- Email: [email protected]
The use of pH measurements to predict the potential of chemicals to cause acute dermal and ocular toxicity
Toxicology 169 (2001) 119– 131 www.elsevier.com/locate/toxicol
The use of pH measurements to predict the potential of chemicals to cause acute dermal and ocular toxicity Andrew P. Worth a,b,*, Mark T.D. Cronin b a
ECVAM, TP 580, Institute for Health and Consumer Protection, Joint Research Centre, European Commission, 21020 Ispra (VA), Italy b School of Pharmacy and Chemistry, Li6erpool John Moores Uni6ersity, Byrom Street, Li6erpool L3 3AF, UK Received 4 May 2001; received in revised form 5 July 2001; accepted 13 August 2001
Abstract Regulatory guidelines for the assessment of acute dermal and ocular toxicity refer to the need to take the pH values of chemicals into consideration, since the acidic and basic properties of chemicals are known to play a role in the generation of acute dermal and ocular lesions. However, not all test guidelines provide an objective interpreting pH measurements in terms of acute skin or eye toxicity. The aim of this study was to develop classification models based on pH data for predicting the potential of chemicals to cause skin corrosion, skin irritation and eye irritation. The possible application of these models in the context of tiered testing strategies is discussed. © 2001 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Alternative method; Physicochemical method; Prediction model; Skin corrosion; Skin irritation; Eye irritation
1. Introduction For regulatory purposes, the Draize rabbit skin and eye tests (Draize et al., 1944) are currently the methods of choice for assessing acute dermal toxicity (skin irritation and corrosion) and acute ocular toxicity (eye irritation and corrosion), respectively (EC, 1993; OECD, 1987). However, for ethical, scientific and economic reasons, a considerable effort has been directed toward the development of alternative methods, such as * Corresponding author. Tel.: + 39-0332-773480; fax: + 390332-785336. E-mail address: [email protected] (A.P. Worth).
physicochemical and in vitro tests, which may ultimately reduce or replace the use of the Draize skin and eye tests. For example, a physicochemical approach for assessing the acute dermal and ocular toxic potentials of a substance is based on the determination of the pH of the substance (a measure of its acidity/alkalinity in aqueous solution, defined as the negative logarithm of the hydrated proton concentration). To interpret the pH value of a substance in terms of its toxic hazard, it is necessary to apply a prediction model (PM), which is an objective means of converting the results of one or more alternative methods into a prediction of an in vivo pharmacotoxicological endpoint (Worth and Balls, 2001). A PM
0300-483X/01/$ - see front matter © 2001 Elsevier Science Ireland Ltd. All rights reserved. PII: S 0 3 0 0 - 4 8 3 X ( 0 1 ) 0 0 4 9 4 - 2
120
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
for skin corrosion potential, which is cited in European Union (EU) regulations on the testing of chemical substances (EC, 2000), and in the Organisation for Economic Cooperation and Development (OECD) test guidelines (OECD, 1992), states that: If the pH of a substance 0 2, or if pH E 11.5, it is likely to be corrosive. (OECD PM) Hereafter, this PM is referred to as the OECD PM. No guidance is provided in OECD test guideline 404 for skin corrosion (OECD, 1992) on the concentration at which the pH should be measured, although the recently revised EU guideline (EC, 2000) states that the pH measurements should be performed on a 10% solution. While the OECD and EU test guidelines provide a PM for skin corrosion potential, they do not provide any PMs for skin and eye irritation potential (a separate PM for eye corrosion potential is not considered necessary, since the PM for skin corrosion potential is assumed to be applicable). This paper reports PMs based on measured pH values to predict the potential of chemicals to cause skin corrosion, skin irritation and eye irritation. The use of these models in the context of tiered testing strategies is also discussed.
2. Methods
2.1. Compilation of data sets To develop PMs based on measured pH values for skin corrosion potential (PM 1) and for skin irritation potential (PM 2), a training set of 44 organic and inorganic chemicals (Table 1) was taken from a data set of 60 chemicals used in the ECVAM validation study on alternative methods for skin corrosivity (Barratt et al., 1998; Fentem et al., 1998). For the purposes of the current investigation, 44 chemicals were chosen from the full set of 60 chemicals on the basis that: (a) they are water-soluble, do not decompose, and do not react with water (as indicated in Fentem et al. (1998)); and (b) they have unambiguous identities (e.g., 20/80 coconut/palm soap was omitted from
the training set). The pH data for 10% solutions of these chemicals had been obtained, using a pH meter (Accumet 15, Fisher Scientific Ltd., Loughborough, UK), by BIBRA International (Croydon, UK) under the terms of an ECVAM contract. The chemicals were classified as skin corrosives (C) or non-corrosives (NC), and as skin irritants (I) or non-irritants (NI), by applying EU classification criteria (EC, 1983) to the animal data (ECETOC, 1995). To investigate the predictive ability of the PM for skin corrosion potential (PM 1), and to compare its predictions with those obtained with the OECD PM, both models were applied to an independent data set of 35 organic and inorganic chemicals (Table 2), taken from Gordon et al. (1994). These chemicals were chosen from a total of 75 chemicals on the basis that: (a) they had not been used in the development of PM 1 (i.e. not listed in Table 1); (b) they are water-soluble, do not decompose, and do not react with water (as indicated in the on-line ChemFinder database of physicochemical properties (http:// www.chemfinder.com); and (c) their chemical identities are unambiguous. For all of these chemicals, pH values had been obtained for a 10% solution, although the experimental method was not mentioned by Gordon et al. (1994). To develop a PM for eye irritation potential (PM 3) based on pH data, a training set of 30 organic chemicals (Table 3) was selected from a data set provided by Re´ gnier and Imbert (1992). The 30 chemicals were chosen on the basis that: (a) they are liquids at physiological temperature (37 °C); (b) they are water-soluble; and (c) their chemical identities are unambiguous. The pH data for these chemicals had been reported for 10% solutions (Re´ gnier and Imbert, 1992), and had been obtained with a Calomel pH electrode. Solid materials were omitted from the training set, partly because the exposure of the rabbit eye to solids is not comparable to that of liquids (Balls et al., 1999), and partly because the irritant effects of solids may be caused by physical abrasion, rather than by a pH-dependent mechanism. Information on the physical state and aqueous solubility of the chemicals was taken from the online ChemFinder
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
database. In cases where solubility information was missing from the database, this was calculated by using the WS-KOWWIN (v. 1.36) software pack-
121
age (Syracuse Research Corporation, NY). The EU classifications of the chemicals (I and NI) were taken from Re´ gnier and Imbert (1992).
Table 1 Skin corrosion/irritation classifications and pH data for 44 chemicals
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
Chemical
C/NC
I/NI
pH
Hexanoic acid 1,2-Diaminopropane Carvacrol Methacrolein Phenethyl bromide Isopropanol 2-Methoxyphenol (guaiacol) 2,4-Xylidine (2,4-dimethylaniline) 2-Phenylethanol (phenylethylalcohol) 3-Methoxypropylamine Allyl bromide Dimethyldipropylenetriamine Methyl trimethylacetate Dimethylisopropylamine Potassium hydroxide Tetrachloroethylene Ferric [iron (III)] chloride Butyl propanoate 2-Tert-butylphenol Sulphuric acid Isostearic acid Methyl palmitate 65/35 Octanoic/decanoic acids 2-Bromobutane 4-(Methylthio)-benzaldehyde 70/30 Oleine/octanoic acid 2-Methylbutyric acid 2-Ethoxyethyl methacrylate Octanoic acid (caprylic acid) Benzyl acetone Heptylamine Cinnamaldehyde 60/40 Octanoic/decanoic acids Eugenol 55/45 Octanoic/decanoic acids Methyl laurate Sodium bicarbonate Sulphamic acid Sodium bisulphite 1-(2-Aminoethyl)piperazine 1,9-Decadiene Phosphoric acid 10-Undecenoic acid 4-Amino-1,2,4-triazole
C C C C NC NC NC NC NC C C C NC C C NC C NC C C NC NC C NC NC NC C NC C NC C NC C NC C NC NC NC NC C NC C NC NC
I I I I NI NI NI NI NI I I I NI I I I I NI I I I I I NI NI I I NI I NI I I I NI I I NI NI NI I I I NI NI
2.57 12.02 4.91 4.18 5.40 5.86 4.86 8.73 5.31 11.78 3.15 11.38 4.96 11.81 13.76 7.13 1.11 4.57 8.17 0.33 4.78 5.69 3.72 3.89 6.38 3.26 2.81 9.52 3.67 4.81 11.88 4.03 3.77 3.68 3.80 5.67 7.89 0.70 3.85 11.67 4.15 1.63 3.88 5.92
C, corrosive (EU risk phrases R34 and R35); I, skin irritant (EU risk phrase R38). The pH data were provided by BIBRA International (Surrey, UK) and refer to measurements made on a 10% solution. The data in this table constitute the training set for PMs 1 and 2.
