Steven T. J.
Droge
*a,
Geoff
Hodges
b,
Mark
Bonnell
c,
Steve
Gutsell
b,
Jayne
Roberts
b,
Alexandre
Teixeira
b and
Elin L.
Barrett
b
aDepartment of Freshwater and Marine Ecology (FAME), Institute for Biodiversity and Ecosystem Dynamics (IBED), Universiteit van Amsterdam (UvA), Science Park 904, 1098XH Amsterdam, The Netherlands. E-mail: steven.droge@wur.nl; Tel: +31 317484410
bSafety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedfordshire, UK
cEnvironment and Climate Change Canada, Ecological Assessment Division, Science and Risk Assessment Directorate, Gatineau, Quebec, Canada
First published on 13th February 2023
The risk assessment of thousands of chemicals used in our society benefits from adequate grouping of chemicals based on the mode and mechanism of toxic action (MoA). We measure the phospholipid membrane–water distribution ratio (DMLW) using a chromatographic assay (IAM-HPLC) for 121 neutral and ionized organic chemicals and screen other methods to derive DMLW. We use IAM-HPLC based DMLW as a chemical property to distinguish between baseline narcosis and specific MoA, for reported acute toxicity endpoints on two separate sets of chemicals. The first set comprised 94 chemicals of US EPA's acute fish toxicity database: 47 categorized as narcosis MoA, 27 with specific MoA, and 20 predominantly ionic chemicals with mostly unknown MoA. The narcosis MoA chemicals clustered around the median narcosis critical membrane burden (CMBnarc) of 140 mmol kg−1 lipid, with a lower limit of 14 mmol kg−1 lipid, including all chemicals labelled Narcosis_I and Narcosis_II. This maximum ‘toxic ratio’ (TR) between CMBnarc and the lower limit narcosis endpoint is thus 10. For 23/28 specific MoA chemicals a TR >10 was derived, indicative of a specific adverse effect pathway related to acute toxicity. For 10/12 cations categorized as “unsure amines”, the TR <10 suggests that these affect fish via narcosis MoA. The second set comprised 29 herbicides, including 17 dissociated acids, and evaluated the TR for acute toxic effect concentrations to likely sensitive aquatic plant species (green algae and macrophytes Lemna and Myriophyllum), and non-target animal species (invertebrates and fish). For 21/29 herbicides, a TR >10 indicated a specific toxic mode of action other than narcosis for at least one of these aquatic primary producers. Fish and invertebrate TRs were mostly <10, particularly for neutral herbicides, but for acidic herbicides a TR >10 indicated specific adverse effects in non-target animals. The established critical membrane approach to derive the TR provides for useful contribution to the weight of evidence to bin a chemical as having a narcosis MoA or less likely to have acute toxicity caused by a more specific adverse effect pathway. After proper calibration, the chromatographic assay provides consistent and efficient experimental input for both neutral and ionizable chemicals to this approach.
Environmental significanceTo prioritize more detailed risk assessment for certain chemicals of concern, it is important for risk assessors to identify chemicals that induce toxicity by an adverse effect pathway other than narcosis. Our study shows that the membrane lipid–water distribution ratio (DMLW) is a key descriptor for both neutral and ionizable organic chemicals. By measuring new DMLW values for 121 chemicals we derive the critical membrane burden (CMB) threshold for fish acute toxicity below which a specific mode of action other than narcosis drives toxicity and use this CMB approach on the response of different (non-)target species to a variety of herbicides. |
The consideration of modes and mechanisms of action can also be very useful as one descriptor of hazards when prioritizing chemicals for further regulatory action. In Canada, for example, identifying organic chemicals with specific and non-specific modes and mechanisms of action was incorporated into version 1.0 of the Ecological Risk Classification (ERC1) approach2,4 used in 2016 by Environment and Climate Change Canada (ECCC) to reset priorities for 640 organic chemicals for phase three of the Chemicals Management Plan (CMP). ERC1 introduced the concept of data consensus weighting between critical body residue (CBR) derived toxicity ratios (TRs) and quantitative structure–activity relationship (QSAR) classification of MoA as one hazard descriptor for priority setting in ERC1. Specific modes of action were responsible for 40% of the high hazard classifications (i.e., not final risk classification) identified in 2016 by ECCC using ERC1.
Building on ERC1, version 2.0 of the ERC5 (ERC2) was developed in 2018 and is currently in use by ECCC for identifying chemicals of concern among 12200 organic chemicals for post 2020 work planning reasons. ERC2 is a weight of evidence logical model relying on data consensus to determine the risk classification, risk confidence and risk scale of organic chemicals for further regulatory consideration. ERC2 takes the MechoA concept further than ERC1 by expanding the number and type of tissue residue and QSAR approaches used for identifying specific and non-specific modes of action. ERC2 also integrates both molecular initiating event information (MIE) and modes and mechanisms of action using the adverse outcome pathway (AOP) concept.6
The degree of MoA consensus in ERC2 was also evaluated by comparing MoA classifications from the five MoA QSAR and five methods used to calculate tissue residue TRs associated with median lethality in fish2 in ERC2. The confidence score associated with the MoA classification in ERC2 is directly related to the degree of consensus between all methods above. The results, based on 929 organic chemicals with available averaged acute fish median lethality data gathered using the OECD QSAR Toolbox,7,8 revealed that 100% consensus between all ten methods was greater for non-specific (narcosis) chemicals (∼53%) than those with specific modes of action (∼29%). There was no large distinction in MoA classification among the five tissue residue methods and the authors suggest further curation of aquatic toxicity and water solubility data may improve the correlation among all methods.
A consensus MoA approach has also been incorporated into automated on-line tools for determining predicted no-effect concentrations (PNECs) for risk assessment using the Ecological Threshold of Toxicological Concern (Eco-TTC) and curated ENVIROTOX database tools.9–11 MechoA considerations have also been incorporated into the derivation of assessment (safety) factors for PNEC derivation used in risk assessments of new and existing substances by ECCC. The evaluation of modes and mechanisms of action is reviewed and documented to ensure that the selection of critical toxicity values used to derive a chronic PNEC are related to the mode and mechanisms of action.
A number of schemes exist to support the identification of the MoA of chemicals. Typically schemes such as those developed by Verhaar and subsequent updates,12–14 the EPA Mode of Action and Toxicity (MOAtox) database15 and the Acute Aquatic Toxicity MOA by OASIS (AAT OASIS) scheme,16 also incorporated into the US EPA inhouse expert system ASTER (ASsessment Tools for the Evaluation of Risk17) and the US EPA Toxicity Estimation Software Tool (TEST18), are easily accessible for such an application. The Verhaar and OASIS schemes also have been incorporated into the OECD QSAR toolbox as mechanistic profilers. More recently there has been a growing recognition that classifying chemicals using the mechanism of action can add more confidence in toxicity prediction.19 The MechoA scheme as one such example has recently been developed and available freely online is the KREATiS MechoA scheme20 to predict the toxicity mechanism based on the chemical structure.21,22 The recent scheme of Sapounidou et al.23,24 is another such example which follows an analogous approach to the MechoA scheme. However, it remains that such schemes have potential for discrepancies in assigned MoA and also have limitations in being able to classify the full chemical space.9 With the benefits of having a reliable understanding of MoA for supporting chemical prioritization and risk assessment in addition to data gap filling, there is, therefore, a continued need to develop consensus on MoA.10 The development of new and complementary approaches to support MoA is thus needed.
In the current study, we investigate the expansion of an existing approach considering the application of the critical body residue (CBR) and critical membrane burden (CMB) to aid the determination of MoA. Specifically, we consider the role of the membrane lipid–water distribution ratio (DMLW, as used for ionizable species, or KMLW for neutral species) to distinguish between chemicals that operate in the range of baseline toxicants from chemicals which are likely to induce toxicity via another MoA at a CMB lower than typical for narcosis. Since the cell membrane is not the target site for most specific adverse effect pathways (e.g., covalent interactions with DNA), the DMLW-approach is not intended to classify a chemical to a certain MoA. Our approach can be used to identify chemicals for which further information on MoA would be considered of high relevance for risk assessment, because they do not induce acute toxicity by baseline narcosis. The key aim is thus to define the lower limit CMB of polar and non-polar narcotic chemicals. DMLW can be derived using a range of experimental methods (e.g. unilamellar liposome vesicles25 or solid supported lipid membranes26,27), or in silico approaches, such as COSMOmic28,29 or molecular dynamics.30–32 Here, we consider the use of a chromatographic column retention approach to derive DMLW values for MoA determination. The overall aim was to generate two strategic DMLW data sets that would allow for MoA assessment with high quality toxicity data, extending previous efforts in multiple aspects in terms of both chemical space, DMLW data quality, and MoA domains. The first data set focuses on chemicals listed in the acute fish toxicity data from EPA's Fathead minnow (FHM) database. The second set involves herbicides for which toxicity data for several types of aquatic species are compared. Before discussing the experimental part and discuss the data interpretation, below we briefly introduce in Section (ii) the chromatographic approach and comparable studies that precede the current work, (iii) the link between DMLW and toxicity, (iv) alternative ways to derive DMLW, and (v) which toxicity data sets were used to evaluate the CMB-MoA analysis. The method section then starts with explaining which chemicals from those toxicity databases were selected to obtain the chromatographic retention data for.
