde reactivity for the imine reaction is sufficient to contribute to their reactive toxicity, and that its trend within the aldehyde subset is similar to the GSH reactivity (considering the good aldehyde regression statistics obtained without taking this additional pathway into account; see Table S4). From this perspective, the kGSH values would capture a systematically smaller part of the toxicity-relevant reactivity for aldehydes than for ketones and esters. Hydrophobic vs Reactive MIEs. In aquatic toxicology, hydrophobicity is usually understood to facilitate the following three processes: Uptake of waterborne xenobiotics in the organism, partitioning within the organism to lipid components such as membranes of nerves and other cells, and nonspecific

membrane irritation resulting in baseline narcosis (e.g., through impairment of membrane-bound proteins). These well-known considerations have led to the general perception that aquatic toxicity should increase with increasing hydrophobicity of the contaminant. In contrast to narcotic agents, organic electrophiles offer a second mode of toxicological action: Besides accumulating in membrane compartments proportional to their hydrophobicity, they may undergo covalent reactions with nucleophilic sites of endogenous molecules,15,20 which from this perspective would take place independently from the hydrophobic route of toxicity. Introduction of the concept of excess toxicity20 (or, in our terminology, toxicity enhancement)16,17,38 provided an elegant way to pragmatically discriminate the reactive component of toxicity from hydrophobic membrane perturbation. However, Te may also reflect noncovalent MIEs such as for oxidative uncoupling with lethal doses below the narcosis level by factors up to 10−10042 and can also be expected for (again noncovalent) receptor-mediated pathways. The early observation that for some electrophiles their toxicity enhancement was in fact inversely related to log Kow suggested that the toxicity-relevant chemical reaction would take place in the aqueous phase of the organism, which is increasingly less available for increasingly more hydrophobic compounds because of their correspondingly increasing F


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considered to be positive (a1 ≥ 0). The meaning of a2 is context-dependent and covers all subsequent phase transfers until the MIE location is reached. For aqueous-phase nucleophilic sites (steps B and C, with cysteine thiol being a typical example), a2 is negative and may be positive for MIEs at water-poor sites. Examples of the latter would be a reactive MIE in a water-poor protein pocket (E) or DNA damage at respective interior sites (D). Regarding Te, its normalization to baseline narcosis essentially eliminates a1 and thus yields an empirical calibration for a2 (membrane-cytosol phase transfer modeled through log Kow) with a current regression value of −0.54 for the combined Michael acceptors (eq 4). Finally, regression coefficient b scales the reactivity contribution to Te as toxicodynamic step C separated from the phase-transfer steps A and B. Equations 5a and 5b thus provide a quantitative framework for specifying the two K ow components referring to bioavailability (a1) and MIE specifics (a2), both of which can be empirically quantified through regression calibration of log Te and log EC50 as shown below. Accordingly, Te does not represent solely the toxicity-relevant reactivity of the compound but incorporates also the net impact of toxicokinetics on the reactive MIE triggering a respective adverse outcome. As such, Te represents the overall efficiency of the non-narcotic MIE in a given biological context, encoding both the availability of the target site for the electrophile, and the extent of the electrophile−nucleophile reaction at that site. For genotoxic agents such as epoxides,43 uptake in the cellular cytosol (steps A and B) is followed by passage through the intracellular nuclear membrane to enter the nucleus and eventually attack a nucleophilic functionality of DNA bases or the DNA backbone (D in Scheme 3). Interestingly, the epoxide gene-toxicological potency did not correlate with Te in 24 h and 48 h growth inhibition bioassays employing bacteria and ciliates, although the exposure times would appear sufficient for translating DNA damage into growth impairment.16,38 The latter indicates that population growth of unicellular organisms, although integrating over a multitude of physiological functions, may be too insensitive for responding to mutagenicity in a sufficiently specific manner. By contrast, a kinetic chemoassay approach using 4-(4-nitrobenzyl)pyridine (NBP) as nucleophile provided an even quantitative relationship with the mutagenic potency of epoxides.43 Besides the reactive MIE, organic electrophiles may also impair cellular membranes proportional to their hydrophobicity, qualifying their overall toxicological potency as a composite measure of both hydrophobic membrane irritation and reactive toxicity. In Scheme 3, F illustrates the membrane as respective noncovalent target site where the hydrophobic MIE takes place. The concept of chemoavailability27 as balance between partitioning into nonaqueous compartments and undergoing aqueous-phase chemical reactions (eq 3) enables one to discriminate between the three routes of predominantly hydrophobic, both hydrophobic and reactive, and reactivitysaturated toxicity, and as such may support the predictive MIE assessment (see also below). Log EC50 Calibration. Equations 5a and 5b are not confined to Te but hold also for other toxicological endpoints that involve a hydrophobicity-driven uptake into the organism and a reactive MIE. Depending on the endpoint metrics chosen, the signs of a1, a2, and b (and the intercept c) may be as for Te (that increases with increasing potency for reactive MIEs) or just reversed. An example of the latter is the EC50 as