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
122
Table 2 Skin corrosion classifications and pH data for 35 chemicals
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
Chemical
pH
EU Class
PM 1
OECD PM
Fluorosulphonic acid Nitric acid Selenic acid Trifluoroacetic acid Acetic acid Acetic anhydride Acrylic acid Ammonium hydrogen difluoride Ammonium hydrogen sulphate 2-Anisoyl chloride Bromoacetic acid Chloroacetic acid Cyclohexylamine Dichloroacetic acid Crotonic acid Ethylene diamine Ferrous [iron (II)] chloride Fluoroboric acid Formic acid Fumaryl chloride Hydrogen bromide Lithium hydroxide Mercaptoacetic acid Octyl trichlorosilane Phenylacetyl chloride Hydroxylamine sulphate Potassium hydrogen sulphate Sodium hydroxide Sulphurous acid Tetramethyl ammonium hydroxide Trichloroacetic acid Triethylene tetramine Valeryl chloride Ethyl triglycol methacrylate Mercaptopropanol
0 0 0 0.8 2.3 2 2.1 5.2 0.8 0.7 1.4 1.4 12.3 0.6 2.3 12.1 2.1 1.3 1.6 0.1 0.3 11.8 0.3 0.1 0.9 3.6 0.9 13.8 1.8 13.6 0.7 11.9 0.5 4.5 6.4
C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C NC NC
C C C C C C C NC C C C C C C C C C C C C C C C C C C C C C C C C C NC NC
C C C C NC C NC NC C C C C C C NC C NC C C C C C C C C NC C C C C C C C NC NC
C, corrosive (R34 = moderate corrosive; R35 = severe corrosive); NC, non-corrosive. The pH data are taken from Gordon et al. (1994) and refer to a 10% solution. The data in this table constitute the test set for PM 1 and the OECD PM. Incorrect predictions are in italics.
To investigate the predictive ability of the PM for eye irritation potential (PM 3), the model was applied to an independent data set of ten chemicals (Table 4), taken from Balls et al. (1995). These chemicals were chosen on the basis that: (a) they had not been used in the development of PM 3 (i.e. not listed in Table 3); (b) they are liquids at physiological temperature; (c) they are water-soluble; and (d) their chemical
identities are unambiguous. The pH data for 10% solutions these chemicals had been provided by BIBRA International (Croydon) under the terms of an ECVAM contract. The measurements had been obtained with a pH meter (Accumet 15, Fisher Scientific Ltd.). The chemicals were classified as eye I or NI by applying EU classification criteria (EC, 1983) to the animal data (ECETOC, 1998).
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
2.2. De6elopment of prediction models All PMs were derived by classification tree (CT) analysis, using the CART algorithm (Breiman et al., 1984) in STATISTICA 5.5 for Windows (Statsoft Inc., Tulsa, OK). Proportional prior probabilities were set for the two classes (C/NC or I/NI), the Gini index was used as the measure of node homogeneity, and splitting was stopped when the CT had three terminal nodes.
2.3. Assessment of prediction models The goodness-of-fit and predictive capacity of a two-group PM are generally expressed in terms of the model’s Cooper statistics (Cooper
123
et al., 1979), applied to the model’s training and test sets, respectively. The statistics sensitivity, specificity and concordance provide measures of the PM’s ability to detect known toxic chemicals (sensitivity), non-toxic chemicals (specificity) and all chemicals (accuracy or concordance). Two other statistics, the positive and negative predictivities, are of more interest when focussing on the effects of individual chemicals. These statistics can be thought of as conditional probabilities: if a chemical is predicted to be toxic, the positive predictivity gives the probability that it really is toxic; similarly, if a chemical is predicted to be non-toxic, the negative predictivity gives the probability that it really is nontoxic.
Table 3 Eye irritation classifications and pH data for 30 chemicals
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Chemical
pH
EU class
I/NI
Acetic acid Acrylic anhydride Methacrylic anhydride Dicyclopentenyloxyethyl acrylate Methoxyethyl acrylate Butanol Glycerol Methanol 2-Methoxyethanol Propanol Isopropanol Diethylaminopropylamine Diisopropylamine Dimethylaminopropylamine Dimethyl-N-butylamine Heptylamine 3-Methoxypropylamine Triethanolamine 1,6-Dibromohexane 1,5-Dibromopentane 1,3-Dibromopropane Ethyl bromodifluoroacetate Dimethylsulphoxide 3-Mercaptopropanol Allyl methacrylate Dimethylaminoethyl methacrylate Ethyltriglycol methacrylate Glycidyl methacrylate Dimethylaminopropionitrile Triacetin (glycerol triacetate)
2.5 3.1 3.2 4.1 5.4 6.8 7.9 5.4 5.9 5.8 5.5 11.9 11.7 12.1 11.5 11.7 10.1 10.7 6.9 4.3 3.8 1 5.2 6.4 6.5 8.8 4.5 4 10.1 5.2
R41 R41 R41 R36 R36 R36 NI NI NI NI NI R41 R41 R41 R41 R41 R41 NI NI NI NI R36 NI R41 NI R41 NI R36 R41 NI
I I I I I I NI NI NI NI NI I I I I I I NI NI NI NI I NI I NI I NI I I NI
The chemicals were taken from Re´ gnier and Imbert (1992); the pH data refer to measurements made on a 10% solution. These chemicals constitute the training set for PM 3.
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
124
Table 4 Eye irritation classifications and pH data for 10 chemicals
1 2 3 4 5 6 7 8 9 10
Chemical
pH
EU class
PM 3
Acetone 2-Ethyl hexanol Methyl ethyl ketone Pyridine g-Butyrolactone Methyl acetate Methyl cyanoacetate Ethanol Ethyl 2-methylacetoacetate Ethyl acetate
5.32 4.78 5.51 9.85 4.51 4.84 5.76 6.24 7.47
I NI I I I I I NI NI
NI NI NI I NI NI NI NI NI
4.81
NI
NI
The 10 chemicals are taken from Balls et al. (1995); the pH data were provided by BIBRA International (Surrey) and refer to measurements made on a 10% solution. These chemicals constitute the test set for PM 3. Incorrect predictions are in italics.
For each PM, Cooper statistics were calculated for the application of the PM to its training set, to define an upper limit for the predictive capacity of the model. In addition, cross-validated Cooper statistics were calculated for each PM, since these statistics provide a more realistic indication of the
model’s performance when applied to independent data. To obtain the cross-validated Cooper statistics, a 3-fold cross-validation was applied in which the data set was randomly divided into three approximately equal parts, the CT was reparameterised using two thirds of the data, and predicted classifications were made for the remaining third of the data. The cross-validated Cooper statistics were then taken to be the mean values of the usual Cooper statistics, taken over the three iterations of the cross-validation procedure. The PM for skin corrosion potential (PM 1) and the OECD PM were both applied to a independent test set of 35 chemicals (Table 2). Similarly, the PM for eye irritation potential (PM 3) was applied to an independent test set of ten chemicals (Table 4). 3. Results
3.1. Prediction models for acute dermal toxicity The distribution of pH values for C and NC chemicals in the data set of 44 chemicals is shown
Fig. 1. Distribution of skin corrosives and non-corrosives by pH value. C, corrosive (EU risk phrases R34 and R35); NC, non-corrosive.
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
125
Fig. 2. Classification tree for distinguishing between corrosive and non-corrosive chemicals on the basis of pH measurements. The height of each bar is proportional to the number of corrosive or non-corrosive chemicals in each node. C, corrosive; NC, non-corrosive.
in Fig. 1. It can be seen that the C chemicals span the full range of pH values from 0 to 14, whereas the NC chemicals span a much narrower range from about 4 to 10. The application of CT analysis to the pH data in Table 1 generated a CT (Fig. 2). The CT is interpreted by reading from the root node (node 1) at the top of the tree to the terminal nodes (nodes 3, 4 and 5) at the bottom. The nodes are numbered in the top left corner. Before the splitting process begins, all 44 observations are placed in node 1. According to the first decision rule, which is applied to all observations, seven observations with pH values \10.5 are placed in node 3 and are predicted to be corrosive. The remaining 37 observations are placed in node 2 and subjected to a second decision rule. Application of the second rule leads to 13 observations with pH values B 3.9 being placed in node 4 and being predicted to be corrosive. The remaining 24 observations are placed in node 5 and are predicted to
be non-corrosive. The numbers above each node show how many observations (chemicals) are sent to each node, and the histograms illustrate the relative proportions of corrosive and non-corrosive chemicals in each node. The CT for skin corrosion potential can be summarised in the form of PM 1. If pHB 3.9 or if pH\ 10.5, then predict as C; otherwise, predict NC. (PM 1) Similarly, the distribution of pH values for I and NI chemicals in the data set of 44 chemicals is shown in Fig. 3, and the CT for skin irritation potential (not shown) can be expressed as PM 2. If pHB 4.5 or if pH\ 9.2, then predict as I; otherwise, predict NI. (PM 2) In both PMs 1 and 2, pH is measured for a 10% solution (w/v in the case of liquids, and w/w in the case of solids). Due to the identities of the chemicals in the training set for these PMs (Table
126
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
1), the domain of both models is expected to cover organic acids, inorganic acids, organic bases, inorganic bases, mixtures, neutral organics (such as alcohols, ketones and esters), phenols, and electrophiles (such as aldehydes and alkyl halides). It is important to note that the domain of PMs 1 and 2 excludes insoluble chemicals and chemicals that react with water. The Cooper statistics, and the cross-validated Cooper statistics, obtained by applying PMs 1 and 2 to the training set of 44 chemicals are given in Table 5. The outcome of applying the two PMs to an independent test set of 35 organic and inorganic chemicals is shown in Table 2.
