Predicting Chemical Toxicity and Fate - Section 5 (end) ppsx

SECTION 5 Application © 2004 by CRC Press LLC C HAPTER 18 The Tiered Approach to Toxicity Assessment Based on the Integrated Use of Alternative (Non-Animal) Tests Andrew P. Worth CONTENTS I. Introduction A.Alternative Methods to Animal Testing B. Prediction Models and Structure-Activity Relationships C. Tiered Testing Strategies D. Statistical Assessment of Classification Models E. Purpose of this Chapter II. Development of a Tiered Approach to Hazard Classification A. Development of a Quantitative Structure-Activity Relationship B. Development of a Prediction Model Based on pH Data C. Development of a Prediction Model Based on EPISKIN Data D. Assessment of the Classification Models E. Incorporation of the Classification Models into a Tiered Testing Strategy III. Evaluation of the Tiered Approach to Hazard Classification A. Evaluation Method B. Results of the Evaluation IV. Conclusions V. Discussion A. Interpretation of the Classification Models B. Comments of the Design of Tiered Testing Strategies References I. INTRODUCTION A. Alternative Methods to Animal Testing In the context of laboratory animal use, alternative methods include all procedures that can completely replace the need for animals (replacement alternatives), reduce the number of animals © 2004 by CRC Press LLC required (reduction alternatives), or diminish the amount of distress or pain suffered by animals (refinement alternatives), in meeting the essential needs of man and other animals (Smyth, 1978). The concept of the three Rs (replacement, reduction, and refinement), attributed to Russell and Burch (1959), is now enshrined in the laws of many countries and in Directive 86/609/EEC on the protection of animals used for experimental and other scientific purposes (European Commission, 1986). This directive requires that replacement alternatives, reduction alternatives, and refinement alternatives should be used wherever and whenever possible. Alternative methods include: (1) computer-based methods (mathematical models and expert systems); (2) physicochemical methods, in which physical or chemical effects are assessed in systems lacking cells; and, most typically, (3) in vitro methods, in which biological effects are observed in cell cultures, tissues, or organs. Alternative methods for the safety and toxicity testing of chemicals and products (e.g., cosmet- ics, medicines, and vaccines) are particularly important, since regulations exist at both the national and international levels to ensure that such chemicals and products can be manufactured, transported and used without adversely affecting human health or the environment. Traditionally, safety and toxicity testing has been conducted on animals. However, animal tests have been criticized not only on ethical grounds, but also on scientific and economic grounds. There has been a considerable effort to develop and validate alternative tests, with a view to increasing their use for regulatory purposes. Validation is a crucial stage in the evolution of any alternative test from its development to its routine application. It consists of the independent assessment of the relevance and reliability of the test, and therefore forms the scientific basis on which regulators can decide whether to incorporate the alternative test into legislation or into a test guideline. A number of successfully validated