IAM-HPLC provides a cost-effective, high-throughput, consistent approach to experimentally derive indicative DMLW values directly from these kIAM measurements. Using kIAM obtained in (or extrapolated to) fully aqueous eluent (i.e., k0IAM), multiplication with a pre-determined medium/phospholipid volume ratio of the IAM-column (φ = 18.9 (ref. 45)) gives the partition coefficient between the bulk aqueous eluent and IAM phospholipid monolayer (KIAM), which should be analogous to DMLW:
DMLW − KIAM = φ × k0IAM = 18.9 × k0IAM | (1) |
A review on liposomal partition coefficients showed a linear relationship between DMLW values and IAM-HPLC retention capacity factors (kIAM) for 24 neutral organic chemicals, as shown in Fig. 1 by black squares (kIAM already converted to KIAM partition coefficients by using eqn (1)).25 For most chemicals, the IAM-HPLC estimate was within a factor of ±3, but for some neutral chemicals with high H-bond donor capacities the KIAM overestimated DMLW by a factor of ∼10. Two studies, on anionic surfactants27 (including perfluorinated anions) and cationic surfactants26 (including quaternary ammonium chemicals), both determined KIAM values and DMLW (via solid supported lipid membranes), which extends the DMLW–KIAM comparison to also include ionic chemicals. The 19 cationic surfactants26 are shown in Fig. 1 as green triangles, and the 10 (fluorinated and non-fluorinated) anionic surfactants as red dots. Since the aim is to derive quantitative and precise DMLW values from kIAM for the CMB approach, and not work with relative chromatographic indices, we need to take into account that the silica particles used in the IAM-columns cause confounding electrostatic effects on the retention time of ionic chemicals. Only intrinsic IAM phospholipid-partition coefficients (KIAM,intr) are to be used for predominantly/fully ionized chemicals, which are corrected for this confounding attraction using empirically based or theoretically derived Boltzmann corrective factors.48 This Boltzmann correction is in more detail reviewed for dissociated acids and protonated bases in ESI Section S1.† While the neutral data in Fig. 1 were obtained from a wide variety of studies and different experimental approaches to derive DMLW and KIAM, the scatter between the series of KIAM and DMLW values obtained for ionic surfactants was obtained in a single research institute. Consistent empirical differences for KIAM and DMLW for specific types of ionic moieties on the surfactant data indicated that it may be necessary to adjust the IAM-HPLC results by several empirical increments (δIAM-SSLM) to improve the KIAM–DMLW consistency, as shown in the plot on the right in Fig. 1: −0.5 log units for all anionic chemicals, +0.8 log units for primary (1°) amine cations, +0.5 log units for secondary (2°) amine cations, −0.03 log units for tertiary (3°) amine cations, and −0.1 log units for quaternary ammonium cations (see also ESI-Table S3†). The reasons for these ionic-type specific increments to align the phospholipid-coating based KIAM data and phospholipid bilayers based DMLW data remain to be elucidated.
The primary goal of the current study was to use a measured DMLW value to predict the baseline toxicity to aquatic organisms for a large and diverse set of organic chemicals, and identify whether a chemical is not a baseline toxicant and very likely induces acute toxicity by a reactive or more specific MoA. Since IAM-HPLC allows for high-throughput and experimental consistency, the KIAM values obtained by this method are considered to be the best DMLW proxies to do so. Several studies have already demonstrated that kIAM values (i.e., IAM-HPLC retention capacity factors) strongly correlate with the acute effect concentrations of non-specific toxicants to aquatic organisms (LC50,Narcosis),49–54 for example tadpoles (Rana temporaria, from the data set on narcotics by Overton and Meyer) and the fathead minnow fish (Pimephales promelas). The set-up of these valuable kIAM-LC50,Narcosis comparative studies, however, are limited in several aspects, and we aimed to extend these aspects in this study. First, while the reported studies used kIAM retention capacity factors that were based on measurements with eluent that contained 40% acetonitrile, the current study aims to derive new IAM-retention data for a large number of strategic chemicals (regarding available toxicity data, ionization, and data scarcity) obtained at, or adequately extrapolated to, fully aqueous eluent. As a result, our IAM-HPLC data set allows for simple conversion of k0IAM (0% solvent, 100% water) to KIAM values that are direct proxies for DMLW, rather than using kIAM as a relative scaling index. Second, whereas the reported studies included only neutral chemicals, the current study aimed to include largely ionized organic bases (or permanently charged cations) and strong organic acids in toxicity evaluations. The KIAM–DMLW data set on ionic surfactants allows for a validation set in deriving the intrinsic KIAM values to relate to DMLW values for ionic species (Fig. 1). Third, while the reported studies included only chemicals with narcosis MoA, we aim to include chemicals with proven specific strongly toxic MoA alongside those with only narcosis MoA. This should exemplify the extent to which IAM-based DMLW values accurately distinguish specific MoA and narcosis MoA chemicals.
![]() | (2) |
According to partition coefficients to different tissue phases, membranes being one specific component besides for example storage lipids, carbohydrates, (structural) proteins, and water,58–62 it was derived from a reviewed set of CBR data for narcosis chemicals that CMBnarc ranges between 80 and 250 mmol kg−1 membrane, with a geometric mean of 140 mmol kg−1.63
Using this narrow range of CMBnarc, dissolved concentrations that are acutely lethal to 50% of aquatic organisms due to narcosis (LC50,narc) can be back-calculated for any chemical using DMLW, as defined by the target lipid model by Di Toro et al. (2000):64
LC50,narc (in mmol L−1 water) = CMBnarc (∼140 mmol kg−1 membrane)/DMLW (in L water per kg membrane) | (3) |
Chemicals that are considered to distinctively exert acute toxicity by a MoA other than narcosis are expected to have a critical membrane burden significantly below 140 mmol kg−1, and hence have an acute (lethal) effect concentration well below the DMLW-calculated LC50,narc. It is important for risk assessors to identify this level of endpoint specificity, as near baseline cytotoxic levels many specific cellular pathways will also be affected, as a so-called “cytotoxic burst”.65,66 The primary aim of the current study was to derive a minimum CMBnarc for as large a set of chemicals as possible classified as narcotics, with the cell membrane as the target site of action for narcosis, and a measured membrane–water partition coefficient. Any organic chemical is likely to induce acute toxicity due to baseline narcosis above this range, whereas chemicals that act via a specific mechanism of action are expected to display a CMB below this minimum CMBnarc and thus indicative of a specific MoA driving the observed acute toxicity. Note that the chronic MoA to aquatic organisms is not taken into account in this CMB-MoA approach. For most baseline toxicants the acute to chronic ratio (ACR) is small (∼10×), and the chronic CMBnarc is thus not much lower than the acute CMBnarc. This suggests that below the threshold of basic membrane disturbance the exposed organism may withstand this toxic pressure for a prolonged period, with a certain loss of energy used for maintenance. However, a small fraction of chemicals identified as baseline toxicants were found to have an acute to chronic ratio of more than 30, indicating that upon chronic exposure these chemicals may act via a specific MoA.67 It is therefore not possible to rely on the current selection of chemicals classified by acute effect studies.
A prediction of the baseline toxic concentration (LC50,narc) of any organic chemical is relevant for risk assessment. First, it easily translates to an initial approach to set maximal allowable concentrations for chemicals for which no toxicity data are available. Second, it could be a check whether the adverse effect concentration reported for a certain chemical is due to a specific MoA, or whether the adverse effect occurred at a level where baseline toxicity is expected (apparently specific effects may have occurred only as part of the cytotoxic burst).68 Third, most environmental pollution occurs as complex chemical mixtures, and in most cases chemicals with a specific MoA are dissolved at concentrations well below the level that induces a specific effect. However, any chemical in a mixture contributes to the accumulation of chemicals in membranes, and each chemical therefore contributes to the narcosis CMB of the total mixture in a (molar) concentration-additive way.69–71
The CMB approach is generally evaluated using the octanol–water partition coefficient (KOW) as a proxy for DMLW, using toxicity data sets for algae, daphnids, and fish.57,64 Baseline toxicity has also been expressed as a function of chemical activity, using the maximum water solubility (Sw) in relation to the LC50 as a metric to assess the MoA.72 There are relevant uncertainties related to using both the KOW and Sw that could lead to false classification of chemicals having or not having a specific MoA.73 This involves uncertainties surrounding the KOW and Sw values, but also the relevance of these values in relation to the DMLW values driving the actual CMB, as discussed below. The approach of the current study aims to obtain DMLW experimentally, which would by-pass several uncertainties relating to KOW and Sw and thereby assess MoA specificity with a higher level of confidence.
Log![]() ![]() | (4) |
Although accurate KOW values can be obtained according to standardized protocols, there can be wide margins in reported values for many chemicals. And although large experimental KOW data sets have been used to create a variety of commonly used predictive algorithms, predicted KOW values further contribute to uncertainty in derived KMLW values according to eqn (4). Octanol also does not necessarily reflect the specific interactive properties of neutral chemicals with phospholipids in a cell membrane. Therefore, a phospholipid-water specific poly-parameter linear free energy relationship (ppLFER) has been constructed (eqn (5)).25 This ppLFER uses five chemical descriptors that should adequately cover the contribution of different solute/system interactions: molecular volume (Vx), hydrogen-bond acidity (A) and basicity (B), hexadecane-air partition coefficients (L), and a parameter to account for excess polarizability (S).
log![]() | (5) |
While Vx is calculated, the descriptors S, A, and B are recommended to be derived experimentally from consistent column retention studies, as collected in the UFZ LSER database (https://www.ufz.de/lserd), as to avoid stacking of estimation uncertainties for each descriptor. So instead of the more generic hydrophobicity descriptor KOW, more accurate values can be calculated via ppLFER if descriptors are available. This indeed requires the experimental ppLFER descriptors S, A, B and L to be available or to be newly derived, e.g. from multiple chromatographic retention data.
For ionizable organic chemicals (IOCs), however, neither KOW nor the ppLFER approach adequately takes into account the ionic interactions between charged sites in the phospholipid headgroup domain with charged groups in ionized organic chemicals. The octanol–water distribution ratio (logD) of the largely ionized form of a strong acid (pKa ≪ testing pH, e.g. <3 units) or strong base (pKa ≫ testing pH, e.g. >3 units) is mostly several orders of magnitude lower than the octanol–water partition coefficient (log
P) of the fully neutral acid or base, because the ionized form strongly prefers the readily polarizable aqueous phase.74,75 Moreover, the affinity of the ionic form for octanol strongly depends on the presence and concentration of counterions. However, the majority of cell membrane phospholipids contain a zwitterionic phosphatidylcholine headgroup. In anisotropically organized phospholipid layers of liposomes and cell membranes, the zwitterion moieties together form a partially hydrated headgroup ‘region’, which shields off the highly hydrophobic ‘region’ formed by the densely packed fatty acid tails from the bulk water phase. Due to strongly favorable ionic interactions with the zwitterionic headgroups, combined with partial embedding in the hydrophobic bilayer core, ionized forms of many organic bases and organic acids have a phospholipid membrane–water distribution ratio (DMLW,ion) only marginally lower than, or even equal to, the KMLW,N of their corresponding neutral forms.29,76–81
The affinity of ionic organic chemicals for phospholipid membranes can be estimated rather crudely for both predominantly charged acids and bases as a first approach.82
log![]() ![]() | (6) |
However, a single increment between the DMLW values of the ionic and neutral forms is overly simplistic, and can be further refined for different ion types. Based on rather small sample sizes, ion-type specific scaling factors (ΔMW) have been derived according to eqn 7
log![]() ![]() | (7) |
The ΔMW describes the average difference between logDMLW,ion and log
KMLW,N: −0.75 for phenolates, −2 for all other anionic chemicals, −0.3 for primary amines, −0.5 for secondary amines, and −1.25 for tertiary amines and other cationic chemicals.80,81,83–85
After defining the DMLW of both ionic and neutral forms, the pH-dependent fractions of neutral forms (fN) and ionic forms (1 − fN), the overall distribution ratio at a certain pH (DMLW(pH)) can be calculated according to eqn (8):
DMLW(pH) = fN × KMLW,N + (1 − fN) × DMLW,ion | (8) |
![]() | (9) |
While the ΔMW-approach takes the specific ion-type differences into account, the KMLW,N is often still based on KOW values and eqn (4). For certain chemicals, particularly for IOCs, it becomes relevant that octanol is a bulk solvent, while phospholipid membranes are anisotropically structured. The charged moiety will favorably position in the headgroup region while the most hydrophobic molecular portion will extend into the core, and this position may strongly differ for the neutral form of the same IOC. Computational methods such as the COSMOmic and COSMOconf modules of the commercial software package COSMOlogic (3ds Dassault systèmes/BIOVIA) and molecular dynamics simulations can be of use in spatially oriented prediction of DMLW. COSMOmic combines quantum chemistry and thermodynamics and uses the three-dimensional (3D) structure of both the solute and the hydrated phospholipid membrane. The internal membrane potential and surface charge density distributions of ionogenic chemicals as well as the phospholipid structure can also be considered.26,29,86,87 Molecular dynamics simulations consider the conformation of, and interactions between, all compounds in the membrane, water and solute, and can also be used in the study of the impact of conformation on DMLW.