3.2. A prediction model for eye irritation The distribution of pH values for I and NI chemicals in the data set of 30 chemicals is shown in Fig. 4. It can be seen that I chemicals span a wide range of pH values from 0 to about 12, whereas the NI chemicals span a much narrower range from about 4 to 8. Using the cut-off values generated by CT analysis, the following PM was
formulated: If pHB 4.3 or if pH\ 8.4, then predict I; otherwise, predict NI. (PM 3) The domain of PM 3 is expected to cover water-soluble liquids in the following chemical classes: organic acids, organic bases, neutral organics (including alcohols, esters, acrylates). It is unclear whether the domain of PM 3 will extend to inorganic chemicals and to mixtures of chemicals. The Cooper statistics and cross-validated Cooper statistics obtained by applying PM 3 to its training set are summarised in Table 5. The predictions obtained by applying PM 3 to an independent data set of 10 chemicals are shown in Table 4.
4. Discussion The aim of this work was to develop a classification model for predicting the potential of chemicals to cause skin corrosion, to compare this
Fig. 3. Distribution of skin irritants and non-irritants by pH value. I, irritant (EU risk phrase R38); NI, non-irritant.
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
127
Table 5 Performance of the PMs for acute dermal and ocular toxicity Model
PM PM PM PM PM PM
1a 1b 2c 2d 3e 3f
Cooper statistics (%) Sensitivity
Specificity
Concordance
Positive predictivity
Negative predictivity
False positives
False negatives
85 70 79 79 82 59
88 75 91 73 85 54
86 73 82 77 83 57
85 70 96 90 88 63
88 75 59 53 79 50
12 25 9 27 15 46
15 30 21 21 18 41
a
Statistics based on the application of PM 1 to its training set of 44 chemicals. Cross-validated statistics based on the 3-fold cross-validation of PM 1. c Statistics based on the application of PM 2 to its training set of 44 chemicals. d Cross-validated statistics based on the 3-fold cross-validation of PM 2. e Statistics based on the application of PM 3 to its training set of 30 chemicals. f Cross-validated statistics based on the 3-fold cross-validation of PM 3. b
model with the OECD PM, and to explore the possibility of using pH-based classification models for predicting skin irritation and eye irritation. Since the PMs are based on experimental measurements, it is important that the data used to develop the PMs have been obtained by a standardised protocol. The pH measurements analysed in this study are standardised in the sense that they refer to measurements obtained in a 10% aqueous solution.
4.1. General interpretation of the models The PMs have a clear mechanistic interpretation-chemicals that form acidic or basic solutions are likely to be corrosive or irritating. This supports the need to consider the acidic and basic properties of chemicals during the assessment of acute dermal toxicity (EC, 2000; OECD, 1992) and ocular toxicity (OECD, 1987). However, on the basis of the data investigated in this study, it cannot be concluded that chemicals with intermediate pH values are non-corrosive and non-irritant. Indeed, a number of chemicals with intermediate pH values are corrosives (Fig. 2), skin irritants (Fig. 3) or eye irritants (Fig. 4), which indicates that some chemicals elicit their corrosive or irritant effects by mechanisms other than a pH-dependent mechanism. This is espe-
cially true of chemically-induced skin and eye irritation, which are known to be associated with an inflammatory response in which the chemically-induced release of ‘primary’ cytokines leads to the synthesis and release of ‘secondary’ cytokines that help to maintain the inflammatory response. The biochemistry of skin inflammation is reviewed by Corsini and Galli (2000). In addition, there is evidence that the inflammatory responses of the human conjunctiva (Gamache et al., 1997) and the human cornea (Torres et al., 1994) are mediated by cytokines. The domains of the PMs for skin corrosion (PM 1), skin irritation (PM 2) and eye irritation (PM 3) are defined above. It should also be noted that these models are intended to make predictions for neat substances, since most chemicals are applied neat in the Draize skin and eye tests. It is therefore possible that the PMs will over-predict the effects of substances applied to the skin as dilute solutions.
4.2. Assessment of the models PM 1 is a classification model for skin corrosion, similar to the OECD PM cited in OECD test guideline 404 (OECD, 1992) and in the EU regulation for skin corrosion testing (EC, 2000). The predictive performance of PM 1 can be regarded
128
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
as satisfactory on the basis that its sensitivity, specificity, concordance, positive predictivity and negative predictivity are all \50% (Table 5). Although PM 1 has the same model structure as the OECD PM, the pH cut-off values are slightly different: in PM 1, a chemical is considered to be corrosive if its pH is B3.9 or \ 10.5, whereas in the OECD PM, the corresponding pH cut-offs are 2 and 11.5 (i.e. further toward the extremes of the pH scale). This means that, in general, more chemicals will be classified as corrosive under PM 1 than under the OECD PM. Indeed, when PM 1 and the OECD PM are both applied to an independent data set of 35 chemicals (Table 2), it can be seen that the OECD PM generates five false negative predictions, whereas PM 1 only generates one false negative. PM 2 is a classification model for skin irritation. Overall, the Cooper statistics for this model (Table 5) indicate a satisfactory predictive performance, although it should be noted that an independent test set was not available. However, the application of cross-validation revealed a crossvalidated negative predictivity of just 50%, compared with a cross-validated positive predictivity of 88%. This indicates that PM 2 can be used
reliably for identifying irritants, but not non-irritants, which is consistent with the general interpretation discussed above. The PM for eye irritation (PM 3) showed a marginally acceptable performance, (Table 5), although the cross-validated negative productivity was just 50%, again indicating that the model cannot be used for the reliable identification of non-irritants. Paradoxically, when the PM 3 was applied to an independent data set of ten chemicals (Table 4), it correctly identified the four known non-irritants, but only one of the six known irritants. It is possible that the test set was simply too small to bear out the conclusions obtained by cross-validation. Alternatively, the training set could have been too small to find the optimal cut-off values.
4.3. Contribution to existing knowledge As mentioned above, PM 1 is similar to the OECD PM for skin corrosion (EC, 2000; OECD, 1992). It is not clear how the OECD PM was developed, but one can assume that it was defined on the basis of in-house experience. In contrast, PM 1 was developed by applying a statistical
Fig. 4. Distribution of eye irritants and non-irritants by pH value. I, irritant (EU risk phrase R36 or R41); NI, non-irritant.
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
method (CT analysis) to a training set of 44 chemicals, carefully chosen on the basis of their physicochemical properties (water-soluble and non-reactive) and their unambiguous identities. PMs 2 and 3 are classification models for skin and eye irritation, respectively. As far as the author is aware, similar models, based on pH measurements alone, have not been published elsewhere. A PM for eye irritation potential based on pH measurements was published previously by the first author (Worth and Fentem, 1999), although this PM was developed to give a pre-defined sensitivity of about 35% (to enable a comparison with another PM, which also had a sensitivity of 35%). PMs based on the combined use of pH measurements and acid/alkaline reserve (a measure of buffering capacity) have been reported for skin irritation (Young et al., 1988; Young and How, 1994) and for eye irritation (Re´ gnier and Imbert, 1992). It was not the purpose of this study to compare the usefulness of pH measurements alone with the combined use of pH measurements and acid/alkaline reserve, although previous studies have suggested that for pure chemicals (as opposed to chemical mixtures), the use of acid/alkaline reserve (in addition to pH measurements) might not be necessary for classifying skin corrosives (Worth et al., 1998) and eye irritants (Worth and Fentem, 1999).