alternative tests have already been accepted by regulatory authorities at national and international levels, and incorporated into various regulations and test guidelines (European Com- mission, 2000; Organization for Economic Co-operation and Development, 2002a; 2002b; 2002c). A comprehensive review of the current status of alternative tests has recently been produced by European Center for the Validation of Alternative Methods (ECVAM) (Worth and Balls, 2002). B. Prediction Models and Structure-Activity Relationships To make predictions of toxic potential by using a physicochemical or an in vitro test system, it is necessary to have a means of extrapolating the physicochemical or in vitro data to the in vivo level. To achieve this, Bruner et al. (1996) introduced the concept of the prediction model (PM), which has been defined as an unambiguous decision rule that converts the results of one or more alternative methods into the prediction of an in vivo pharmacotoxicological endpoint (Worth and Balls, 2001). A PM could be a classification model (CM) for predicting toxic potential, or it could be a regression model for predicting toxic potency. The usefulness of an alternative method for regulatory purposes is formally assessed by per- forming an interlaboratory validation study. The alternative method is judged valid for a specific purpose (e.g., the classification of chemicals on the basis of skin corrosivity) if it meets predefined criteria of reliability and relevance (Balls and Karcher, 1995). In this context, reliable means that the data generated by the alternative method are reproducible (within and between laboratories). Relevant means that the method has a sound scientific basis (mechanistic relevance) and is asso- ciated with a PM of sufficient predictive ability (predictive relevance). In addition to using PMs, predictions of toxic hazard can also be made by using structure- activity relationships (SARs). A quantitative structure-activity relationship (QSAR) can be defined as any mathematical model for predicting biological activity from the structure or physicochemical properties of a chemical. In this chapter, the premodifer quantitative is used in accordance with the recommendation of Livingstone (1995) to indicate that a quantitative measure of chemical structure is used. In contrast, a SAR is simply a (qualitative) association between a specific molecular (sub)structure and biological activity. © 2004 by CRC Press LLC A subtle distinction can be made between QSARs and the PMs associated with physicochemical tests. The distinction is that while any PM (associated with a physicochemical test) could also be called a QSAR, not all QSARs could also be called PMs. For example, QSARs can also be based on theoretical descriptors (e.g., topological indices) or on experimental properties that are them- selves more easily predicted than measured (e.g., the octanol-water partition coefficient). Further- more, QSARs developed for the prediction of physicochemical and in vitro end points would not be regarded as PMs. C. Tiered Testing Strategies Because of the limitations of individual alternative (non-animal) methods for predicting toxi- cological hazard, there is a growing emphasis on the use of integrated approaches that combine the use of two or more alternative tests. This has led to the concept of the integrated testing strategy, which has been defined as follows (Blaauboer et al., 1999): An integrated testing strategy is any approach to the evaluation of toxicity which serves to reduce, refine or replace an existing animal procedure, and which is based on the use of two or more of the following: physicochemical, in vitro, human (e.g., epidemiological, clinical case reports), and animal data (where unavoidable), and computational methods, such as (quantitative) structure-activity rela- tionships ([Q]SAR) and biokinetic models. Since integrated testing strategies are based on the use of different types of information, they are expected to be particularly successful at predicting in vivo end points that are too complex in biochemical and physiological terms for any single method to reproduce. A particular type of integrated testing strategy is the so-called tiered (stepwise or hierarchical) testing strategy. This is based on the sequential use of existing information and data derived from alternative methods, before any animal testing is performed. The outlines of tiered testing strategies have been proposed for a variety of human health end points (Worth and Balls, 2002). An important principle in the design of many strategies for hazard classification is that chemicals that are predicted to be toxic in an early step are classified without further assessment. Conversely, chemicals that are predicted to be non-toxic proceed to the next step for further assessment. In this way, it is intended that toxic chemicals will be identified by non-animal methods, while the animal tests performed at the end of the stepwise procedure will merely serve to confirm predictions of non-toxicity made in previous steps. At the regulatory level, a stepwise approach for classifying skin irritants and corrosives has been based on this principle, and is included in a supplement to Organization for Economic Co-operation and Development (OECD) Test Guideline 404 (Organization for Economic Cooper- ation and Development, 2001). This testing strategy is an adaptation of a testing strategy adopted by the OECD in November 1998 (Organization for Economic Co-operation and Development, 1998). D. Statistical Assessment of Classification Models QSARs, PMs based on physicochemical data, and PMs based on in vitro data can all be used to make predictions on a categorical scale. Such CMs are often developed and evaluated on the basis that they will be applied as stand-alone alternatives to animal experiments, but in practice they are more likely to be used in the context of a tiered testing strategy. The predictive performance of a CM is often expressed in terms of a contingency table (Table 18.1) containing the numbers of true and false positive and negative predictions made by the CM, and in terms of the CM’s Cooper statistics, which are derived from the contingency table. Definitions of the Cooper statistics are provided in Table 18.2. © 2004 by CRC Press LLC E. Purpose of this Chapter The objectives of this chapter are to illustrate: 1. The development of a tiered testing strategy for predicting a particular kind of toxic potential, skin corrosion, based on the sequential use of a QSAR; a PM based on physicochemical (pH) data; and a PM based on in vitro data obtained with the EPISKIN™ test, a particular type of human skin model 2. A method for evaluation of the tiered testing strategy in terms of its predictive capacity and its ability to reduce and refine the use of