Experimental measurements on phospholipophilicity may be preferred over descriptor calculated values for chemicals of environmental concern that require higher tier level risk assessment, particularly for ionizable chemicals. Many pharmaceuticals and illicit drugs are ionizable chemicals,88 as well as a considerable fraction of high production volume chemicals for which chemical fate and hazard assessment needs to be more detailed.89 Using the neutral form of an acid or base to calculate the phospholipophilicity of the anionic or cationic species, e.g. viaeqn (4) + (6), or (5) + (6), entails considerable uncertainty. Although various batch tests with pure artificial phospholipids are possible,79,90–92 no widely recognized testing protocols, such as OECD or ASTM, are available. Chromatographic methods, however, are already standardized in OECD guideline 117 to determine octanol–water distribution ratios93 and OECD 121 to screen for soil organic carbon sorption affinities. Retention on a commercially available HPLC-column with an immobilized artificial membrane (IAM) facilitates consistent measurements of large numbers of chemicals, with simple HPLC systems pumping aqueous eluent into various detectors (RI, ELSD, UV, FLU, and MS/MS).43–46 The experimentally feasible range covers logDMLW 0–6, depending on detection limits for the strongest sorbing chemicals.94–96 Although the silica IAM packing can have confounding coulombic electrostatic effects on the retention of anions and cations, as discussed in Section S1,† this can be corrected for by empirical or modeled Boltzmann factors.27,48,97 Consistent IAM-based DMLW data sets have been derived for both cationic (>150 chemicals26,77,78,98,99) and anionic chemicals (>20 (ref. 27 and 100)).
The first MoA-evaluation approach we selected was to use a widely recognized consistent database of acute toxicity values on a single fish species, performed by a single institute with measured exposure concentrations. The United States Environmental Protection Agencies Fathead Minnow (FHM) database16 provides such MoA data for 616 organic chemicals. We selected 74 neutral chemicals for IAM-measurement: 47 narcotics, 27 with a specific MoA. One of the other criteria for selecting neutral chemicals was that we wanted to address the scarcity of physicochemical information for many chemicals in the FHM set, and selected only chemicals for which no other estimate of KMLW was available other than (estimates of) KOW, as discussed in the Methods section in more detail.
The second MoA-evaluation approach selected was to use chemicals that are designed to exert specific MoA on a certain type of species, while they are expected to have only baseline effects (non-specific MoA) for non-target species. For pesticides, standard toxicity tests on different representative species under strict testing protocols are mandatory in the regulatory process. Also, from the interest of (eco)toxicological assessment, typically pesticides have high quality toxicity data sets on diverse test organisms that allow for adequate comparisons. In this case, we performed IAM-measurements for 12 neutral herbicides as well as for a set of 17 strongly acidic herbicides, which under physiological pH exist for >99.9% as organic anions. For these herbicides, we used the CMBnarc approach to determine to what extent toxicity to ‘likely target’ organisms was more specific than toxicity to ‘non-target’ organisms. For example, algae are likely affected specifically by herbicides, whereas fish are hopefully only reacting non-specifically to herbicides at levels predicted by the CMBnarc approach.
Code | 47 narcosis FHM chemicals | CAS | FHM LC50 (mmol L−1) | FHM LC50 (mg L−1) | Log![]() |
New log![]() |
Nr injections | Solvent range (%) | CMBIAM (mmol kg−1) |
---|---|---|---|---|---|---|---|---|---|
a Coding of MoA of chemicals in the FHM database (set 1): N_I = Narcosis_I; N_II = Narcosis_II (polar narcosis); log![]() |
|||||||||
1N_I-01 | 2-Hydroxyethyl ether | 111-46-6 | 709 | 75![]() |
−1.30 | −0.46 | 2 | 0 | 246 |
1N_I-02 | Triethylene glycol | 112-27-6 | 399 | 59![]() |
−1.24 | 0.01 | 1 | 0 | 408 |
1N_I-03 | 2-Methyl-2,4-pentanediol | 107-41-5 | 90.5 | 10![]() |
−0.67 | 0.75 | 1 | 0 | 509 |
1N_I-04 | Urethane | 51-79-6 | 58.8 | 5240 | −0.15 | 0.48 | 3 | 0 | 178 |
1N_I-05 | 3-Methyl-1-pentyn-3-ol | 77-75-8 | 12.4 | 1220 | 0.86 | 1.19 | 1 | 0 | 192 |
1N_I-06 | n-Phenyldiethanolamine | 120-07-0 | 4.06 | 735 | 0.44 | 1.84 | 2 | 0 | 281 |
1N_I-07 | 3-Methyl-3-pentanol | 77-74-7 | 6.58 | 672 | 1.53 | 1.43 | 5 | 0 | 177 |
1N_I-08 | 2,4,5-Trimethyloxazole | 20![]() |
4.04 | 449 | 1.79 | 1.78 | 3 | 0 | 243 |
1N_I-09 | cis-3-Hexen-1-ol | 928-96-1 | 3.80 | 381 | 1.34 | 1.58 | 2 | 0 | 144 |
1N_I-10 | 2-Methyl-3,3,4,4-tetrafluoro-2-butanol | 29![]() |
3.64 | 582 | 1.03 | 1.53 | 1 | 0 | 123 |
1N_I-11 | trans-3-Hexen-1-ol | 928-97-2 | 2.71 | 271 | 1.34 | 1.62 | 2 | 0 | 113 |
1N_I-12 | 2-Phenoxyethanol | 122-99-6 | 2.49 | 344 | 1.16 | 1.70 | 5 | 0 | 125 |
1N_I-13 | 2,6-Dichlorobenzamide | 2008-58-4 | 2.47 | 469 | 1.25 | 1.51 | 3 | 0 | 80 |
1N_I-14 | 1-Ethynyl-cyclohexanol | 78-27-3 | 2.06 | 256 | 1.73 | 1.80 | 2 | 0 | 130 |
1N_I-15 | 3,4-Dimethyl-1-pentyn-3-ol | 1482-15-1 | 1.84 | 205 | 1.26 | 1.59 | 2 | 0 | 72 |
1N_I-16 | Diethyl benzylphosphonate | 1080-32-6 | 1.47 | 336 | 1.59 | 2.41 | 3 | 0 | 378 |
1N_I-17 | 2-(Bromomethyl)-tetrahydro-2h-pyran | 34![]() |
1.14 | 205 | 1.61 | 1.77 | 4 | 0 | 67 |
1N_I-18 | 2′,3′,4′-Trimethoxy-acetophenone | 13![]() |
1.09 | 229 | 1.12 | 2.39 | 3 | 0 | 268 |
1N_I-19 | 2-Dimethylaminopyridine | 5683-33-0 | 1.04 | 127 | 1.43 | 1.96 | 3 | 0 | 95 |
1N_I-20 | 2-Amino-4-chloro-6-methylpyrimidine | 5600-21-5 | 1.02 | 147 | 1.13 | 1.76 | 3 | 0 | 59 |
1N_I-21 | 2-Phenyl-3-butyn-2-ol | 127-66-2 | 0.773 | 113 | 1.68 | 2.15 | 2 | 0 | 109 |
1N_I-22 | 2,5-Dimethylfuran | 625-86-5 | 0.740 | 71.1 | 2.62 | 2.05 | 4 | 0 | 83 |
1N_I-23 | 2,3-Dihydrobenzofuran | 496-16-2 | 0.680 | 81.7 | 2.14 | 2.18 | 4 | 0 | 113 |
1N_I-24 | 5-Ethyl-2-methylpyridine | 104-90-5 | 0.669 | 81.1 | 2.49 | 2.51 | 3 | 0 | 216 |
1N_I-25 | Benzyl-tert-butanol | 103-05-9 | 0.404 | 66.4 | 2.57 | 2.59 | 3 | 0 | 157 |
1N_I-26 | Benzyl sulfoxide | 621-08-9 | 0.348 | 80.1 | 1.96 | 2.73 | 3 | 0 | 187 |
1N_I-27 | 2-(n-Ethyl-m-toluidino)ethanol | 91-88-3 | 0.295 | 52.9 | 2.49 | 2.74 | 3 | 0 | 163 |
1N_I-28 | α,α,α-Trifluoro-o-tolunitrile | 447-60-9 | 0.247 | 42.2 | 2.46 | 2.54 | 3 | 0 | 86 |
1N_I-29 | α,α,α-4-Tetrafluoro-m-toluidine | 2357-47-3 | 0.168 | 30.1 | 2.62 | 2.64 | 4 | 0 | 73 |
1N_I-30 | α,α,α-4-Tetrafluoro-o-toluidine | 393-39-5 | 0.165 | 29.6 | 2.62 | 2.54 | 7 | 0 | 57 |
1N_I-31 | 4-Ethoxy-2-nitroaniline | 616-86-4 | 0.143 | 26.0 | 2.47 | 2.87 | 3 | 0 | 106 |
1N_I-32 | 4-(Diethylamino)-benzaldehyde | 120-21-8 | 0.135 | 23.9 | 2.94 | 3.10 | 6 | 0 | 170 |
1N_I-33 | 4-Phenylpyridine | 939-23-1 | 0.104 | 16.1 | 2.59 | 3.05 | 3 | 0 | 117 |
1N_I-34 | 1,1-Diphenyl-2-propyn-1-ol | 3923-52-2 | 0.053 | 11.1 | 2.71 | 3.34 | 3 | 0 | 116 |
1N_I-35 | Butyl phenyl ether | 1126-79-0 | 0.0384 | 5.8 | 3.65 | 3.64 | 7 | 15–30% | 168 |
1N_I-36 | 4-(Diethylamino)-salicylaldehyde | 17![]() |
0.0277 | 5.4 | 3.34 | 3.35 | 2 | 0 | 62 |
1N_I-37 | Flavone | 525-82-6 | 0.0157 | 3.5 | 3.56 | 3.90 | 6 | 15–30% | 125 |
1N_I-38 | 2-Amino-4′-chloro-benzophenone | 2894-51-1 | 0.0092 | 2.1 | 3.95 | 4.21 | 6 | 15–30% | 149 |
1N_I-39 | Di-n-butylisophthalate | 3126-90-7 | 0.0032 | 0.9 | 5.53 | 4.67 | 9 | 20–30% | 150 |
1N_I-40 | 3-(4-tert-Butylphenoxy)-benzaldehyde | 79![]() |
0.0015 | 0.4 | 5.93 | 5.41 | 4 | 20–30% | 386 |
1N_I-41 | 3-(3,4-Dichlorophenoxy)-benzaldehyde | 69![]() |
0.0011 | 0.3 | 5.49 | 4.98 | 4 | 20–30% | 105 |