partitioning into the lipid phase.22,23 In Scheme 3, this reasoning is elaborated in more detail, showing the two major steps toward the intracellular electrophilic attack at a waterborne nucleophilic site together with their individual dependencies on log Kow. Conceptual Model for the Kow Impact on Te. Upon uptake from the external aqueous phase into the aquatic organism, the compound enters a cellular membrane, which is increasingly facilitated with increasing hydrophobicity (step A in Scheme 3). Release from the membrane into the cytosol implies a membrane−water transfer of the compound and is thus inversely related to log Kow (step B). In other words, increasing hydrophobicity makes the compound less ready for leaving the membrane phase and entering the aqueous-phase cytosol (or going back to the external water phase or the aqueous-phase intercellular compartment). Subsequent passive diffusion enables the electrophile to reach a nucleophilic target site exposed to the cytosol. The following electrophile− nucleophile reaction (step C) is governed by the reactivity of both the toxicant and the endogenous reactant, and the resultant chemical modification of the latter, impairing its physiological function, is the molecular initiating event (MIE) of the downstream adverse outcome. For an organic electrophile, the above-outlined step A is required for both hydrophobic membrane impairment (step F) and to enter step B for the reactive toxicity route. This latter route, however, becomes less available for more hydrophobic compounds, translating into a negative correlation between log Kow and step B that triggers the reactive MIE. Step B thus causes hydrophobicity to affect also the potency of reactive toxicants (as long as the log Kow dependencies of steps A and B do not cancel perfectly), resulting in the above-mentioned Te dependence on both hydrophobicity and reactivity. If, however, the intracellular target site of covalent attack would be in a lipid-phase region, a further uptake step from the cytosol to the relevant nonaqueous phase would be required and would be increasingly efficient with increasing hydrophobicity. It thus depends on the local environment of the covalent target site whether its net availability for the reactive toxicant increases (if in a nonaqueous phase such as the interior of a protein; E in Scheme 3) or decreases (if in the water phase, C) with hydrophobicity. General log Te Calibration. By definition, Te reflects the toxicity increase through reactive or (possibly noncovalent) specific contributions. The original understanding20 that log Te would be independent from log Kow was subsequently challenged as discussed above.22,23 Accordingly, Te depends explicitly on Kow (in addition to its implicit Kow dependence through narcosis-level toxicity), and a log Te regression model should thus consider both reactivity and hydrophobicity. Respective empirical evidence has been given earlier22,23 as well as through eq 4 and the associated discussion (see above). When combining in Scheme 3 all major steps A, B, and C (with D and E providing alternatives to C) required for a reactive MIE to take place, the resultant general model equation for log Te reads log Te = a1log Kow + a 2 log Kow + b log k GSH + c

(5a)

log Te = (a1 + a 2)log Kow + b log k GSH + c

(5b)

In eqs 5a and 5b, a1 refers to step A. It calibrates the bioavailability component of log Kow (uptake from the external water into the organism) and for organic compounds can be G