laboratory animals II. DEVELOPMENT OF A TIERED APPROACH TO HAZARD CLASSIFICATION To develop a tiered approach to hazard classification, it is first necessary to use existing data to develop the CMs that will serve as the individual steps of the tiered strategy. The example presented in this chapter used existing data on skin corrosion, and represents a development of earlier work (Worth et al., 1998). A. Development of a Quantitative Structure-Activity Relationship Before developing a QSAR for skin corrosion, a data set of 277 organic chemicals (Table 18.3) was constructed from a variety of literature sources (Barratt, 1995; 1996a; 1996b; European Centre for Ecotoxicology and Toxicology of Chemicals, 1995; National Institutes of Health, 1999; Whittle et al., 1996). Chemicals taken from the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) data bank (European Centre for Ecotoxicology and Toxicology of Chemicals, 1995) were classified for skin corrosion potential according to European Union (EU) classification criteria; in the case of the chemicals taken from the other sources, the published classifications of corrosion potential were used. Table 18.1 A 2 vv vv 2 Contingency Table Predicted Class Non-toxic Toxic Marginal Totals Observed (in vivo) Class Non-toxic Toxic a c b d a + b c + d Marginal totals a + c b + d a + b + c + d Table 18.2 Definitions of the Cooper Statistics Statistic Definition: “The Proportion (or Percentage) of the … Sensitivity Toxic chemicals (chemicals that give positive results in vivo) which the CM predicts to be toxic.” = d/(c + d) Specificity Non-toxic chemicals (chemicals that give negative results in vivo) which the CM predicts to be non-toxic.” = a/(a + b) Concordance or accuracy Chemicals which the CM classifies correctly.” = (a + d)/(a + b + c + d) Positive predictivity Chemicals predicted to be toxic by the CM that give positive results in vivo.” = d/(b + d) Negative predictivity Chemicals predicted to be non-toxic by the CM that give negative results in vivo.” = a/(a + c) False positive (overclassification) rate Non-toxic chemicals that are falsely predicted to be toxic by the CM.” = b/(a + b) = 1 – specificity False negative (under-classification) rate Toxic chemicals that are falsely predicted to be non-toxic by the CM.” = c/(c + d) = 1 – sensitivity © 2004 by CRC Press LLC Table 18.3 Skin Corrosion Data for 277 Organic Chemicals Chemical Source C/NC MP MW 1 1-Naphthoic acid Barratt (1996a) NC 106.7 172.2 2 1-Naphthol Barratt (1996a) NC 67.7 144.2 3 2,3-Lutidine Barratt (1996a) NC –7.6 107.2 4 2,3-Xylenol Barratt (1996a) C 25.4 122.2 5 2,4,6-Trichlorophenol Barratt (1996a) NC 63.8 197.5 6 2,4-Dichlorophenol Barratt (1996a) NC 46.8 163.0 7 2,4-Dinitrophenol Barratt (1996a) NC 118.5 184.1 8 2,4-Xylenol Barratt (1996a) C 25.4 122.2 9 2,5-Dinitrophenol Barratt (1996a) NC 118.5 184.1 10 2,5-Xylenol Barratt (1996a) C 25.4 122.2 11 2,6-Xylenol Barratt (1996a) C 25.4 122.2 12 2-Bromobenzoic acid Barratt (1995b) NC 81.6 201.0 13 2-Butyn-1,4-diol Barratt (1996b) C 29.0 86.1 14 2-Chlorobenzaldehyde Barratt (1996b) C 8.7 140.6 15 2-Chloropropanoic acid Barratt (1996a) C 8.1 108.5 16 2-Ethylphenol Barratt (1996a) NC 27.1 122.2 17 2-Hydroxyethyl acrylate Barratt (1996b) C –15.9 116.1 18 2-Mercaptoethanoic acid Barratt (1996a) C 18.8 92.1 19 2-Naphthoic acid Barratt (1996a) NC 106.7 172.2 20 2-Naphthol Barratt (1996a) NC 67.7 144.2 21 2-Nitrophenol Barratt (1996a) NC 70.8 139.1 22 2-Phenylphenol Barratt (1996a) NC 86.6 170.2 23 3-Methylbutanal Barratt (1996b) NC –79.3 86.1 24 3-Nitrophenol Barratt (1996a) NC 70.8 139.1 25 3-Picoline Barratt (1996a) NC –25.9 93.1 26 3-Toluidine Barratt (1995b) NC 11.6 107.2 27 4-Ethylbenzoic acid Barratt (1996a) NC 73.5 150.2 28 4-Methoxyphenol Barratt (1996a) NC 25.2 124.1 29 4-Nitrophenol Barratt (1996a) NC 70.8 139.1 30 4-Nitrophenylacetic acid Barratt (1996a) NC 124.3 181.2 31 4-Picoline Barratt (1996a) NC –25.9 93.1 32 Acridine Barratt (1995b) NC 100.3 179.2 33 Acrolein Barratt (1996b) C –94.6 56.1 34 Acrylic acid Barratt (1995b) C –36.5 74.1 35 Aminotris(methylphosphonic acid) Barratt (1996a) C 90.3 299.1 36 Barbituric acid Barratt (1996a) NC 199.0 128.1 37 Benzoic acid Barratt (1996a) NC 48.9 122.1 38 Benzylamine Barratt (1996a) C –6.2 93.1 39 Butyric acid Barratt (1996a) C 3.0 88.1 40 Catechol Barratt (1996a) NC 45.7 110.1 41 Citric acid Barratt (1995b) NC 169.2 192.1 42 Cocoamine (dodecylamine) Barratt (1995b) C 35.1 185.4 43 Cyanoacetic acid Barratt (1996a) C 38.0 85.1 44 Cyclopropane carboxylic acid Barratt (1996a) C 13.0 86.1 45 Decanoic acid Barratt (1995b) NC 62.7 172.3 46 Formaldehyde Barratt (1996b) C –110.9 30.0 47 Fumaric acid Barratt (1996a) NC 84.1 116.1 48 Glycolic acid Barratt (1996a) NC 23.3 76.1 49 Glyoxylic acid Barratt (1996a) C 16.1 74.0 50 Hexylcinnamic aldehyde Barratt (1996b) NC 44.4 216.3 51 Hydrogenated tallow amine (hexadecylamine) Barratt (1996a) NC 75.6 241.5 52 Hydroquinone Barratt (1996a) NC 45.7 110.1 53 Imidazole Barratt (1995b) NC 18.5 68.1 54 Iodoacetic acid Barratt (1996a) C 29.6 186.0 55 Isobutanal Barratt (1996b) NC –80.2 72.1 56 Isobutyric acid Barratt (1996a) C –8.3 88.1 57 Isoeugenol Barratt (1996a) NC 61.9 164.2 58 Isoquinoline Barratt (1995b) NC 37.6 129.2 © 2004 by CRC Press LLC Table 18.3 (continued) Skin Corrosion Data for 277 Organic Chemicals Chemical Source C/NC MP MW 59 Kojic acid Barratt (1996a) NC 96.2 142.1 60 Lactic acid Barratt (1995b) C 22.7 90.1 61 Malic acid Barratt (1996a) NC 112.7 134.1 62 Malonic (propanedioic) acid Barratt (1996a) NC 73.3 104.1 63 3-Cresol Barratt (1995b) C 15.7 108.1 64 Methoxyacetic acid Barratt (1996a) C 8.7 90.1 65 Methyl isothiocyanate Barratt (1996b) C –63.3 73.1 66 Morpholine Barratt (1995b) C –15.2 87.1 67 Myristic (tetradecanoic) acid Barratt (1995b) NC 99.7 228.4 68 2-Cresol Barratt (1995b) C 15.7 108.1 69 Oxalic (ethanedioic) acid Barratt (1995b) C 63.0 90.0 70 4-Cresol Barratt (1995b) C 15.7 108.1 71 Propargyl alcohol Barratt (1996b) C –49.0 56.1 72 Propylphosphonic acid Barratt (1996a) C 28.3 124.1 73 Pyridine Barratt (1995b) NC -44.5 79.1 74 Pyruvic acid Barratt (1996a) C 28.2 88.1 75 Quinoline Barratt (1995b) NC 37.6 129.2 76 Salicylic acid Barratt (1995b) NC 93.8 138.1 77 Succinic acid Whittle (1996) NC 83.3 118.1 78 Thymol Barratt (1996a) C 38.1 150.2 79 trans-Cinnamic acid Barratt (1995b) NC 69.5 148.2 80 3-Methoxyphenol Barratt (1996a) NC 25.2 124.1 81 4-Ethylphenol Barratt (1996a) NC 27.1 122.2 82 Phenol Barratt (1995b) C –2.3 94.1 83 1,1,1-Trichloroethane ECETOC (1995) NC –72.0 133.4 84 1,13-Tetradecadiene ECETOC (1995) NC –1.2 194.4 85 1,3-Dibromopropane ECETOC (1995) NC –27.0 201.9 86 1,5-Hexadiene ECETOC (1995) NC –96.7 82.2 87 1,6-Dibromohexane ECETOC (1995) NC 7.9 244.0 88 1,9-Decadiene ECETOC (1995) NC –46.8 138.3 89 10-Undecenoic Acid ECETOC (1995) NC 71.5 184.3 90 1-Bromo-2-chloroethane