Average | 160 | ||||||||
St. dev. | 105 | ||||||||
1N_II-01 | 6-Chloro-2-pyridinol | 16![]() |
1.65 | 214.0 | 1.78 | 1.34 | 3 | 0 | 36 |
1N_II-02 | 2-Amino-5-bromopyridine | 1072-97-5 | 1.02 | 177.0 | 1.39 | 2.12 | 3 | 0 | 134 |
1N_II-03 | 2-Chloro-4-methylaniline | 615-65-6 | 0.253 | 35.9 | 2.58 | 2.55 | 5 | 0 | 90 |
1N_II-04 | 4-Amino-2-nitrophenol | 119-34-6 | 0.235 | 36.2 | 0.96 | 1.74 | 6 | 0 | 13 |
1N_II-05 | 2-Chloro-4-nitroaniline | 121-87-9 | 0.109 | 18.9 | 2.17 | 2.84 | 4 | 0 | 75 |
1N_II-06 | 3-Trifluoromethyl-4-nitrophenol | 88-30-2 | 0.0441 | 9.1 | 3.00 | 3.28 | 3 | 0 | 84 |
As shown in Fig. 2B, we also selected a set of 27 neutral FHM chemicals that were categorized by MoA, 17 with electrophile/pro-electrophile reactivity (‘REACTIVE’), 7 acetylcholinesterase inhibitors (‘ACHE’), 1 uncoupler of oxidative phosphorylation, and 1 that acts as a respiratory blocker/inhibitor (‘BLOCK’). The selection was aimed at minimizing overlap with the existing IAM-HPLC/liposomal database (see further details in ESI section S2, Table S1B†). These specific MoA FHM-chemicals are listed in Table 2, sorted per MoA by decreasing reported FHM LC50 in mmol L−1.
Codea | 27 specific MoA FHM chemicals | CAS | FHM LC50 (mmol L−1) | LC50 (mg L−1) | Log![]() |
New log![]() |
Nr inj. | Solvent range (%) | CMBIAM (mmol kg−1) |
---|---|---|---|---|---|---|---|---|---|
a Coding of MoA of chemicals in the FHM database (set 1): Ach = acetylcholinesterase inhibitors; Rea = electrophile/proelectrophile reactivity; Unc = uncouplers of oxidative phosphorylation (AUnc = acidic uncoupler); Blo = respiratory blocker/inhibitor; 1CNeu = cationic neurotoxin; CunsA = cationic unsure amine mode of action; AN = acidic narcosis chemical. b δ IAM-SSLM corrected. NA = not available. δIAM-SSLM corrections for 1°, 2°, and 3° amines: +0.78, +0.47, and −0.03, respectively, as in ref. 77. | |||||||||
1Ach-01 | Carbaryl | 63-25-2 | 0.0444 | 8.93 | 2.46 | 2.98 | 3 | 0 | 42.40 |
1Ach-02 | Propoxur | 114-26-1 | 0.0421 | 8.80 | 1.45 | 2.27 | 3 | 0 | 7.84 |
1Ach-03 | Diazinon | 333-41-5 | 0.0307 | 9.35 | 4.19 | 4.05 | 7 | 15–30% | 344.46 |
1Ach-04 | Aminocarb | 2032-59-9 | 0.0094 | 1.95 | 2.16 | 2.48 | 4 | 0 | 2.84 |
1Ach-05 | Aldicarb | 116-06-3 | 0.0045 | 0.86 | 1.12 | 1.91 | 4 | 0 | 0.37 |
1Ach-06 | Carbofuran | 1563-66-2 | 0.0038 | 0.84 | 2.32 | 2.46 | 4 | 0 | 1.10 |
1Ach-07 | EPN | 2104-64-5 | 0.0002 | 0.08 | 3.85 | 5.10 | 7 | 20–30% | 25.18 |
1Ach-08 | Azinphos-methyl | 86-50-0 | 0.0002 | 0.06 | 2.75 | 3.57 | 7 | 15–30% | 0.74 |
1Rea-01 | Butanal | 123-72-8 | 0.2219 | 16.00 | 0.88 | 1.02 | 1 | 0 | 2.32 |
1Rea-02 | 4-Fluoroaniline | 371-40-4 | 0.1521 | 16.90 | 1.15 | 1.63 | 3 | 0 | 6.49 |
1Rea-03 | 2,4-Dinitrotoluene | 121-14-2 | 0.1334 | 24.30 | 2.00 | 2.42 | 3 | 0 | 35.09 |
1Rea-04 | 4-Nitrobenzaldehyde | 555-16-8 | 0.0668 | 10.10 | 1.50 | 1.90 | 3 | 0 | 5.31 |
1Rea-06 | Benzaldehyde | 100-52-7 | 0.0717 | 7.61 | 1.48 | 1.80 | 3 | 0 | 4.52 |
1Rea-05 | 2,4,5-Tribromoimidazole | 2034-22-2 | 0.0261 | 7.96 | 1.96 | 2.27 | 3 | 0 | 4.86 |
1Rea-07 | 4-Dimethylamino-cinnamaldehyde | 6203-18-5 | 0.0337 | 5.90 | NA | 3.11 | 3 | 0 | 43.41 |
1Rea-08 | Salicylaldehyde | 90-02-8 | 0.0188 | 2.30 | 1.81 | 1.91 | 3 | 0 | 1.53 |
1Rea-09 | 4-Chlorocatechol | 2138-22-9 | 0.0109 | 1.58 | 1.97 | 1.60 | 1 | 0 | 0.43 |
1Rea-11 | 1-Benzoylacetone | 93-91-4 | 0.0068 | 1.10 | 1.05 | 2.57 | 3 | 0 | 2.53 |
1Rea-10 | Pentafluorobenzaldehyde | 653-37-2 | 0.0056 | 1.10 | 2.45 | 1.83 | 3 | 0 | 0.38 |
1Rea-12 | α,α,α-Trifluoro-m-tolualdehyde | 454-89-7 | 0.0053 | 0.92 | 2.47 | 2.46 | 3 | 0 | 1.53 |
1Rea-13 | 1,3,5-Trichloro-2,4-dinitrobenzene | 6284-83-9 | 0.0008 | 0.22 | 2.65 | 4.04 | 9 | 20–30% | 8.77 |
1Rea-14 | 2-Methyl-1,4-naphthoquinone | 58-27-5 | 0.0006 | 0.11 | 2.20 | 2.72 | 3 | 0 | 0.31 |
1Rea-15 | α-Bromo-2′,5′-dimethoxyacetophenone | 1204-21-3 | 0.0003 | 0.09 | 2.39 | 3.12 | 3 | 0 | 0.40 |
1Rea-16 | 1,3-Dichloro-4,6-dinitrobenzene | 3698-83-7 | 0.0002 | 0.05 | 2.49 | 2.64 | 4 | 0 | 0.09 |
1Rea-17 | N-Vinylcarbazole | 1484-13-5 | 0.0000166 | 0.0032 | NA | 4.85 | 7 | 20–30% | 1.18 |
1Blo-01 | Rotenone | 83-79-4 | 0.0000114 | 0.0045 | 4.10 | 4.58 | 6 | 20–30% | 0.43 |
5 Mostly anionic FHM chemicals | FHM LC50 (mmol L−1) | LC50 (mg L−1) | Log![]() |
New log![]() |
Log![]() |
CMBIAM (mmol kg−2) | ||||
---|---|---|---|---|---|---|---|---|---|---|
1AUns-01 | Salicylic acid | 54-21-7 | 13.5 | 2160 | 2.26 | 1.49 | 0.99 | 1 | 0 | 131.9 |
1AUnc-01 | 2,4-Dinitrophenol | 51-28-5 | 0.072 | 13.3 | 1.67 | 2.32 | 1.82 | 2 | 0 | 4.8 |
1AUnc-02 | Dinoseb | 88-85-7 | 0.0029 | 0.70 | 4.62 | 3.76 | 3.26 | 2 | 0 | 5.3 |
1AUnc-03 | Pentachlorophenol | 87-86-5 | 0.00083 | 0.22 | 5.12 | 4.45 | 3.95 | 7 | 15–30% | 7.4 |
1AN-01 | 2,3,4,6-Tetrachlorophenol | 58-90-2 | 0.0044 | 1.03 | 4.12 | 3.74 | 3.29 | 8 | 15–30% | 7.6 |
15 Mostly cationic FHM chemicals | FHM LC50 (mmol L−1) | LC50 (mg L−1) | Log![]() |
Log![]() |
CMBIAM (mmol kg−2) | ||
---|---|---|---|---|---|---|---|
1CNeu-01 | Amphetamine sulfate | 60-13-9 | 0.078 | 28.80 | 1.76 | 2.31 | 15.9 |
1CNeu-02 | Nicotine sulfate | 65-30-5 | 0.029 | 12.20 | 1.17 | 0.96 | 0.3 |
1CNeu-03 | Strychnine hemisulphate | 60-41-3 | 0.0029 | 1.11 | 1.93 | 2.28 | 0.6 |
1CUnsA-01 | Phenyltrimethylammonium methosulfate | 98-04-4 | 0.924 | 243 | NA | 0.64 | 4.0 |
1CUnsA-02 | Benzyltriethylammonium chloride | 56-37-1 | 0.952 | 161 | NA | 1.37 | 22.3 |
1CUnsA-03 | N,N-Dimethylbenzylamine | 103-83-3 | 0.707 | 37.8 | 1.95 | 1.08 | 8.5 |
1CUnsA-04 | Hexylamine | 111-26-2 | 0.559 | 56.6 | 2.06 | 2.12 | 73.7 |
1CUnsA-05 | N-Ethylbenzylamine | 14321-27-8 | 0.422 | 57.1 | 2.04 | 1.64 | 18.4 |
1CUnsA-06 | Benzylamine | 100-46-9 | 0.280 | 102 | 1.09 | 1.73 | 15.0 |
1CUnsA-07 | N-Heptylamine | 111-68-2 | 0.190 | 21.8 | 2.57 | 2.54 | 65.9 |
1CUnsA-08 | tert-Octylamine | 107-45-9 | 0.189 | 24.6 | 2.43 | 2.29 | 36.9 |
1CUnsA-09 | 1-Adamantanamine | 768-94-5 | 0.165 | 25.0 | 2.44 | 2.44 | 45.4 |
1CUnsA-10 | Octylamine | 111-86-4 | 0.040 | 5.19 | 3.04 | 3.08 | 48.1 |
1CUnsA-11 | Di-n-hexylamine | 143-16-8 | 0.0065 | 0.78 | 4.77 | 3.28 | 12.4 |
1CUnsA-12 | N-Decylamine | 2016-57-1 | 0.0042 | 1.03 | 4.10 | 4.40 | 105.5 |
A substantial number of chemicals in the FHM database are ionizable chemicals: forty nine are bases that are >95% ionized at tested pH (pKa > 8.5) and nineteen are acids with pKa < 6 (>95% ionized at physiological pH 7.4). MoA classification of most of the ionizable chemicals is equivocal. For chemical set 1, we selected fifteen strong bases/cations listed in the FHM database for which KIAM values were already available, i.e. three neurotoxic bases (amphetamine, strychnine, and nicotine)78 and twelve bases/cations of the type “UNSURE AMINES”.77 The five strongly dissociated acids selected from the FHM database for chemical set 1 had different MoA labels, including three phosphorylation uncouplers. The fifteen selected cations were assumed to be a relevant set of chemicals to evaluate the MoA approach based on the CMB for cations, with expectations that the “UNSURE AMINES” would be considered non-specific (narcosis) chemicals, as they lack specific functional moieties other than the charged amine group, while the three neurotoxic cations are expected to have a CMB significantly below the narcosis level. The set of anions was considered too small to evaluate the MoA approach based on the CMB, which is another reason we included the strongly acidic herbicides for chemical set 2 as part of this study. ESI Section S2† provides more details on the chemical selection procedure, and ESI Table S2† presents a list of the purity and suppliers of the chemicals purchased to create Chemical set 1.