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example now appears to be the also presently studied toxicity enhancement Te,16,17,22,23,27 as long as the context-specific chemoavailability allows for a significant reactivity contribution to toxicity.27 Fourth, a2 > 0 applies for nucleophilic target sites in nonaqueous phases such as respective functional groups of lipids or in the interior of proteins (E in Scheme 3) or the DNA (D in Scheme 3), and results in an overall toxicity-enhancing role of hydrophobicity. This may hold for skin sensitizing Schiff-base formers that prefer water-poor (hydrophobic) regions for reacting with amino groups of proteins.29−31 Because the same trend is also obtained with the abovediscussed second case, a2 < 0 and |a1| > |a2|, the empirical finding (a1 + a2) > 0 alone does not discriminate between these two scenarios. By contrast, (a1 + a2) < 0 corresponding to the abovediscussed third case can be taken as empirical confirmation that the covalent attack is located in the aqueous phase of the organism. In other words, eqs 5a and 5b enable one to sense reactive toxicity regarding the following two situations: For (a1 + a2) < 0, the reactive MIE is an aqueous-phase process, and for (a1 + a2) > 0 it is either taking place at water-poor sites, or the toxicokinetics qualifying for the endpoint of interest is bioavailability-driven (making the hydrophobic uptake the rate-determining toxicokinetic process). Chemoavailability. Complementing bioavailability, chemoavailability provides a quantitative measure of the accessibility of waterborne nucleophilic sites for organic electrophiles.27 Its current parametrization as the DkK (eq 3) value reflects the opposing roles of reactivity (increasing log kGSH increases DkK) and hydrophobicity (increasing log Kow decreases DkK). Equation 3 implies that a lower aqueous-phase concentration and thus a reduced opportunity for attacking waterborne nucleophilic site can be compensated, in practice up to a certain threshold, by an accordingly high reactivity. Chemoavailability thus accounts for limitations of using reactivity alone33 or hydrophobicity alone22,23 to discriminate narcosis-level from excess toxicity.27 In particular, chemoavailability is a means to predictively allocate organic toxicants to one of the following three classes: prevalence of narcosis, parallel relevance of both narcotic and reactive MIEs, and reactive MIE saturation associated with an approximate toxicity limit (see below). As such, it contrasts with empirical regression analyses considering hydrophobicity and reactivity separately and in combination (see the example with methyl tiglate (G7) below). Calculation of DkK for all 58 Michael acceptors and detailed inspection of the upper and lower DkK range as compared to the impact of log Kow and log kGSH on ciliate log EC50 (Table 1) suggest the following criteria for a screening-level D kK discrimination between reactive and hydrophobic MIEs triggering predominantly alone or in parallel downstream toxicity:

measure of the overall ciliate toxicity of organic electrophiles. For the 57 α,β-unsaturated carbonyls excluding methyl tiglate (see above), the respective regression yields log EC50 = −0.334( ±0.052) ·log Kow − 0.668(± 0.040)· log k GSH − 2.55( ±0.10) n = 57; r 2 = 0.85; rms = 0.31; qcv2 = 0.83; rmscv = 0.34; F2,54 = 154

(6)

Comparison with eq 4 shows a very similar dependence on reactivity (keeping in mind that −log EC50 = log 1/EC50 increases with increasing toxicity, implying a change of the regression coefficient signs). More importantly, however, may be that the difference in log Kow coefficients provides an estimate for a1 of eqs 5a and 5b, and thus offers a way to unravel the impact of membrane bioavailability (watermembrane phase transfer, A in Scheme 3) on toxicity. Since eq 6 yields an empirical calibration of (a1 + a2) that, after sign adaption to Te (see above), amounts to 0.33, subtraction of the respective log Te regression coefficient of a2 = −0.54 (eq 4) gives a1 = (a1 + a 2) − a 2 = 0.33 − ( −0.54) = 0.87

as the log Kow scaling factor. The latter thus models the uptake of the electrophiles from aqueous solution into the biological membrane and at the same time also their potency for hydrophobic membrane irritation (F in Scheme 3). The resultant a1 value of 0.87 is in fact consistent with the baseline narcosis scaling of log Kow as specified in eq 2. Overall, the presently proposed conceptual framework provides a mechanistic rationale for the Kow impact on Te and the associated link between the log Kow coefficients of regression models for aquatic toxicity in terms of log EC50 and log Te, respectively. Four Toxicokinetic Scenarios for Te. Depending on the sign of a2 and the absolute values of a1 (that is considered to be ≥0, see above) and a2 in eqs 5a and 5b, the following four scenarios may be of interest: First, in the case of a2 < 0 and |a1| ≈ |a2| the net Kow contribution is negligible because of its essentially opposite impact on uptake into and elimination from the membrane (steps A and B in Scheme 3). An example is the ciliate toxicity of some Michael acceptors in terms of the 48 h growth inhibition EC50 values, the variation of which could be explained through GSH reactivity alone.24,25 Skin sensitization provides further cases where this setting may hold, with the respective potency of SNAr and Michael-acceptor electrophiles being literature examples.28 Second, a2 < 0 and |a1| > |a2| implies an overall enhancing log Kow contribution to the toxicological potency at the endpoint of interest. Here, bioavailability (step A) has a larger toxicokinetic impact on the subsequent MIE or MIEs than the process of elimination from the cellular membrane into the aqueous-phase cytosol (step B). Literature examples include the 14-day fish toxicity of epoxides23 and the 48 h ciliate growth inhibition,26,27 to which also the current regression model of eq 6 refers. In all of these cases, toxicity increases with both increasing hydrophobicity and increasing reactivity. Third, for a2 < 0 and |a1| < |a2|, the endpoint of interest is more sensitive to the cytosol concentration of the compound than to the efficiency of its uptake from outside. In this case, the entrance into the cytosol is the rate-determining process regarding toxicokinetics. In aquatic toxicology, the prominent