4.4. Application of the prediction models in tiered testing strategies The PMs reported in this paper could be applied in the context of stepwise testing strategies for acute dermal and/or ocular toxicity, such as the tiered strategies that have been adopted by the OECD (OECD, 1998). The OECD strategies are based on the following premises: (a) alternative methods should be used to identify toxic (corrosive or irritant) chemicals, but not nontoxic chemicals, since the latter can be identified in the Draize test, applied in the final step of the tiered testing strategy; (b) skin corrosion potential should be identified before skin irritation potential, since corrosive chemicals can be assumed to be skin irritants, but NC chemicals may be
129
irritant or non-irritant; (c) chemicals identified as corrosive to the skin can be assumed to be corrosive to the eye as well; and (d) the order of steps in any strategy should reflect the relative ease and cost of applying the models. Thus, a PM-based on pH data should be applied before a PM-based on in vitro data, since physicochemical tests are generally quicker and cheaper to perform than in vitro tests. Further, guidance on the strategic use of PMs is given by Worth and Balls (2001). If the cut-offs for PM 1 are compared with the cut-offs for PM 2, it is noticeable that the cut-offs for skin corrosion are further toward the extremes of the pH scale than are the corresponding cut-offs for skin irritation, which is consistent with the fact that corrosion is a stronger effect than irritation. It is suggested that in a tiered testing strategy for acute dermal toxicity, PM 1 should be applied first to identify corrosive substances, and PM 2 should be applied to those chemicals predicted to be NC by PM 1, to identify substances that are irritant, but not corrosive. On the basis of their Cooper statistics, the PMs for skin irritation and eye irritation would be unsatisfactory as stand-alone models for discriminating between I and NI chemicals. However, in the context of tiered testing strategies, PMs 2 and 3 could be used to identify I chemicals, but not NI chemicals. In other words, PM 2 could be recast as PM 2b, and PM 3 as PM 3b: If pHB 4.5 or if pH\ 9.2, then predict skin irritant; otherwise, make no prediction. (PM 2b)
If pHB4.3 or if pH\ 8.4, then predict eye irritant; otherwise, make no prediction. (PM 3b) The Cooper statistics for PM 1 indicate that this model would be appropriate as a standalone model for discriminating between skin corrosives and non-corrosives. However, since not all chemicals are expected to be corrosive by a pH-dependent mechanism, it would also be wise to use PM 1 in a similar way, i.e. as a model for identifying corrosives, but not non-corrosives.
130
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
5. Conclusions It is concluded that acceptable PMs for identifying skin corrosives and skin irritants can be derived from pH measurements, although it is uncertain whether a pH-based model can be used for the reliable identification of eye irritants. Classification models for identifying skin corrosives and irritants, such as the PMs reported in this study, could therefore be incorporated into tiered testing strategies for acute dermal and ocular toxicity, such as those adopted by the OECD (1998).
Acknowledgements Andrew Worth acknowledges the support of a European Commission Research Training Grant (Marie-Curie programme). The contents of this paper are the sole responsibility of the first author and do not reflect the European Community’s opinion. The European Community is not responsible for any use that might be made of data appearing in this paper.
References Balls, M., Botham, P.A., Bruner, L.H., Spielmann, H., 1995. The EC/HO international validation study on alternatives to the Draize eye irritation test. Toxicology in Vitro 9, 871– 929. Balls, M., Berg, N., Bruner, L.H., Curren, R.D., de Silva, O., Earl, L.K., Esdaile, D.J., Fentem, J.H., Liebsch, M., Ohno, Y., Prinsen, M.K., Spielmann, H., Worth, A.P., 1999. Eye irritation testing: the way forward. The report and recommendations of ECVAM workshop 34. Alternatives to Laboratory Animals 27, 53 –77. Barratt, M.D., Brantom, P.G., Fentem, J.H., Gerner, I., Walker, A.P., Worth, A.P., 1998. The ECVAM international validation study on in vitro tests for skin corrosivity. 1. Selection and distribution of the test chemicals. Toxicology in Vitro 12, 471 –482. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1984. Classification and Regression Trees. Wadsworth, Monterey, CA. Cooper, J.A., Saracci, R., Cole, P., 1979. Describing the validity of carcinogen screening tests. British Journal of Cancer 39, 87 – 89.
Corsini, E., Galli, C.L., 2000. Epidermal cytokines in experimental contact dermatitis. Toxicology 142, 203 – 211. Draize, J.H., Woodard, G., Calvery, H.O., 1944. Methods for study of irritation and toxicity of substances applied topically to the skin and mucous membranes. Journal of Pharmacology and Experimental Therapeutics 82, 377 – 390. EC, 1983. Guide to the classification and labelling of dangerous substances and preparations. Criteria for the choice of chemicals indicating special risks (R phrases) and safety advice (S phrases). Official Journal of the European Communities L257, 13 – 20. EC, 1993. Commission directive 93/21/EEC of 27 April 1993 adapting to technical progress for the 18th time council directive 67/548/EEC on the approximation of laws, regulations and administrative provisions relating to the classification, packaging and labelling of dangerous substances. Official Journal of the European Communities L110A, 1 – 86. EC, 2000. Commission directive 2000/33/EEC of 25 April 2000 adapting to technical progress for the 27th time council directive 67/548/EEC on the approximation of laws, regulations and administrative provisions relating to the classification, packaging and labelling of dangerous substances. Annex I. B40. Skin corrosion. Official Journal of the European Communities L136A, 91 – 97. ECETOC, 1995. Skin Irritation and Corrosion: Reference Chemicals Data Bank. ECETOC Technical Report No. 66. European Centre for Ecotoxicology and Toxicology of Chemicals, Brussels. ECETOC, 1998. Eye Irritation: Reference Chemicals Data Bank. Second Edition. ECETOC Technical Report No. 48. European Centre for Ecotoxicology and Toxicology of Chemicals, Brussels. Fentem, J.H., Archer, G.E.B., Balls, M., Botham, P.A., Curren, R.D., Earl, L.K., Esdaile, D.J., Holzhu¨ tter, H.G., Liebsch, M., 1998. The ECVAM international validation study on in vitro tests for skin corrosivity. 2. Results and evaluation by the management team. Toxicology in Vitro 12, 483 – 524. Gamache, D.A., Dimitrijevich, S.D., Weimer, L.K., Lang, L.S., Spellman, J.M., Graff, G., Yanni, J.M., 1997. Secretion of proinflammatory cytokines by human conjunctival epithelial cells. Ocular Immunology and Inflammation 5, 117 – 128. Gordon, V.C., Harvell, J.D., Maibach, H.I., 1994. Dermal corrosion, the CORROSITEX™ system: a DOT accepted method to predict corrosivity potential of test materials. In: Rougier, A., Goldberg, A.M., Maibach, H.I. (Eds.), In Vitro Skin Toxicology — Irritation, Phototoxicity, Sensitization. Alternative Methods in Toxicology, vol. 10. Mary Ann Liebert, New York, pp. 37 – 45. OECD, 1987. OECD Guideline for the Testing of Chemicals No. 405. Acute Eye Irritation/Corrosion. Organisation for Economic Cooperation and Development: Paris. OECD, 1992. OECD Guideline for the Testing of Chemicals No. 404. Acute Dermal Irritation/Corrosion. Organisation for Economic Cooperation and Development: Paris.
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131 OECD, 1998. Harmonized Integrated Hazard Classification System for Human Health and Environmental Effects of Chemical Substances. Organisation for Economic Cooperation and Development: Paris. Re´ gnier, J.-F., Imbert, C., 1992. Contributions of physicochemical properties to the evaluation of ocular irritation. Alternatives to Laboratory Animals 20, 457 –465. Torres, P., De Vos, A.F., Van der Gaag, R., Kijlstra, A., 1994. Expression of the interleukin 1 receptor antagonist in the normal human cornea. Ocular Immunology and Inflammation 2, 217 – 222. Worth, A.P., Fentem, J.H., Balls, M., Botham, P.A., Curren, R.D., Earl, L.K., Esdaile, D.J., Liebsch, M., 1998. An evaluation of the proposed OECD testing strategy for skin corrosion. Alternatives to Laboratory Animals 26, 709 – 720.
131
Worth, A.P., Balls, M., 2001. The importance of the prediction model in the validation of alternative tests. Alternatives to Laboratory Animals 29, 135 – 143. Worth, A.P., Fentem, J.H., 1999. A general approach for evaluating stepwise testing strategies. Alternatives to Laboratory Animals 27, 161 – 177. Young, J.R., How, M.J., 1994. Product classification as corrosive or irritant by measuring pH and acid/alkali reserve. In: Rougier, A., Goldberg, A.M., Maibach, H.I. (Eds.), In Vitro Methods in Toxicology —Irritation, Phototoxicity, Sensitization. Alternative Methods in Toxicology, vol. 10. Mary Ann Liebert, New York, pp. 23 – 35. Young, J.R., How, M.J., Walker, A.P., Worth, W.M.H., 1988. Classification as corrosive or irritant to skin of preparations containing acidic or alkaline substances without testing on animals. Toxicology in Vitro 2, 19 –26.