Code | Name (FHM set 1 code, LC50 in mg L−1, MoA) | CAS | Herbicide MoA | pKa | log![]() |
New log![]() |
New log![]() |
Nr inj. | Solvent range (%) | Toxicity endpoint source |
---|---|---|---|---|---|---|---|---|---|---|
a Coding of MoA of chemicals in Set 2: A = acid; N = neutral; 1 Lemna endpoint (or Myriophyllum endpoint between brackets) as listed in Table 5; 2 alg = EC50Raphidocelis subcapitata if no data on aquatic plants were retrieved; 3Oncorhynchys mykiss (rainbow trout) as listed in Table 5. 4 7d ErC50 at pH 7.8, reported as 0.000202 mg L−1 (nom) for the Frond number of Lemna gibba in EFSA J. 2015; 13(1):3958.5 14d ErC50, reported as 0.0008 mg L−1 for the Frond number of Lemna gibba in EFSA Scientific Report 2008; 178, 1. | ||||||||||
Anionic herbicides | ||||||||||
2A-01 | Clopyralid | 1702-17-6 | Synth. auxin | 2.02 | 1.31 | 0.64 | 0.14 | 1 | 0 | EFSA scientific report, 2005, 50, 1–65 |
2A-02 | Fluroxypyr | 69377-81-7 | Synth. auxin | 2.22 | 3.16 | 1.90 | 1.40 | 3 | 0 | EFSA J., 2011, 9(3), 2091 |
2A-03 | Triclopyr | 55335-06-3 | Synth. auxin | 2.26 | 2.98 | 2.16 | 1.66 | 3 | 0 | EFSA scientific report, 2005, 56, 1–103 |
2A-04 | MCPA | 94-74-6 | Synth. auxin | 3.14 | 2.49 | 2.14 | 1.64 | 6 | 0 | EC (2008), Review report for the active substance MCPA (SANCO/4062/2001-final; pp. 1–62) |
2A-05 | 2,4-D | 94-75-7 | Synth. auxin | 2.6 | 2.59 | 2.13 | 1.63 | 3 | 0 | EFSA J. 2014; 12(9), 3812 |
2A-06 | MCPP | 93-65-2 | Synth. auxin | 3.19 | 2.84 | 2.28 | 1.78 | 6 | 0 | EFSA J. 2017; 15(5), 4832 |
2A-07 | 2,4-DP | 120-36-5 | Synth. auxin | 3.1 | 3.43 | 2.29 | 1.79 | 3 | 0 | EFSA J. 2018; 16(6), 5288 |
2A-08 | MCPB | 94-81-5 | Synth. auxin | 4.84 | 3.42 | 2.74 | 2.24 | 3 | 0 | EC (2005). Review report for the active substance MCPB (No. SANCO/4063/2001-final; pp. 1–42) |
2A-09 | 2,4-DB | 94-82-6 | Synth. auxin | 4.95 | 3.53 | 2.78 | 2.28 | 3 | 0 | EFSA J. 2016; 14(5), 4500 |
2A-10 | 2,4,5-T | 93-76-5 | Synth. auxin | 2.88 | 3.3 | 2.59 | 2.09 | 3 | 0 | US EPA ECOTOX/PPDB |
2A-11 | DNOC | 534-52-1 | Uncoupler | 4.31 | 2.13 | 2.65 | 2.15 | 3 | 0 | US EPA ECOTOX (EC (1998). Review report for the active substance DNOC (no. 7777/VI/98-rev. 3; pp. 1–3)) |
2A-12 | Dinoseb | 88-85-7 | Uncoupler | 4.62 | 3.60 | 3.76 | 3.26 | 2 | 0 | REACH registration dossier https://echa.europa.eu/nl/registration-dossier/-/registered-dossier/12446 |
2A-13 | Bromoxynil | 1689-84-5 | PS-II inh. | 4.09 | 2.95 | 2.70 | 2.20 | 2 | 0 | EFSA J., 2017; 15(6), 4790 |
2A-14 | Ioxynil | 1689-83-4 | PS-II inh. | 3.96 | 3.43 | 3.31 | 2.81 | 2 | 0 | EC (2004). Review report for the active substance ioxynil (no. SANCO/4349/2000 final; pp. 1–98) |
2A-15 | Triasulfuron | 82097-50-5 | AHAS inh. | 4.34 | 2.36 | 2.05 | 1.55 | 3 | 0 | EFSA J., 2015; 13(1), 3958 |
2A-16 | Bensulfuron-methyl | 83055-99-6 | AHAS inh. | 3.50 | 2.38 | 2.82 | 2.32 | 6 | 10–20% | EFSA scientific report, 2008, 178, 1–102 |
2A-17 | Fomesafen | 72178-02-0 | PROTOX | 2.42 | 2.94 | 3.48 | 2.98 | 6 | 0–20% | Australian pesticides and veterinary medicines authority (APVMA): public release summary on fomesafen in the product reflex herbicide, ISSN 1443–1335 |
![]() |
||||||||||
Neutral herbicides | ||||||||||
2N-01 | Alachlor | 15972-60-8 | Cell div. inh. (VLCFA) | 3.59 | 3.31 | 10 | 0–30% | EC (2007). Review report for the active substance alachlor (SANCO/4331/2000-final)//US EPA ECOTOX | ||
2N-02 | Metolachlor | 1418095-19-8 | Cell div. inh. (VLCFA) | 3.45 | 3.39 | 8 | 0–30% | Public consultation_S-Metolachlor_RAR_23_LoEP_ 2018-09-06.pdf | ||
2N-03 | Simazine | 122-34-9 | PS-II inh. | 1.78 | 2.49 | 3 | 0 | US EPA ECOTOX (EC (2003)), Review report for the active substance simazine SANCO/10495/2003-rev. Final) | ||
2N-04 | Ametryn | 834-12-8 | PS-II inh. | 2.60 | 3.14 | 3 | 0 | US EPA ECOTOX/REACH registration dossier https://echa.europa.eu/nl/registration-dossier/-/registered-dossier/2171/6/2/1 | ||
2N-05 | Terbuthylazin | 5915-41-3 | PS-II inh. | 2.48 | 3.36 | 3 | 0 | EFSA J. 2011; 9(1):1969/US EPA ECOTOX | ||
2N-06 | Diuron | 330-54-1 | PS-II inh. | 2.53 | 3.38 | 3 | 0 | EFSA scientific report, 2005, 25, 1–58 | ||
2N-07 | Linuron | 330-55-2 | PS-II inh. | 2.30 | 3.51 | 3 | 0 | EFSA J., 2016; 14(7), 4518 | ||
2N-08 | Chloroxuron | 1982-47-4 | PS-II inh. | 3.43 | 4.26 | 6 | 15–30% | US EPA ECOTOX | ||
2N-09 | Ethofumesate | 26225-79-6 | Lipid synth inh. | 2.34 | 3.25 | 3 | 0 | EFSA J., 2016, 14(1), 4374 | ||
2N-10 | Prosulfocarb | 52888-80-9 | Lipid synth inh. | 4.17 | 4.62 | 7 | 15–30% | EFSA scientific report, 2007, 111, 1–81 | ||
2N-11 | Clomazone | 81777-89-1 | Lycopene cyclase inh. | 2.93 | 2.93 | 14 | 0–30% | EFSA scientific report, 2007, 109, 1–73 | ||
2N-12 | Trifluralin | 1582-09-8 | Mitosis inh. | 4.60 | 5.48 | 4 | 20–30% | EFSA scientific report, 2009, 327, 1–111 |
Code | Name (code in Set 1; FHM LC50 in mg L−1, MoA) | CAS | pKa | Log![]() |
New log![]() |
Nr inj. | Solvent range (%) | Log![]() |
δ IAM-liposome | New log![]() |
---|---|---|---|---|---|---|---|---|---|---|
Anion KIAM,intr | Liposome | Log units | δ IAM-SSLM corrected | |||||||
a Coding of MoA of chemicals in set 3: Ca = carboxylic acid; Su = sulfonate acid; Ph = phenolic acid. b Considered outlier (>2 time st. dev.), not included in the calculation of average δIAM-liposome. | ||||||||||
3Ca-01 | Salicylic acid (1AUns-01; 2160, UNSURE) | 54-21-7 | 2.75 | 2.26 | 1.49 | 1 | 0 | 1.03 | 0.46 | 0.99 |
3Ca-02 | 5-Phenylvaleric acid | 2270-20-4 | 4.88 | 2.70 | 1.97 | 1 | 0 | 1.66 | 0.31 | 1.47 |
3Ca-03 | Ibuprofen | 15![]() |
4.45 | 3.50 | 2.78 | 1 | 0 | 1.81 | 0.97 | 2.28 |
3Ca-04 | Fenamic acid | 91-40-7 | 3.99 | 4.36 | 3.43 | 3 | 0 | 2.28 | 1.15 | 2.93 |
3Ca-05 | Diclofenac | 15![]() |
3.99 | 4.5 | 3.76 | 6 | 0 | 2.64 | 1.12 | 3.26 |
3Ca-06 | Diflunisal | 22![]() |
3.00 | 4.44 | 3.73 | 6 | 0 | 2.73 | 1.00 | 3.23 |
3Su-01 | 4-Octylbenzene-1-sulfonate | 6149-03-7 | <0 | 4.65 ACD | 4.81 | 2 | 0 | 3.63 | 1.11 | 4.31 |
3Su-02 | C10-2-LAS | NA | <0 | 5.53 ACD | 5.58 | 8 | 15–30% | 4.79 | 0.79 | 5.11 |
3Ph-01 | Warfarin | 81-81-2 | 4.9 | 2.7 | 3.06 | 5 | 20–30% | 1.40 | 1.66 | 2.56 |
3Ph-02 | Pentafluorophenol | 771-61-9 | 5.53 | 3.05 ACD | 2.20 | 1 | 0 | 1.74 | 0.46 | 1.70 |
3Ph-03 | 2,4-Dinitrophenol (not in set 1; 13.3, UNCOUPLER_1) | 51-28-5 | 3.94 | 1.67 | 2.36 | 1 | 0 | 1.90 | 0.46 | 1.86 |
3Ph-04 | Bromoxynil | 1689-84-5 | 4.09 | 2.8 | 2.70 | 2 | 0 | 2.10 | 0.78 | 2.20 |
3Ph-05 | 2-Methyl-4,6-dinitrophenol | 534-52-1 | 4.31 | 2.20 ACD | 2.77 | 2 | 0 | 2.35 | 0.46 | 2.27 |
3Ph-06 | 4-tert-Butyl-2,6-dinitrophenol | 4097-49-8 | 4.11 | 3.56 ACD | 3.38 | 1 | 0 | 3.23 | 0.18 | 2.88 |
3Ph-07 | Dinoseb (1AUnc-01; 0.7, UNCOUPLER_3) | 88-85-7 | 4.62 | 3.56 | 3.76 | 1 | 0 | 3.35 | 0.43 | 3.26 |
3Ph-08 | 2,3,4,6-Tetrachlorophenol (1AN-01; 1.03, NARCOSIS_I_3) | 58-90-2 | 5.40 | 4.12 | 3.79 | 2 | 0 | 3.46 | 0.28 | 3.29 |