DkK > 2.5:

reactive MIE saturation (log Te > 2.8)

−3.0 ≤ DkK ≤ 2.5: reactive MIE (−0.3 ≤ log Te ≤ 3.7) + hydrophobic MIE DkK < −3.0:

prevalence of hydrophobic MIE

(log Te < 0.5)

(7)

The respective log Te ranges overlap partly and in contrast to DkK are not recommended as an MIE profiler. H


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lack of an aqueous-phase reactive MIE is tentatively set as follows:

For the six compounds A1, A2, C3, E1, E2, and E5 with the largest chemoavailability of the present data set (DkK > 1.4), the EC50 variation is only 1.1 log units (from −4.79 to −3.69, Table 1) as opposed to Kow and kGSH differences by 1.64 and 1.8 log units, disqualifying this subset for separate regression analyses (because r2 depends also on the variation range of the target property).44 The relatively small EC50 variation appears to indicate that with a further increase in DkK, a toxicity limit may be reached, which we hypothesize to result from a saturation of the reactive MIE (corresponding to the diffusion-control limit of chemical reaction kinetics). Lacking respective experimental examples, we tentatively estimate this saturation toxicity to be around a log EC50 [mol/L] value of −5.50 ± 0.5, which may serve as a hypothesis to be challenged in future studies. Compounds F9, G6, and G7 with DkK < −3.0 show ciliate toxicities in the baseline narcosis range (eq 2). At the same time, for D2 (trans-2-decenal) with the fourth-to-smallest DkK of −2.55, the toxicity enhancement over narcosis amounts to 0.91 log units. Moreover, it is more toxic by 1.1 log units than F9 (log EC50: −4.98 vs −3.90, Table 1) that appears to be driven more by its significantly larger reactivity (log kGSH: 1.00 vs −0.11) than by the minor increase in hydrophobicity (log Kow: 3.55 vs 3.10). Thus, DkK at −2.6 qualifies for a still relevant impact of reactivity on toxicity, reducing to a negligible contribution when DkK has reached ca. −3.0 (see F9, G6 and G7 above). Coming back to methyl tiglate (G7) as the only outlier of the log Te and log EC50 regression models (eq 4 and Figure 2; eq 6), its overall smallest DkK value of −3.84 suggests that below a certain level of chemoavailability, the reactive MIE is in fact no longer feasible. Since F9 and G6 with DkK values of −3.01 and −3.21 still fit well to eqs 5a and 5b, the threshold indicating the

DkK < −3.5:

hydrophobic MIE

(log Te < 0.4)

(8)

Here, the indicated log Te range may serve as a rough expectation, taking also data uncertainty into account. Equation 8 implies that Michael acceptors may act as pure narcotics, provided their chemoavailability is accordingly small, although their electrophilic reactivity may still be measurable. Indeed, methyl tiglate shows the overall smallest second-order rate constant of reaction with GSH (kGSH) determined so far in our laboratory.25,26,32 Kinetic Basis of Chemoavailability. According to reaction kinetics, the velocity or turnover rate of a chemical reaction depends on both the rate constant (intrinsic reactivity) and the reactant concentrations. For an aqueous-phase target site (E in Scheme 3), K ow triggers the electrophile concentration, cw = co/Kow assuming that its concentration in the membrane, cm ≈ co (octanol as membrane surrogate), is essentially constant or at least sufficiently above the depletion limit. Then log[k GSH·(1/Kow )] = log k GSH − log Kow = DkK

(9)

shows a qualitative correspondence to pseudo-first-order kinetics in logarithmic form: log[( − dc /dt )] = log[k·c] = log k + log c

(10)

(with c = reactant concentration and k = rate constant of reaction). Equation 10 holds as long as a possible decrease in free nucleophile concentration does not become ratedetermining. Equation 9 and its formal correspondence to eq 10 provide a mechanistic basis for the DkK definition that had been originally introduced as an empirical approach.27 Future investigations may not only extend the chemical domain beyond the presently studied Michael acceptors, but also consider nonthiol nucleophilic target sites with associated model nucleophiles suitable for chemoassay approaches. Overall, the evaluation of the chemoavailability parameter DkK (eq 3) enables a screening-level assessment of whether the toxicity exerted by the organic electrophile would be triggered predominantly by a reactive or hydrophobic (noncovalent) MIE or through significant contributions from both (eq 7). Possible applications of this knowledge include identifying a mechanistically fitting QSAR for predicting the toxicity of interest,27 and designing a targeted study program with a focus on analyzing presumably relevant mechanisms of action.