The use of pH measurements to predict the potential of chemicals to cause acute dermal and ocular toxicity Andrew P. Worth a,b,*, Mark T.D. Cronin b a
ECVAM, TP 580, Institute for Health and Consumer Protection, Joint Research Centre, European Commission, 21020 Ispra (VA), Italy b School of Pharmacy and Chemistry, Li6erpool John Moores Uni6ersity, Byrom Street, Li6erpool L3 3AF, UK Received 4 May 2001; received in revised form 5 July 2001; accepted 13 August 2001
Abstract Regulatory guidelines for the assessment of acute dermal and ocular toxicity refer to the need to take the pH values of chemicals into consideration, since the acidic and basic properties of chemicals are known to play a role in the generation of acute dermal and ocular lesions. However, not all test guidelines provide an objective interpreting pH measurements in terms of acute skin or eye toxicity. The aim of this study was to develop classification models based on pH data for predicting the potential of chemicals to cause skin corrosion, skin irritation and eye irritation. The possible application of these models in the context of tiered testing strategies is discussed. © 2001 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Alternative method; Physicochemical method; Prediction model; Skin corrosion; Skin irritation; Eye irritation
1. Introduction For regulatory purposes, the Draize rabbit skin and eye tests (Draize et al., 1944) are currently the methods of choice for assessing acute dermal toxicity (skin irritation and corrosion) and acute ocular toxicity (eye irritation and corrosion), respectively (EC, 1993; OECD, 1987). However, for ethical, scientific and economic reasons, a considerable effort has been directed toward the development of alternative methods, such as * Corresponding author. Tel.: + 39-0332-773480; fax: + 390332-785336. E-mail address: [email protected] (A.P. Worth).
physicochemical and in vitro tests, which may ultimately reduce or replace the use of the Draize skin and eye tests. For example, a physicochemical approach for assessing the acute dermal and ocular toxic potentials of a substance is based on the determination of the pH of the substance (a measure of its acidity/alkalinity in aqueous solution, defined as the negative logarithm of the hydrated proton concentration). To interpret the pH value of a substance in terms of its toxic hazard, it is necessary to apply a prediction model (PM), which is an objective means of converting the results of one or more alternative methods into a prediction of an in vivo pharmacotoxicological endpoint (Worth and Balls, 2001). A PM
0300-483X/01/$ - see front matter © 2001 Elsevier Science Ireland Ltd. All rights reserved. PII: S 0 3 0 0 - 4 8 3 X ( 0 1 ) 0 0 4 9 4 - 2
120
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
for skin corrosion potential, which is cited in European Union (EU) regulations on the testing of chemical substances (EC, 2000), and in the Organisation for Economic Cooperation and Development (OECD) test guidelines (OECD, 1992), states that: If the pH of a substance 0 2, or if pH E 11.5, it is likely to be corrosive. (OECD PM) Hereafter, this PM is referred to as the OECD PM. No guidance is provided in OECD test guideline 404 for skin corrosion (OECD, 1992) on the concentration at which the pH should be measured, although the recently revised EU guideline (EC, 2000) states that the pH measurements should be performed on a 10% solution. While the OECD and EU test guidelines provide a PM for skin corrosion potential, they do not provide any PMs for skin and eye irritation potential (a separate PM for eye corrosion potential is not considered necessary, since the PM for skin corrosion potential is assumed to be applicable). This paper reports PMs based on measured pH values to predict the potential of chemicals to cause skin corrosion, skin irritation and eye irritation. The use of these models in the context of tiered testing strategies is also discussed.
2. Methods
2.1. Compilation of data sets To develop PMs based on measured pH values for skin corrosion potential (PM 1) and for skin irritation potential (PM 2), a training set of 44 organic and inorganic chemicals (Table 1) was taken from a data set of 60 chemicals used in the ECVAM validation study on alternative methods for skin corrosivity (Barratt et al., 1998; Fentem et al., 1998). For the purposes of the current investigation, 44 chemicals were chosen from the full set of 60 chemicals on the basis that: (a) they are water-soluble, do not decompose, and do not react with water (as indicated in Fentem et al. (1998)); and (b) they have unambiguous identities (e.g., 20/80 coconut/palm soap was omitted from
the training set). The pH data for 10% solutions of these chemicals had been obtained, using a pH meter (Accumet 15, Fisher Scientific Ltd., Loughborough, UK), by BIBRA International (Croydon, UK) under the terms of an ECVAM contract. The chemicals were classified as skin corrosives (C) or non-corrosives (NC), and as skin irritants (I) or non-irritants (NI), by applying EU classification criteria (EC, 1983) to the animal data (ECETOC, 1995). To investigate the predictive ability of the PM for skin corrosion potential (PM 1), and to compare its predictions with those obtained with the OECD PM, both models were applied to an independent data set of 35 organic and inorganic chemicals (Table 2), taken from Gordon et al. (1994). These chemicals were chosen from a total of 75 chemicals on the basis that: (a) they had not been used in the development of PM 1 (i.e. not listed in Table 1); (b) they are water-soluble, do not decompose, and do not react with water (as indicated in the on-line ChemFinder database of physicochemical properties (http:// www.chemfinder.com); and (c) their chemical identities are unambiguous. For all of these chemicals, pH values had been obtained for a 10% solution, although the experimental method was not mentioned by Gordon et al. (1994). To develop a PM for eye irritation potential (PM 3) based on pH data, a training set of 30 organic chemicals (Table 3) was selected from a data set provided by Re´ gnier and Imbert (1992). The 30 chemicals were chosen on the basis that: (a) they are liquids at physiological temperature (37 °C); (b) they are water-soluble; and (c) their chemical identities are unambiguous. The pH data for these chemicals had been reported for 10% solutions (Re´ gnier and Imbert, 1992), and had been obtained with a Calomel pH electrode. Solid materials were omitted from the training set, partly because the exposure of the rabbit eye to solids is not comparable to that of liquids (Balls et al., 1999), and partly because the irritant effects of solids may be caused by physical abrasion, rather than by a pH-dependent mechanism. Information on the physical state and aqueous solubility of the chemicals was taken from the online ChemFinder
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
database. In cases where solubility information was missing from the database, this was calculated by using the WS-KOWWIN (v. 1.36) software pack-
121
age (Syracuse Research Corporation, NY). The EU classifications of the chemicals (I and NI) were taken from Re´ gnier and Imbert (1992).
Table 1 Skin corrosion/irritation classifications and pH data for 44 chemicals
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
Chemical
C/NC
I/NI
pH
Hexanoic acid 1,2-Diaminopropane Carvacrol Methacrolein Phenethyl bromide Isopropanol 2-Methoxyphenol (guaiacol) 2,4-Xylidine (2,4-dimethylaniline) 2-Phenylethanol (phenylethylalcohol) 3-Methoxypropylamine Allyl bromide Dimethyldipropylenetriamine Methyl trimethylacetate Dimethylisopropylamine Potassium hydroxide Tetrachloroethylene Ferric [iron (III)] chloride Butyl propanoate 2-Tert-butylphenol Sulphuric acid Isostearic acid Methyl palmitate 65/35 Octanoic/decanoic acids 2-Bromobutane 4-(Methylthio)-benzaldehyde 70/30 Oleine/octanoic acid 2-Methylbutyric acid 2-Ethoxyethyl methacrylate Octanoic acid (caprylic acid) Benzyl acetone Heptylamine Cinnamaldehyde 60/40 Octanoic/decanoic acids Eugenol 55/45 Octanoic/decanoic acids Methyl laurate Sodium bicarbonate Sulphamic acid Sodium bisulphite 1-(2-Aminoethyl)piperazine 1,9-Decadiene Phosphoric acid 10-Undecenoic acid 4-Amino-1,2,4-triazole
C C C C NC NC NC NC NC C C C NC C C NC C NC C C NC NC C NC NC NC C NC C NC C NC C NC C NC NC NC NC C NC C NC NC
I I I I NI NI NI NI NI I I I NI I I I I NI I I I I I NI NI I I NI I NI I I I NI I I NI NI NI I I I NI NI
2.57 12.02 4.91 4.18 5.40 5.86 4.86 8.73 5.31 11.78 3.15 11.38 4.96 11.81 13.76 7.13 1.11 4.57 8.17 0.33 4.78 5.69 3.72 3.89 6.38 3.26 2.81 9.52 3.67 4.81 11.88 4.03 3.77 3.68 3.80 5.67 7.89 0.70 3.85 11.67 4.15 1.63 3.88 5.92
C, corrosive (EU risk phrases R34 and R35); I, skin irritant (EU risk phrase R38). The pH data were provided by BIBRA International (Surrey, UK) and refer to measurements made on a 10% solution. The data in this table constitute the training set for PMs 1 and 2.