3Ph-09 | 2,3,5,6-Tetrachlorophenol | 935-95-5 | 5.14 | 3.88 | 3.78 | 8 | 15–30% | 3.49 | 0.28 | 3.28 |
3Ph-10 | Pentachlorophenol (1AUnc-02; 0.22, UNCOUPLER_1) | 87-86-5 | 4.75 | 5.12 | 4.45 | 8 | 15–30% | 4.35 | 0.10 | 3.95 |
Average | 0.60 | |||||||||
St. dev. | 0.36 |
A series of at least 3 different eluent mixtures with acetonitrile (≤30%) were applied to chemicals with a logKow >3 as a first indication (Fig. S2†). At least 3 measurements were performed on 3 different solvent mixtures for these chemicals. Linear trends between log
KIAM and fraction solvent in the eluent mixtures were extrapolated to estimate log
KIAM values with a fully aqueous eluent composition in MS Excel.
For 28 chemicals in set 1,2, or 3, where IAM-HPLC retention capacity was measured for a series of (water/acetonitrile) compositions, ESI-fig. S2† shows the extrapolated linear trendlines to derive the KIAM,intr at 100% water. For seven chemicals (2A-16, 2A-17, 2N-01, 2N-02, 2N-11, 3Ca-05, and 3Ca-06), a solvent range was determined as well as measurements at 0% solvent, confirming the linearity of the trendline in this solvent range. For most chemicals with a solvent range extrapolation, the 95% confidence limit for KIAM,intr at 0% solvent was <0.2 log units (details in Fig. S2†), particularly if multiple measurements were made per solvent composition. In some cases, with single measurements per solvent composition (3Ca-04 fenamic acid), or one deviating point (3Ca-05 diclofenac), and with a limited range of 20–30% solvent (1Ach-07 EPN), the 95% confidence limits around the extrapolated 100% aqueous medium KIAM,intr are actually too high to derive a reliable DMLW value for further interpretation. For all other chemicals measured in 100% aqueous medium, replicate IAM measurements demonstrate high consistency (<0.1 log unit deviations for KIAM,intr), and as such also single KIAM measurements for 15 chemicals in 100% aqueous buffer are considered sufficiently reliable to derive the DMLW value.
Since both set 1 and set 2 contain largely dissociated acids, for which the chromatographic method is used to determine the DMLW, the intrinsic KIAM (accounting for electrostatic repulsion from the IAM surface at neutral pH) obtained for the acids of chemical set 3 will be presented and discussed first. Whilst the alignment between liposomal DMLW and KIAM,intr values has been presented in other studies for neutral organics25 and organic cations,26,77,78 the current study provides data for a substantial set of organic anions in addition to the anionic surfactants27 that are already presented in Fig. 1. This collection should demonstrate the uncertainty margins with which IAM-HPLC can be used to derive DMLW for a wide chemical domain that includes both neutral and ionizable organic chemicals.
The data for non-surfactant organic ions demonstrate more scatter than the surfactants. Obviously, surfactants have very simple hydrocarbon or fluorocarbon structures, and don't account for the influence of polar groups on the interaction difference between the IAM monolayer and bilayer liposomes. The empirical incremental δIAM-MLW correction of −0.47 log units derived from the different anionic surfactants was also applied to all corresponding types of organic anions. As shown in the right plot of Fig. 3, for all anions this indeed brings nearly all KIAM,intr values for anions closer to the 1:
1 line with liposomal KMLW,anion values. Warfarin (3Ph-01, indicated by the red arrow in Fig. 4) is the organic anion with the highest deviating KIAM,intr (δIAM-MLW adjusted KIAM,intr still 1.2 log units above liposomal KMLW,anion. Warfarin also showed a higher KIAM compared to liposomal KMLW (pH 7.4) in another study, although the slightly different IAM.PC.DD column was used.101 It is not clear what features of warfarin are responsible for this deviation, although it has a very delocalised charge in comparison to the other carboxylic acids and phenolic acids in the selection. Leaving out warfarin as an atypical outlier, the average difference between KIAM,intr and KMLW,anion for 27 anionic compounds is 0.54 log units, so the final δIAM-MLW remains at −0.5. The root mean square error (RMSE) for all 27 δIAM-MLW adjusted anion log
KIAM,intr values compared to log
KMLW,anion is 0.38. This indicates that there is about a factor of ±3 uncertainty when extrapolating IAM-HPLC measurements for anions (incl. δIAM-MLW) to liposomal DMLW.
For cations, δIAM-MLW was defined for various types of simple amine structures in another study.26 Surprisingly, the diverse set of organic cations does not seem to converge to the 1:
1 line with the δIAM-MLW increments set by the cationic surfactants (Fig. 3B). Several adjusted KIAM,intr values even deviate by more than a log unit from DMLW data, and not one cation has an adjusted KIAM partition coefficient lower than the KMLW values. The most outlying cation is acebutolol (indicated by the green arrow in Fig. 4, log
KMLW,ion 0.66, log
KIAM,intr 2.4, and log
KIAM,intr + δIAM-MLW 2.9). For the majority of chemicals in the right plot of Fig. 4, the δIAM-MLW corrected KIAM,intr values are within a factor of 0.7–10 of the liposomal DMLW data, with a tendency to particularly overestimate DMLW for lower affinity chemicals. Using all data on neutral, anionic and cationic chemicals, the overall double-log linear trendline shows a standard deviation of the residuals (sy·x, the square root of the average squared deviation) of 0.46 log units:
log![]() | (10) |
Eqn (10) may be used to further minimize the error margins between (KIAM,intr + δIAM-MLW) and DMLW for the wide variety of neutral and ionizable chemicals. However, for the current evaluation we only used δIAM-MLW corrective increments for anionic and cationic surfactant DMLW determination, no further corrections were applied for the KIAM of neutral chemicals.
(i) 29 liposomal sorption coefficients (KMLW in the left plot of Fig. 4)
(ii) 30 existing IAM-HPLC partition coefficients (KIAM-exist in the left plot of Fig. 4)
(iii) 47 new IAM-HPLC based KIAM values obtained in this study (KIAM-new in the left plot of Fig. 4)
(iv) 138 ppLFER predicted KMLW values (right plot of Fig. 4).
As shown in the two plots of Fig. 4, most of the predicted toxic concentrations are within a factor of ±3 of the observed acutely toxic concentrations for fathead minnows. When using experimental membrane lipid–water distribution coefficients (Fig. 4 left), this is the case for 79% of the LC50,narc values based on liposomal KMLW values (97% within a factor of 10), and 84% of the LC50,narc values based on (existing and new) IAM-HPLC values (96% within a factor of 10). When using KMLW calculated with ppLFER descriptors (Fig. 4 right), this is the case for 71% of the LC50,narc values based on ppLFER calculated KMLW (93% within a factor of 10).