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CONCLUSIONS The aquatic toxicity of organic electrophiles results from a balance between their hydrophobicity and their chemical reactivity. Analysis in terms of the toxicity enhancement over baseline narcosis, Te , through a conceptual model in combination with the chemoavailability profile of the compounds provides a mechanistic explanation of the early observation that Te decreases with increasing hydrophobicity. Whereas the latter facilitates the compound uptake from external water, it counteracts the transfer from the cellular membrane into the aqueous-phase cytosol. Accordingly, Te is a composite measure, integrating over the phase transfers and the toxicodynamic impairment of endogenous molecules through covalent attacks at water-rich nucleophilic sites. Model application to a data set of 58 Michael acceptors with

Figure 2. Predicted vs experimental ciliate toxicity enhancement in terms of log Te (eq 1) for Michael-acceptor α,β-unsaturated carbonyls, employing the subset-specific regression models (Table S4) based on log Kow and log kGSH, and calibrated for 15 ketones (blue squares; r2 = 0.96, rms = 0.19; log Kow slope −0.604, log kGSH slope 0.682, intercept 1.45), 18 aldehydes (red triangles; r2 = 0.93, rms = 0.12; −0.303, 0.423, and 1.45), and 24 esters (green dots; r2 = 0.95, rms = 0.26; −0.601, 0.842, and 1.63). The outlying prediction for methyl tiglate (G7, green dot close to the x axis, excluded from the regression model calibration) can be traced back to its very low chemoavailability (eq 3, Table 1) that triggers for a purely hydrophobic molecular initiating event (MIE; see text). I



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ABBREVIATIONS GSH, glutathione; kGSH, second-order rate constant of reaction with glutathione; EC50, effective concentration yielding 50% growth inhibition of the ciliates Tetrahymena pyriformis; Te, toxicity enhancement; Kow, octanol/water partition coefficient; DkK, chemoavailability parameter; MIE, molecular initiating event; AOP, adverse outcome pathway

experimental reactivity toward glutathione and toxicity toward the ciliates Tetrahymena pyriformis provides empirical support that the toxicity-relevant reactive molecular initiating event (MIE) takes place in the aqueous phase of the organism. As such, the approach may generally be useful to sense whether for a given organic electrophile, the reactive MIE target site is in a water-rich or water-poor region (such as in the interior of proteins, lipids or the DNA). In particular, chemoavailability parametrized as DkK value yields a rationale for the opportunity to compensate a hydrophobicity-driven decrease in target-site dose by an increased chemical reactivity, and enables a predictive discrimination between the prevalence of hydrophobic vs both hydrophobic and reactive MIEs vs reactive MIE saturation (suggesting a reactive toxicity limit). Moreover, chemoavailability offers a mechanistic criterion to select the most appropriate QSAR employing hydrophobicity alone or in combination with reactivity, overcoming best-fit QSAR strategies as well as Te-based categorizations that both (and in contrast to DkK) require experimental toxicity. Overall, the approach may support the nonanimal assessment of organic electrophiles, provide mechanistic criteria for identifying possibly prevalent MIEs, and thus also contribute to the AOP assessment of toxicological effects.

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.chemrestox.5b00398. Scheme showing the chemical structures of the 58 α,βunsaturated carbonyls used for this work, experimental details regarding the chemoassay protocol and the bioassay EC50 correction for exposure loss through sorption and volatilization, and a table with the log Te regression models for the subsets of ketones, aldehydes, and esters (PDF)

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AUTHOR INFORMATION

Corresponding Author

*Tel: +49-341-235-1262. Fax: +49-341-235-45-1262. E-mail: [email protected]. Present Address §

A.L.: PolyComply Hoechst GmbH, Industriepark Höchst, Building F 821, 65926 Frankfurt am Main, Germany. Funding

Financial support through the EU-funded project OSIRIS (Optimized Strategies for Risk Assessment of Industrial Chemicals through Integration of Non-Test and Test Information, contract no. GOCE-CT-2007-037017) and the BMBF-funded project ProHapTox (Development of a Reactivity-Based Non-Animal Testing Strategy for Identifying the Skin Sensitization Potential of Electrophilic and ProElectrophilic Chemicals within the Framework of REACH, FKZ 031A422A and 031A422B) is gratefully acknowledged. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank our undergraduate students for their support with some experiments. J


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