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
122
Table 2 Skin corrosion classifications and pH data for 35 chemicals
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
Chemical
pH
EU Class
PM 1
OECD PM
Fluorosulphonic acid Nitric acid Selenic acid Trifluoroacetic acid Acetic acid Acetic anhydride Acrylic acid Ammonium hydrogen difluoride Ammonium hydrogen sulphate 2-Anisoyl chloride Bromoacetic acid Chloroacetic acid Cyclohexylamine Dichloroacetic acid Crotonic acid Ethylene diamine Ferrous [iron (II)] chloride Fluoroboric acid Formic acid Fumaryl chloride Hydrogen bromide Lithium hydroxide Mercaptoacetic acid Octyl trichlorosilane Phenylacetyl chloride Hydroxylamine sulphate Potassium hydrogen sulphate Sodium hydroxide Sulphurous acid Tetramethyl ammonium hydroxide Trichloroacetic acid Triethylene tetramine Valeryl chloride Ethyl triglycol methacrylate Mercaptopropanol
0 0 0 0.8 2.3 2 2.1 5.2 0.8 0.7 1.4 1.4 12.3 0.6 2.3 12.1 2.1 1.3 1.6 0.1 0.3 11.8 0.3 0.1 0.9 3.6 0.9 13.8 1.8 13.6 0.7 11.9 0.5 4.5 6.4
C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C NC NC
C C C C C C C NC C C C C C C C C C C C C C C C C C C C C C C C C C NC NC
C C C C NC C NC NC C C C C C C NC C NC C C C C C C C C NC C C C C C C C NC NC
C, corrosive (R34 = moderate corrosive; R35 = severe corrosive); NC, non-corrosive. The pH data are taken from Gordon et al. (1994) and refer to a 10% solution. The data in this table constitute the test set for PM 1 and the OECD PM. Incorrect predictions are in italics.
To investigate the predictive ability of the PM for eye irritation potential (PM 3), the model was applied to an independent data set of ten chemicals (Table 4), taken from Balls et al. (1995). These chemicals were chosen on the basis that: (a) they had not been used in the development of PM 3 (i.e. not listed in Table 3); (b) they are liquids at physiological temperature; (c) they are water-soluble; and (d) their chemical
identities are unambiguous. The pH data for 10% solutions these chemicals had been provided by BIBRA International (Croydon) under the terms of an ECVAM contract. The measurements had been obtained with a pH meter (Accumet 15, Fisher Scientific Ltd.). The chemicals were classified as eye I or NI by applying EU classification criteria (EC, 1983) to the animal data (ECETOC, 1998).
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
2.2. De6elopment of prediction models All PMs were derived by classification tree (CT) analysis, using the CART algorithm (Breiman et al., 1984) in STATISTICA 5.5 for Windows (Statsoft Inc., Tulsa, OK). Proportional prior probabilities were set for the two classes (C/NC or I/NI), the Gini index was used as the measure of node homogeneity, and splitting was stopped when the CT had three terminal nodes.
2.3. Assessment of prediction models The goodness-of-fit and predictive capacity of a two-group PM are generally expressed in terms of the model’s Cooper statistics (Cooper
123
et al., 1979), applied to the model’s training and test sets, respectively. The statistics sensitivity, specificity and concordance provide measures of the PM’s ability to detect known toxic chemicals (sensitivity), non-toxic chemicals (specificity) and all chemicals (accuracy or concordance). Two other statistics, the positive and negative predictivities, are of more interest when focussing on the effects of individual chemicals. These statistics can be thought of as conditional probabilities: if a chemical is predicted to be toxic, the positive predictivity gives the probability that it really is toxic; similarly, if a chemical is predicted to be non-toxic, the negative predictivity gives the probability that it really is nontoxic.
Table 3 Eye irritation classifications and pH data for 30 chemicals
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Chemical
pH
EU class
I/NI
Acetic acid Acrylic anhydride Methacrylic anhydride Dicyclopentenyloxyethyl acrylate Methoxyethyl acrylate Butanol Glycerol Methanol 2-Methoxyethanol Propanol Isopropanol Diethylaminopropylamine Diisopropylamine Dimethylaminopropylamine Dimethyl-N-butylamine Heptylamine 3-Methoxypropylamine Triethanolamine 1,6-Dibromohexane 1,5-Dibromopentane 1,3-Dibromopropane Ethyl bromodifluoroacetate Dimethylsulphoxide 3-Mercaptopropanol Allyl methacrylate Dimethylaminoethyl methacrylate Ethyltriglycol methacrylate Glycidyl methacrylate Dimethylaminopropionitrile Triacetin (glycerol triacetate)
2.5 3.1 3.2 4.1 5.4 6.8 7.9 5.4 5.9 5.8 5.5 11.9 11.7 12.1 11.5 11.7 10.1 10.7 6.9 4.3 3.8 1 5.2 6.4 6.5 8.8 4.5 4 10.1 5.2
R41 R41 R41 R36 R36 R36 NI NI NI NI NI R41 R41 R41 R41 R41 R41 NI NI NI NI R36 NI R41 NI R41 NI R36 R41 NI
I I I I I I NI NI NI NI NI I I I I I I NI NI NI NI I NI I NI I NI I I NI
The chemicals were taken from Re´ gnier and Imbert (1992); the pH data refer to measurements made on a 10% solution. These chemicals constitute the training set for PM 3.
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
124
Table 4 Eye irritation classifications and pH data for 10 chemicals
1 2 3 4 5 6 7 8 9 10
Chemical
pH
EU class
PM 3
Acetone 2-Ethyl hexanol Methyl ethyl ketone Pyridine g-Butyrolactone Methyl acetate Methyl cyanoacetate Ethanol Ethyl 2-methylacetoacetate Ethyl acetate
5.32 4.78 5.51 9.85 4.51 4.84 5.76 6.24 7.47
I NI I I I I I NI NI
NI NI NI I NI NI NI NI NI
4.81
NI
NI
The 10 chemicals are taken from Balls et al. (1995); the pH data were provided by BIBRA International (Surrey) and refer to measurements made on a 10% solution. These chemicals constitute the test set for PM 3. Incorrect predictions are in italics.
For each PM, Cooper statistics were calculated for the application of the PM to its training set, to define an upper limit for the predictive capacity of the model. In addition, cross-validated Cooper statistics were calculated for each PM, since these statistics provide a more realistic indication of the
model’s performance when applied to independent data. To obtain the cross-validated Cooper statistics, a 3-fold cross-validation was applied in which the data set was randomly divided into three approximately equal parts, the CT was reparameterised using two thirds of the data, and predicted classifications were made for the remaining third of the data. The cross-validated Cooper statistics were then taken to be the mean values of the usual Cooper statistics, taken over the three iterations of the cross-validation procedure. The PM for skin corrosion potential (PM 1) and the OECD PM were both applied to a independent test set of 35 chemicals (Table 2). Similarly, the PM for eye irritation potential (PM 3) was applied to an independent test set of ten chemicals (Table 4). 3. Results
3.1. Prediction models for acute dermal toxicity The distribution of pH values for C and NC chemicals in the data set of 44 chemicals is shown
Fig. 1. Distribution of skin corrosives and non-corrosives by pH value. C, corrosive (EU risk phrases R34 and R35); NC, non-corrosive.
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
125
Fig. 2. Classification tree for distinguishing between corrosive and non-corrosive chemicals on the basis of pH measurements. The height of each bar is proportional to the number of corrosive or non-corrosive chemicals in each node. C, corrosive; NC, non-corrosive.
in Fig. 1. It can be seen that the C chemicals span the full range of pH values from 0 to 14, whereas the NC chemicals span a much narrower range from about 4 to 10. The application of CT analysis to the pH data in Table 1 generated a CT (Fig. 2). The CT is interpreted by reading from the root node (node 1) at the top of the tree to the terminal nodes (nodes 3, 4 and 5) at the bottom. The nodes are numbered in the top left corner. Before the splitting process begins, all 44 observations are placed in node 1. According to the first decision rule, which is applied to all observations, seven observations with pH values \10.5 are placed in node 3 and are predicted to be corrosive. The remaining 37 observations are placed in node 2 and subjected to a second decision rule. Application of the second rule leads to 13 observations with pH values B 3.9 being placed in node 4 and being predicted to be corrosive. The remaining 24 observations are placed in node 5 and are predicted to
be non-corrosive. The numbers above each node show how many observations (chemicals) are sent to each node, and the histograms illustrate the relative proportions of corrosive and non-corrosive chemicals in each node. The CT for skin corrosion potential can be summarised in the form of PM 1. If pHB 3.9 or if pH\ 10.5, then predict as C; otherwise, predict NC. (PM 1) Similarly, the distribution of pH values for I and NI chemicals in the data set of 44 chemicals is shown in Fig. 3, and the CT for skin irritation potential (not shown) can be expressed as PM 2. If pHB 4.5 or if pH\ 9.2, then predict as I; otherwise, predict NI. (PM 2) In both PMs 1 and 2, pH is measured for a 10% solution (w/v in the case of liquids, and w/w in the case of solids). Due to the identities of the chemicals in the training set for these PMs (Table
126
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
1), the domain of both models is expected to cover organic acids, inorganic acids, organic bases, inorganic bases, mixtures, neutral organics (such as alcohols, ketones and esters), phenols, and electrophiles (such as aldehydes and alkyl halides). It is important to note that the domain of PMs 1 and 2 excludes insoluble chemicals and chemicals that react with water. The Cooper statistics, and the cross-validated Cooper statistics, obtained by applying PMs 1 and 2 to the training set of 44 chemicals are given in Table 5. The outcome of applying the two PMs to an independent test set of 35 organic and inorganic chemicals is shown in Table 2.