Fig. 4 demonstrates that the prediction of baseline toxic MoA based on IAM-HPLC derived partition coefficients is accurate for 96% of the tested neutral narcosis chemicals; only for 4% of the tested narcosis chemicals in the FHM set, the LC50 deviates by more than a factor of 10 from the LC50,narc. Fig. 4 also indicates that the previously derived CMB of 140 mmol kg−1 seems to apply equally to Narcosis_I and Narcosis_II classified chemicals, as discussed further below in section (iii). The ppLFER predictions show comparably successful predictions of acutely toxic concentrations, but this typically is only possible when experimental ppLFER descriptors are available.102 The three Narcosis_II classified chemicals that have a ppLFER calculated CMB <14 mmol kg−1 (catechol, pyridine and acetylpyridine) may even be re-classified as having a more specific mode of toxic action.
MoA class | Using experimental DMLW neutral/ions | Using ppLFER (neutral only) | Using experimental KOW neutral only | Using COSMOmic neutral/ion | |
---|---|---|---|---|---|
a The highest calculated CMB of 3832 was considered an outlier, see the text. | |||||
Narcosis_I (EPA) | CMB mmol kg−1 | 155 | 132 | 169 | 397 |
Classes 1 and 2 only | St. dev. | 119 | 202 | 138 | 1039 |
(130 out of 225 classes 1–4) | Min–max | 30–568a | 13–1645 | 2–825 | 2–10599 |
N used | 63 | 65 | 87 | 130 | |
Narcosis_II (EPA) | CMB mmol kg−1 | 72 | 46 | 29 | 35 |
Classes 1 and 2 only | St. dev. | 59 | 45 | 36 | 51 |
(26 out of 36 classes 1–4) | Min–max | 13–247 | 2–152 | 0.9–97.4 | 3–275 |
N used | 15 | 23 | 24 | 29 | |
Specific toxic | CMB mmol kg−1 | 21 | 6 | 20 | n.a. |
Mode of action | St. dev. | 64 | 11 | 66 | |
Min–max | 0.09–341 | 0.24–32 | 0.08–285 | ||
N used | 26 | 8 | 18 | ||
Unsure amines | Average CMB mmol kg−1 | 45 | 179 | ||
St. dev. | 45 | 244 | |||
Min–max | 3–164 | 2–734 | |||
N used | 12 | 12 | |||
Neurotox amphetamine | CMB mmol kg−1 | 16 | 23 | ||
Neurotox nicotine | CMB mmol kg−1 | 0.27 | 0.06 | ||
Neurotox strychnine | CMB mmol kg−1 | 0.55 | 0.06 |
The current study involves a much larger data set on Narcosis_I and Narcosis_II chemicals than the study by Vaes et al.103 Instead of using IAM-HPLC partition coefficients to calculate LC50,narc for comparison with reported LC50 values, as done in the previous section (ii), the KIAM for narcosis FHM chemicals can also be used to re-calculate the CMBnarc. We can now do this for 82 Narcosis_I chemicals (64 classes 1, 2 and 18 class 3) from the FHM database for which KIAM is available. For one Narcosis_I chemical, 2-methyl-2-propanol, a very high CMBnarc of 3832 mmol kg−1 was calculated based on an IAM-HPLC based logDMLW of 1.65 (kIAM = 0.37 from ref. 104, and as such included in a review105 on IAM-HPLC capacity factors). This CMBnarc is more than 6 times higher than the second highest CMB value of 570 mmol kg−1 for Narcosis_I chemicals. The reported log
DMLW may be a considerable overestimation, because the pp-LFER-based log
DMLW estimate is 0.05, which would result in a 40 times lower CMB. The same IAM-HPLC study also reported an almost 3 times lower retention capacity factor for the more hydrophobic homolog 2-methyl-2-butanol, which would have a log
DMLW of only 1.20. Without this outlier, a CMBnarc range of 19–570 mmol kg−1 (average 151 ± 114 s. d.), which closely compares to the average value of 140 mmol kg−1 derived recently.63 For Narcosis_II chemicals still only a limited set of 17 KIAM are available (15 classes 1, 2). This Narcosis_II set shows an average CMBnarc range of 13–247 mmol kg−1 (average 71 ± 56 s. d.). Based on this set of 98 chemicals (the one outlier excluded) with a defined narcosis MoA and measured DMLW, there is a significant distinction between the CMBnarc for Narcosis_I and Narcosis_II chemicals (using Graphpad Prism V9, unpaired t-test, p = 0.006, t = 2.813, and df = 96). This is consistent with a data compilation of several studies where internal whole body residues were measured and it was found that the range of polar narcosis overlaps with non-polar but goes lower in all data sets used. Still, a valuable distinction seems to be the lowest observed CMB of 13 mmol kg−1 for both types of narcosis chemicals.103 For simplicity, we set this limit to 14 mmol kg−1 from here on, as 10% of the average CMBnarc of 140 mmol kg−1.63 Chemicals with CMBnarc calculated using LC50/DMLW above 14 mmol kg−1 most likely do not exert lethal toxicity via a specific MoA, while chemicals with CMBnarc below 14 mmol kg−1 may be considered to have lethal adverse effects via some kind of specific or reactive MoA.
Experimental DMPC membrane–water partition coefficients have been used as the chemical descriptor to plot LC50 values against.103 The difference between Narcosis_I and Narcosis_II chemicals in their analysis was still a factor 1.8 lower average CMBnarc for Narcosis_II chemicals, comparable to our evaluation. For the 8 Narcosis_I chemicals, the CMBnarc ranged between 33 and 513 mmol kg−1 (average 173 mmol kg−1), and for the 10 Narcosis_II chemicals, 11–174 mmol kg−1 (average 94 mmol kg−1). This set did not show a significant difference (p = 0.13, t = 1.555, and df = 17). As discussed in that study, the sorption affinities to the DMPC phospholipids (KDMPC) of Narcosis_II chemicals are relatively higher than to octanol, compared to Narcosis_I chemicals. The fact that Lethal Body Burden (LBB) values differ for Narcosis_I and Narcosis_II chemicals in this study may be related to the fact that these values relate to the whole body of the fish, including storage lipid, and the more polar Narcosis_II chemicals have a distinctly lower affinity for neutral storage lipids compared to polar phospholipids. The rationale behind a similar CMBnarc for all chemicals with a narcosis MoA is that the target site is the cell membrane, and that when normalized to this specific lipid pool all organic chemicals exert baseline toxicity at a comparable molar cell membrane loading.
In conclusion, the CMBnarc is on average about a factor 2 lower for more polar chemicals with a narcosis MoA, and based on experimental DMLW values, there is a significant difference in CMBnarc for Narcosis_I and Narcosis_II classified chemicals. The lower CMBnarc limit of 98 narcosis chemicals (classes 1–3) of the current study and the 18 narcosis chemicals from Vaes et al.103 is 11 mmol kg−1, or about 10% of the average CMBnarc of 140 mmol kg−1 phospholipid. If for a chemical the CMB is lower than 14 mmol kg−1, the toxic effect is thus very likely associated with an adverse effect pathway other than narcosis.
Most of the tested FHM chemicals (23 out of 28) that were originally classified to have a specific MoA are on the right of this dividing line, i.e. these chemicals had lethally toxic effects occurring at membrane burdens below 14 mmol kg−1 (according to the IAM-based DMLW values). This confirms that these chemicals act via a MoA other than narcosis. It is interesting to see that several acetylcholinesterase inhibitors (AChE, indicated by + signs: diazinon, carbaryl, and EPN) are not as specifically (acutely) toxic to fish via the AChE mechanism as compared to other AChE chemicals, but have lethal membrane concentrations associated with a nonspecific narcosis effect. Although these chemicals may still adversely affect fish via the AChE mechanism, the potencies with regard to binding to the enzyme is rather low for these chemicals.
The AChE pesticide most toxic to fathead minnow fish is aldicarb (2.6 log units more toxic than LC50,narc). The maximum difference between the observed fish LC50,FHM and LC50,narc is 3.2 log units (i.e. an “excessive toxicity factor” of 1600) for the reactive chemical 1,3-dichloro-4,6-dinitro-benzene. The most toxic chemical tested in terms of dissolved concentrations was the respiratory blocker/inhibitor rotenone (0.01 μmol L−1), although the reactive chemical 1,3-dichloro-4,6-dinitro-benzene is toxic at the lowest calculated cell membrane concentration (0.1 mmol kg−1). Whether any organic chemical is likely to exerts a specific MoA at levels below or within the CMBnarc range is not readily derived by the current CMB approach. This is part of more detailed risk profile assessments, which may be done using the various MoA and MechoA tools available mentioned in the Introduction, or even based on the likelihood of interactions with the key initiating receptor using the chemical properties of the solute using a polyparameter approach.106
The organic anion salicylic acid is classified in the FHM database as “unsure” MoA, but according to the CMB-approach this organic anion acts as a narcosis organic acid to fish. The acid 2,3,4,6-tetrachlorophenol (pKa 5.4) is classified as a Narcosis_I-3 chemical, but falls in line with the other uncoupler acids, acting at the same level as dinoseb (17 and 24 times more toxic than LC50,narc, respectively). This was already established for guppy fish data based on liposomal distribution coefficients for these same two acids.107 The four tested acidic uncouplers are lethally toxic to FHM at a level 17–24 times below the predicted LC50,narc, and thus also appear to act as toxicants by a specific MoA based on the IAM-HPLC derived KMLW values. Unfortunately, the number of largely dissociated acids in the FHM database with a narcosis MoA is limited, so we focused on herbicides to further evaluate the use of IAM-HPLC values for acidic chemicals to distinguish between specific MoA and baseline toxicity.
As discussed above, in order to translate the IAM-HPLC based KIAM,intr values to DMLW, an empirical corrective increment δIAM-MLW of −0.47 for anions was applied to the values listed in Table 2. Using the critical membrane burden of 140 mmol kg−1 as derived for neutral chemicals, the baseline LC50,narc was calculated, in Table 6 shown in mg L−1 for comparison to the toxicity data.