3.2. A prediction model for eye irritation The distribution of pH values for I and NI chemicals in the data set of 30 chemicals is shown in Fig. 4. It can be seen that I chemicals span a wide range of pH values from 0 to about 12, whereas the NI chemicals span a much narrower range from about 4 to 8. Using the cut-off values generated by CT analysis, the following PM was
formulated: If pHB 4.3 or if pH\ 8.4, then predict I; otherwise, predict NI. (PM 3) The domain of PM 3 is expected to cover water-soluble liquids in the following chemical classes: organic acids, organic bases, neutral organics (including alcohols, esters, acrylates). It is unclear whether the domain of PM 3 will extend to inorganic chemicals and to mixtures of chemicals. The Cooper statistics and cross-validated Cooper statistics obtained by applying PM 3 to its training set are summarised in Table 5. The predictions obtained by applying PM 3 to an independent data set of 10 chemicals are shown in Table 4.
4. Discussion The aim of this work was to develop a classification model for predicting the potential of chemicals to cause skin corrosion, to compare this
Fig. 3. Distribution of skin irritants and non-irritants by pH value. I, irritant (EU risk phrase R38); NI, non-irritant.
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
127
Table 5 Performance of the PMs for acute dermal and ocular toxicity Model
PM PM PM PM PM PM
1a 1b 2c 2d 3e 3f
Cooper statistics (%) Sensitivity
Specificity
Concordance
Positive predictivity
Negative predictivity
False positives
False negatives
85 70 79 79 82 59
88 75 91 73 85 54
86 73 82 77 83 57
85 70 96 90 88 63
88 75 59 53 79 50
12 25 9 27 15 46
15 30 21 21 18 41
a
Statistics based on the application of PM 1 to its training set of 44 chemicals. Cross-validated statistics based on the 3-fold cross-validation of PM 1. c Statistics based on the application of PM 2 to its training set of 44 chemicals. d Cross-validated statistics based on the 3-fold cross-validation of PM 2. e Statistics based on the application of PM 3 to its training set of 30 chemicals. f Cross-validated statistics based on the 3-fold cross-validation of PM 3. b
model with the OECD PM, and to explore the possibility of using pH-based classification models for predicting skin irritation and eye irritation. Since the PMs are based on experimental measurements, it is important that the data used to develop the PMs have been obtained by a standardised protocol. The pH measurements analysed in this study are standardised in the sense that they refer to measurements obtained in a 10% aqueous solution.
4.1. General interpretation of the models The PMs have a clear mechanistic interpretation-chemicals that form acidic or basic solutions are likely to be corrosive or irritating. This supports the need to consider the acidic and basic properties of chemicals during the assessment of acute dermal toxicity (EC, 2000; OECD, 1992) and ocular toxicity (OECD, 1987). However, on the basis of the data investigated in this study, it cannot be concluded that chemicals with intermediate pH values are non-corrosive and non-irritant. Indeed, a number of chemicals with intermediate pH values are corrosives (Fig. 2), skin irritants (Fig. 3) or eye irritants (Fig. 4), which indicates that some chemicals elicit their corrosive or irritant effects by mechanisms other than a pH-dependent mechanism. This is espe-
cially true of chemically-induced skin and eye irritation, which are known to be associated with an inflammatory response in which the chemically-induced release of ‘primary’ cytokines leads to the synthesis and release of ‘secondary’ cytokines that help to maintain the inflammatory response. The biochemistry of skin inflammation is reviewed by Corsini and Galli (2000). In addition, there is evidence that the inflammatory responses of the human conjunctiva (Gamache et al., 1997) and the human cornea (Torres et al., 1994) are mediated by cytokines. The domains of the PMs for skin corrosion (PM 1), skin irritation (PM 2) and eye irritation (PM 3) are defined above. It should also be noted that these models are intended to make predictions for neat substances, since most chemicals are applied neat in the Draize skin and eye tests. It is therefore possible that the PMs will over-predict the effects of substances applied to the skin as dilute solutions.
4.2. Assessment of the models PM 1 is a classification model for skin corrosion, similar to the OECD PM cited in OECD test guideline 404 (OECD, 1992) and in the EU regulation for skin corrosion testing (EC, 2000). The predictive performance of PM 1 can be regarded
128
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
as satisfactory on the basis that its sensitivity, specificity, concordance, positive predictivity and negative predictivity are all \50% (Table 5). Although PM 1 has the same model structure as the OECD PM, the pH cut-off values are slightly different: in PM 1, a chemical is considered to be corrosive if its pH is B3.9 or \ 10.5, whereas in the OECD PM, the corresponding pH cut-offs are 2 and 11.5 (i.e. further toward the extremes of the pH scale). This means that, in general, more chemicals will be classified as corrosive under PM 1 than under the OECD PM. Indeed, when PM 1 and the OECD PM are both applied to an independent data set of 35 chemicals (Table 2), it can be seen that the OECD PM generates five false negative predictions, whereas PM 1 only generates one false negative. PM 2 is a classification model for skin irritation. Overall, the Cooper statistics for this model (Table 5) indicate a satisfactory predictive performance, although it should be noted that an independent test set was not available. However, the application of cross-validation revealed a crossvalidated negative predictivity of just 50%, compared with a cross-validated positive predictivity of 88%. This indicates that PM 2 can be used
reliably for identifying irritants, but not non-irritants, which is consistent with the general interpretation discussed above. The PM for eye irritation (PM 3) showed a marginally acceptable performance, (Table 5), although the cross-validated negative productivity was just 50%, again indicating that the model cannot be used for the reliable identification of non-irritants. Paradoxically, when the PM 3 was applied to an independent data set of ten chemicals (Table 4), it correctly identified the four known non-irritants, but only one of the six known irritants. It is possible that the test set was simply too small to bear out the conclusions obtained by cross-validation. Alternatively, the training set could have been too small to find the optimal cut-off values.
4.3. Contribution to existing knowledge As mentioned above, PM 1 is similar to the OECD PM for skin corrosion (EC, 2000; OECD, 1992). It is not clear how the OECD PM was developed, but one can assume that it was defined on the basis of in-house experience. In contrast, PM 1 was developed by applying a statistical
Fig. 4. Distribution of eye irritants and non-irritants by pH value. I, irritant (EU risk phrase R36 or R41); NI, non-irritant.
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
method (CT analysis) to a training set of 44 chemicals, carefully chosen on the basis of their physicochemical properties (water-soluble and non-reactive) and their unambiguous identities. PMs 2 and 3 are classification models for skin and eye irritation, respectively. As far as the author is aware, similar models, based on pH measurements alone, have not been published elsewhere. A PM for eye irritation potential based on pH measurements was published previously by the first author (Worth and Fentem, 1999), although this PM was developed to give a pre-defined sensitivity of about 35% (to enable a comparison with another PM, which also had a sensitivity of 35%). PMs based on the combined use of pH measurements and acid/alkaline reserve (a measure of buffering capacity) have been reported for skin irritation (Young et al., 1988; Young and How, 1994) and for eye irritation (Re´ gnier and Imbert, 1992). It was not the purpose of this study to compare the usefulness of pH measurements alone with the combined use of pH measurements and acid/alkaline reserve, although previous studies have suggested that for pure chemicals (as opposed to chemical mixtures), the use of acid/alkaline reserve (in addition to pH measurements) might not be necessary for classifying skin corrosives (Worth et al., 1998) and eye irritants (Worth and Fentem, 1999).