LC50,narc (mg L−1) (CMBnarc of 140 mmol kg−1) | Algal EC50b (mg L−1) | Algal TR | Macrophyte EC50 (mg L−1) Lemna sp.a (Myrio-phyllum) | Macrophyte TR | Invertebrate EC50c (mg L−1) | Invertebrate TR | Fish LC50d (mg L−1) | Fish TR | |
---|---|---|---|---|---|---|---|---|---|
a 7 d 50% biomass reduction for duckweed species. b 3 d 50% growth reduction of fresh water green algae Raphidocelis subcapitata (formerly Pseudokirchneriella subcapitata). c 2 d 50% immobilization of Daphnia magna. d 4 d 50% lethal effects on rainbow trout (O. mykiss). e SA: synthethic auxin; Unc: Uncoupler of phosphorylation; AHAS inh.: inhibits plant amino acid synthesis – acetohydroxyacid synthase AHAS; PROTOX: inhibits protoporphyrinogen oxidase (PROTOX), leading to irreversible cell membrane damage. PS-II: inhibitor for photosystem II. (VLCFA: very-long-chain fatty acid (inhibition of cell division); LSH: lipid synthesis inhibitor; LCI: lycopene cyclase inhibitor; MIT: mitosis inhibitor. f ✗ = no reliable ecotoxicity data retrieved. g Italic values: not from regulatory dossiers but through the US EPA ECOTOX database. | |||||||||
Anionic herbicidese (MoA, as in Table 3) | |||||||||
Clopyralid (SA) | 19![]() |
30 | 643 | 89 | 217 | 130 | 148 | 53 | 364 |
Fluroxypyr (SA) | 1417 | 35.3 | 40 | 12.3 | 115 | 100 | 14 | 14.3 | 99 |
Triclopyr (SA) | 779 | 75.8 | 10 | ✗f | ✗ | 131 | 6 | 117 | 7 |
MCPA (SA) | 647 | 32.9 | 20 | 0.152 | 4258 | 190 | 3 | 50 | 13 |
2,4-D (SA) | 717 | 0.68 | 1054 | 10.66(0.011) |
67(65![]() |
134.2 | 5 | 100 | 7 |
MCPP (SA) | 503 | 23.9 | 21 | 1.6(0.027) |
315(18![]() |
91 | 6 | 93 | 5 |
2,4-DP (SA) | 532 | 100 | 5 | 50(0.156) | 11(3411) | 46.6 | 11 | 46.6 | 11 |
MCPB (SA) | 184 | 1.5 | 123 | 37 | 5 | 55 | 3 | 4.3 | 43 |
2,4-DB (SA) | 181 | 16.4 | 11 | 23.47(0.51) | 8(355) | 21.2 | 9 | 3 | 60 |
2,4,5-T (SA) | 286 | 100.8 | 3 | ✗ | ✗ | 5.0 | 57 | 38.2 | 7 |
DNOC (Unc) | 267 | 3.4 | 79 | 0.81 | 330 | 3.67 | 73 | 0.066 | 4050 |
Dinoseb (Unc) | 19 | 0.74 | 25 | ✗ | ✗ | 0.24 | 77 | 0.058 | 319 |
Bromoxynil (PS-II) | 242 | 0.12 | 2018 | 0.118 | 2052 | 12.5 | 19 | 23 | 11 |
Ioxynil (PS-II) | 80 | 0.15 | 533 | 0.027 | 2959 | 3.14 | 25 | 0.64 | 125 |
Triasulfuron (AHAS) | 1586 | 0.39 | 4067 | 0.000202 |
7![]() ![]() |
100 | 16 | 100 | 16 |
Bensulfuron-methyl (AHAS) | 274 | 0.0077 |
35![]() |
0.0008 |
342![]() |
130 | 2 | 66 | 4 |
Fomesafen (PROTOX) | 64 | 0.42 | 151 | 0.48 | 133 | 25 | 3 | >99 | 1 |
![]() |
|||||||||
Neutral herbicides (MoA) | |||||||||
Alachlor (VLCFA) | 18 | 0.0016 |
11![]() |
0.0023 | 8043 | 18.4 | 1 | 2.24 | 8 |
Metolachlor (VLCFA) | 16 | 0.056 | 289 | 0.0367 | 441 | 11.24 | 1.4 | 1.23 | 13 |
Simazine (PS-II) | 91 | 0.10 | 914 | 0.14 | 653 | 1.10 | 83 | 60.2 | 2 |
Ametryn (PS-II) | 23 | 0.0032 | 7205 | 0.009 | 2506 | 15 | 2 | 3.6 | 6 |
Terbuthylazine (PS-II) | 14 | 0.028 | 501 | 0.0128 | 1097 | 36 | 0.4 | 2.2 | 6 |
Diuron (PS-II) | 14 | 0.019 | 716 | 0.0183 | 743 | 1.1 | 12 | 6.7 | 2 |
Linuron (PS-II) | 11 | 50.7 | 0.2 | 0.017(0.0317) | 634(340) | 5.81 | 2 | 6.7 | 2 |
Chloroxuron (PS-II) | 2.2 | 0.0160 | 140 | ✗ | 2.95 | 0.8 | 0.43 | 5 | |
Ethofumesate (LSI) | 23 | 4.7 | 5 | 42(0.38) | 0.5(59) | 13.5 | 2 | 11.9 | 2 |
Prosulfocarb (LSI) | 0.8 | 0.049 | 17 | 0.69 | 1.2 | 0.51 | 2 | 11.9 | 1 |
Clomazone (LCI) | 39 | 0.14 | 290 | 34 | 1.2 | 12.7 | 3 | 15.5 | 3 |
Trifluralin (MIT) | 0.2 | 0.0122 | 13 | 0.0435 | 4 | 0.245 | 0.6 | 0.088 | 2 |
Dividing CMBnarc by DMLW, the LC50,narc was calculated as the aqueous concentration at which non-specific MoA was expected to result in a 50% (sub-)lethal effect (Table 6). Just as we did for the evaluation above for 1 fish species (fathead minnow), the LC50,narc serves as the benchmark value to compare the observed toxic concentrations for the four different aquatic organisms evaluated here.
Using the baseline CMBnarc approach, the specificity of an observed toxic effect is more easily expressed by the toxic ratio (TR) in eqn (11):
![]() | (11) |
When including the margins of CMBnarc between 80 and 250 mmol kg−1, and residual uncertainty in the KIAM–DMLW relationship, a TR > 10 is a strong indicator of a chemical exerting adverse effects other than via a non-specific MoA. Since LC50,narc is based on the CMBnarc of 140 mmol kg−1, a TR > 10 is set as the cut-off concentration of the critical membrane burden of 14 mmol kg−1 below which chemicals induce toxicity through a specific MoA. This corresponds to the observation for narcosis chemicals in chemical set 1 with the US-EPA fathead minnow database, where out of 78 Narcosis_I and Narcosis_II chemicals the lowest CMB was 13. The CMBnarc is a fairly constant value across all kinds of organisms56,57,107 and the FHM evaluation above already showed adequate predictions for a broad range of narcosis chemicals in fish. Table 6 lists the TR values for the herbicide endpoints for different aquatic organisms relative to LC50,narc.
We assumed that all herbicides are toxic to aquatic plants via a specific MoA, but mostly act by narcosis (non-specific MoA) on non-target invertebrates and fish, except for uncouplers of oxidative phosphorylation (DNOC and dinoseb). Fig. 6 shows the range of toxicity endpoints for various aquatic species for each herbicide along the Y-axis, in relation to the expected baseline LC50,narc.
As expected, for 21 of the 25 herbicides with complete toxicity data on duckweed and green algae, the herbicides do seem to impair growth by a specific toxic MoA (TR > 10) for at least one of these two aquatic plant species (Table 6). When comparing the two plots of Fig. 6, it appears that most acidic herbicides are more toxic to duckweed than to green algae, while most neutral herbicides affect green algae at lower concentrations than duckweed.
For several anionic synthetic auxins, Lemna and algae appeared to be affected only by baseline toxicity, with TR of less than 10 for 2,4,5-T (algae), 2,4-DB (algae and Lemna), 2,4-DP (Lemna), MCPB (Lemna). Lemna does show a high specific effect for MCPA with a TR > 4000. Myriophyllum, however, appears to be a much more sensitive primary producer than Lemna and R. subcapitata, with a TR of 6.5 × 104 for 2,4-D.
For the herbicide 2,4,5-T, only an algal toxicity endpoint was retrieved, which showed a TR < 10. For 3 out of the 29 herbicides the TR was < 10 for green algae, but TR was > 10 for one or both of the aquatic plants. For the neutral herbicides targeting lipid synthesis (ethofumesate and prosulfocarb), microtubule assembly during mitosis (trifluralin), and lycopene cyclase to block provitamin A carotenoid synthesis (clomazone), duckweed appeared to be affected only through baseline toxicity (TR <10). For ethofumesate, however, Myriophyllum showed a higher TR of 59, and green algae appeared to be specifically affected by clomazone (TR of 290).
The herbicides most toxic to aquatic plants in our data set are the acidic N-sulfonylurea chemicals bensulfuron-methyl and triasulfuron. In target plants, these herbicides inhibit plant-essential amino acid synthesis (acetohydroxyacid synthase AHAS). Particularly duckweed growth is affected by this (or related) specific MoA, at a TR of 3.4 × 105 and 7.8 × 107, for bensulfuron-methyl and triasulfuron respectively. Triasulfuron has been banned for use in the EU since 2017, while bensulfuron-methyl is approved in several European countries, according to the University of Herfortshire's PPDB.
However, the CMB-approach also demonstrates that several herbicides affect non-target aquatic species other than aquatic plants via a specific MoA. Since some of the acidic herbicides were classified as having an uncoupler MoA already in the FHM database, as well as in PPDB (Table 5: DNOC, dinoseb), it was expected that some other acidic herbicides may also demonstrate specific toxicity to daphnids and fish. For 11 of the 17 acidic herbicides the TR was indeed higher than 10, ranging up to 4050. For some of these acidic herbicides fish were the most sensitive species, although no data for Lemna/Myriophyllym were available for both DNOC and dinoseb to evaluate over multiple aquatic plant species. The herbicides most toxic to fish were indeed the phosphorylation uncouplers DNOC and dinoseb, with respective TR values of 4050 and 319. Dinoseb is even considered highly toxic to birds, and while it was once widely used, it has therefore been banned as a pesticide in most countries. DNOC has no longer been approved in the EU and North America since 1991.
Simazine is the only neutral herbicide example in the current selection with a specific toxicity to daphnids, with a TR of 83, while it is not acutely toxic via a specific MoA to rainbow trout. The acids 2,4,5-T, DNOC and dinoseb also appear to affect daphnids by a specific toxicity, with a TR of 53, 77, and 73, as well as the photosystem inhibitors bromoxynil and ioxynil (TR of 19 and 25, resp.).
Footnote |
† Electronic supplementary information (ESI) available: Section S1 reports on the IAM assessment, Section S2 and Tables S1A and B on FHM chemical selection, Table S2 on chemical suppliers, Table S3 on incremental δIAM-MLW correction factors, Table S4 on IAM-HPLC measurement details, and Fig. S2 on solvent range extrapolations of KIAM. See DOI: https://doi.org/10.1039/d2em00391k |
This journal is © The Royal Society of Chemistry 2023 |