4.4. Application of the prediction models in tiered testing strategies The PMs reported in this paper could be applied in the context of stepwise testing strategies for acute dermal and/or ocular toxicity, such as the tiered strategies that have been adopted by the OECD (OECD, 1998). The OECD strategies are based on the following premises: (a) alternative methods should be used to identify toxic (corrosive or irritant) chemicals, but not nontoxic chemicals, since the latter can be identified in the Draize test, applied in the final step of the tiered testing strategy; (b) skin corrosion potential should be identified before skin irritation potential, since corrosive chemicals can be assumed to be skin irritants, but NC chemicals may be
129
irritant or non-irritant; (c) chemicals identified as corrosive to the skin can be assumed to be corrosive to the eye as well; and (d) the order of steps in any strategy should reflect the relative ease and cost of applying the models. Thus, a PM-based on pH data should be applied before a PM-based on in vitro data, since physicochemical tests are generally quicker and cheaper to perform than in vitro tests. Further, guidance on the strategic use of PMs is given by Worth and Balls (2001). If the cut-offs for PM 1 are compared with the cut-offs for PM 2, it is noticeable that the cut-offs for skin corrosion are further toward the extremes of the pH scale than are the corresponding cut-offs for skin irritation, which is consistent with the fact that corrosion is a stronger effect than irritation. It is suggested that in a tiered testing strategy for acute dermal toxicity, PM 1 should be applied first to identify corrosive substances, and PM 2 should be applied to those chemicals predicted to be NC by PM 1, to identify substances that are irritant, but not corrosive. On the basis of their Cooper statistics, the PMs for skin irritation and eye irritation would be unsatisfactory as stand-alone models for discriminating between I and NI chemicals. However, in the context of tiered testing strategies, PMs 2 and 3 could be used to identify I chemicals, but not NI chemicals. In other words, PM 2 could be recast as PM 2b, and PM 3 as PM 3b: If pHB 4.5 or if pH\ 9.2, then predict skin irritant; otherwise, make no prediction. (PM 2b)
If pHB4.3 or if pH\ 8.4, then predict eye irritant; otherwise, make no prediction. (PM 3b) The Cooper statistics for PM 1 indicate that this model would be appropriate as a standalone model for discriminating between skin corrosives and non-corrosives. However, since not all chemicals are expected to be corrosive by a pH-dependent mechanism, it would also be wise to use PM 1 in a similar way, i.e. as a model for identifying corrosives, but not non-corrosives.
130
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131
5. Conclusions It is concluded that acceptable PMs for identifying skin corrosives and skin irritants can be derived from pH measurements, although it is uncertain whether a pH-based model can be used for the reliable identification of eye irritants. Classification models for identifying skin corrosives and irritants, such as the PMs reported in this study, could therefore be incorporated into tiered testing strategies for acute dermal and ocular toxicity, such as those adopted by the OECD (1998).
Acknowledgements Andrew Worth acknowledges the support of a European Commission Research Training Grant (Marie-Curie programme). The contents of this paper are the sole responsibility of the first author and do not reflect the European Community’s opinion. The European Community is not responsible for any use that might be made of data appearing in this paper.
References Balls, M., Botham, P.A., Bruner, L.H., Spielmann, H., 1995. The EC/HO international validation study on alternatives to the Draize eye irritation test. Toxicology in Vitro 9, 871– 929. Balls, M., Berg, N., Bruner, L.H., Curren, R.D., de Silva, O., Earl, L.K., Esdaile, D.J., Fentem, J.H., Liebsch, M., Ohno, Y., Prinsen, M.K., Spielmann, H., Worth, A.P., 1999. Eye irritation testing: the way forward. The report and recommendations of ECVAM workshop 34. Alternatives to Laboratory Animals 27, 53 –77. Barratt, M.D., Brantom, P.G., Fentem, J.H., Gerner, I., Walker, A.P., Worth, A.P., 1998. The ECVAM international validation study on in vitro tests for skin corrosivity. 1. Selection and distribution of the test chemicals. Toxicology in Vitro 12, 471 –482. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1984. Classification and Regression Trees. Wadsworth, Monterey, CA. Cooper, J.A., Saracci, R., Cole, P., 1979. Describing the validity of carcinogen screening tests. British Journal of Cancer 39, 87 – 89.
Corsini, E., Galli, C.L., 2000. Epidermal cytokines in experimental contact dermatitis. Toxicology 142, 203 – 211. Draize, J.H., Woodard, G., Calvery, H.O., 1944. Methods for study of irritation and toxicity of substances applied topically to the skin and mucous membranes. Journal of Pharmacology and Experimental Therapeutics 82, 377 – 390. EC, 1983. Guide to the classification and labelling of dangerous substances and preparations. Criteria for the choice of chemicals indicating special risks (R phrases) and safety advice (S phrases). Official Journal of the European Communities L257, 13 – 20. EC, 1993. Commission directive 93/21/EEC of 27 April 1993 adapting to technical progress for the 18th time council directive 67/548/EEC on the approximation of laws, regulations and administrative provisions relating to the classification, packaging and labelling of dangerous substances. Official Journal of the European Communities L110A, 1 – 86. EC, 2000. Commission directive 2000/33/EEC of 25 April 2000 adapting to technical progress for the 27th time council directive 67/548/EEC on the approximation of laws, regulations and administrative provisions relating to the classification, packaging and labelling of dangerous substances. Annex I. B40. Skin corrosion. Official Journal of the European Communities L136A, 91 – 97. ECETOC, 1995. Skin Irritation and Corrosion: Reference Chemicals Data Bank. ECETOC Technical Report No. 66. European Centre for Ecotoxicology and Toxicology of Chemicals, Brussels. ECETOC, 1998. Eye Irritation: Reference Chemicals Data Bank. Second Edition. ECETOC Technical Report No. 48. European Centre for Ecotoxicology and Toxicology of Chemicals, Brussels. Fentem, J.H., Archer, G.E.B., Balls, M., Botham, P.A., Curren, R.D., Earl, L.K., Esdaile, D.J., Holzhu¨ tter, H.G., Liebsch, M., 1998. The ECVAM international validation study on in vitro tests for skin corrosivity. 2. Results and evaluation by the management team. Toxicology in Vitro 12, 483 – 524. Gamache, D.A., Dimitrijevich, S.D., Weimer, L.K., Lang, L.S., Spellman, J.M., Graff, G., Yanni, J.M., 1997. Secretion of proinflammatory cytokines by human conjunctival epithelial cells. Ocular Immunology and Inflammation 5, 117 – 128. Gordon, V.C., Harvell, J.D., Maibach, H.I., 1994. Dermal corrosion, the CORROSITEX™ system: a DOT accepted method to predict corrosivity potential of test materials. In: Rougier, A., Goldberg, A.M., Maibach, H.I. (Eds.), In Vitro Skin Toxicology — Irritation, Phototoxicity, Sensitization. Alternative Methods in Toxicology, vol. 10. Mary Ann Liebert, New York, pp. 37 – 45. OECD, 1987. OECD Guideline for the Testing of Chemicals No. 405. Acute Eye Irritation/Corrosion. Organisation for Economic Cooperation and Development: Paris. OECD, 1992. OECD Guideline for the Testing of Chemicals No. 404. Acute Dermal Irritation/Corrosion. Organisation for Economic Cooperation and Development: Paris.
A.P. Worth, M.T.D. Cronin / Toxicology 169 (2001) 119–131 OECD, 1998. Harmonized Integrated Hazard Classification System for Human Health and Environmental Effects of Chemical Substances. Organisation for Economic Cooperation and Development: Paris. Re´ gnier, J.-F., Imbert, C., 1992. Contributions of physicochemical properties to the evaluation of ocular irritation. Alternatives to Laboratory Animals 20, 457 –465. Torres, P., De Vos, A.F., Van der Gaag, R., Kijlstra, A., 1994. Expression of the interleukin 1 receptor antagonist in the normal human cornea. Ocular Immunology and Inflammation 2, 217 – 222. Worth, A.P., Fentem, J.H., Balls, M., Botham, P.A., Curren, R.D., Earl, L.K., Esdaile, D.J., Liebsch, M., 1998. An evaluation of the proposed OECD testing strategy for skin corrosion. Alternatives to Laboratory Animals 26, 709 – 720.
131
Worth, A.P., Balls, M., 2001. The importance of the prediction model in the validation of alternative tests. Alternatives to Laboratory Animals 29, 135 – 143. Worth, A.P., Fentem, J.H., 1999. A general approach for evaluating stepwise testing strategies. Alternatives to Laboratory Animals 27, 161 – 177. Young, J.R., How, M.J., 1994. Product classification as corrosive or irritant by measuring pH and acid/alkali reserve. In: Rougier, A., Goldberg, A.M., Maibach, H.I. (Eds.), In Vitro Methods in Toxicology —Irritation, Phototoxicity, Sensitization. Alternative Methods in Toxicology, vol. 10. Mary Ann Liebert, New York, pp. 23 – 35. Young, J.R., How, M.J., Walker, A.P., Worth, W.M.H., 1988. Classification as corrosive or irritant to skin of preparations containing acidic or alkaline substances without testing on animals. Toxicology in Vitro 2, 19 –26.