ECETOC (1995) NC –58.0 143.4 91 1-Bromo-4-chlorobutane ECETOC (1995) NC –33.6 171.5 92 1-Bromo-4-fluorobenzene ECETOC (1995) NC –19.1 175.0 93 1-Bromohexane ECETOC (1995) NC –41.6 165.1 94 1-Bromopentane ECETOC (1995) NC –53.8 151.1 95 1-Decanol ECETOC (1995) NC 7.9 158.3 96 1-Formyl-1-methyl-4(4-methyl-3-penten-1-yl)-3-cyclohexane ECETOC (1995) NC 46.5 208.4 97 2,3-Dichloroproprionitrile ECETOC (1995) NC –21.2 124.0 98 2,4-Decadienal ECETOC (1995) NC 6.0 154.3 99 2,4-Dimethyl-3-cyclohexene-1-carboxaldehyde ECETOC (1995) NC –10.1 138.2 100 2,4-Dimethyltetrahydrobenzaldehyde ECETOC (1995) NC –10.1 138.2 101 2,4-Dinitromethylaniline ECETOC (1995) NC 108.9 197.2 102 2,4-Hexadienal ECETOC (1995) NC –56.2 96.1 103 2,4-Xylidine ECETOC (1995) NC 34.7 135.2 104 2,5-Methylene-6-propyl-3-cyclo-hexen-carbaldehyde ECETOC (1995) NC 15.2 164.3 105 2,6-Dimethyl-2,4,6-octatriene ECETOC (1995) NC –21.2 134.2 106 2,6-Dimethyl-4-heptanol ECETOC (1995) NC –38.1 144.3 107 2-Bromobutane ECETOC (1995) NC –78.1 137.0 108 2-Bromopropane ECETOC (1995) NC –91.0 123.0 109 2-Chloronitrobenzene ECETOC (1995) NC 48.8 157.6 110 2-Ethoxyethyl methacrylate ECETOC (1995) NC –25.2 158.2 111 2-Ethylhexanal ECETOC (1995) NC –42.3 128.2 112 2-Ethylhexylpalmitate ECETOC (1995) NC 117.2 368.7 113 2-Fluorotoluene ECETOC (1995) NC –54.2 110.1 114 2-Methoxyethyl acrylate ECETOC (1995) C –56.2 128.2 115 2-Methoxyphenol (guaiacol) Barratt (1996a) NC 25.2 124.1 © 2004 by CRC Press LLC Table 18.3 (continued) Skin Corrosion Data for 277 Organic Chemicals Chemical Source C/NC MP MW 116 2-Methyl-4-phenyl-2-butanol ECETOC (1995) NC 30.4 164.3 117 2-Methylbutyric acid ECETOC (1995) C 3.6 102.1 118 2-Phenylethanol (phenylethylalcohol) ECETOC (1995) NC 5.8 122.2 119 2-Phenylpropanal (2-phenylpropionaldehyde) ECETOC (1995) NC –10.0 134.2 120 2-tert-Butylphenol ECETOC (1995) C 36.9 150.2 121 3,3d-Dithiopropionic acid ECETOC (1995) NC 141.5 210.3 122 3,7-Dimethyl-2,6-nonadienal ECETOC (1995) NC –3.9 180.3 123 3-Chloro-4-fluoronitrobenzene ECETOC (1995) NC 44.2 175.6 124 3-Diethylaminopropionitrile ECETOC (1995) NC –0.4 126.2 125 3-Mercapto-1-propanol ECETOC (1995) NC –33.6 92.2 126 3-Methoxypropylamine ECETOC (1995) NC –40.4 89.1 127 3-Methylphenol ECETOC (1995) NC 15.7 108.1 128 3-Methylbutyraldehyde ECETOC (1995) NC –79.3 86.1 129 4-(Methylthio)-benzaldehyde ECETOC (1995) NC 28.6 152.2 130 4,4d-Methylene-bis-(2,6-ditert-butylphenol) ECETOC (1995) NC 208.5 424.7 131 4-Amino-1,2,4-triazole ECETOC (1995) NC 31.0 84.1 132 4-Tricyclo-decylindene-8-butanal ECETOC (1995) NC 233.9 494.9 133 6-Butyl-2,4-dimethyldihydropyrane ECETOC (1995) NC –2.3 168.3 134 E-Hexyl cinnamic aldehyde ECETOC (1995) NC 44.4 216.3 135 E-Ionol ECETOC (1995) NC 45.2 194.3 136 Allyl bromide ECETOC (1995) C –80.5 121.0 137 Allyl heptanoate ECETOC (1995) NC –10.8 170.3 138 Allyl phenoxyacetate ECETOC (1995) NC 36.5 192.2 139 E-Terpineol ECETOC (1995) NC 12.4 154.3 140 E-Terpinyl acetate ECETOC (1995) NC 21.5 196.3 141 Benzyl acetate ECETOC (1995) NC –0.5 150.2 142 Benzyl acetone ECETOC (1995) NC 12.8 148.2 143 Benzyl alcohol ECETOC (1995) NC –5.4 108.1 144 Benzyl benzoate ECETOC (1995) NC 70.8 212.3 145 Benzyl salicylate ECETOC (1995) NC 115.5 228.3 146 F-Ionol ECETOC (1995) NC 54.5 194.3 147 Butyl propanoate ECETOC (1995) NC –44.6 130.2 148 Carvacrol ECETOC (1995) C 38.1 150.2 149 Cinnamaldehyde ECETOC (1995) NC 0.0 132.2 150 Cinnamyl alcohol ECETOC (1995) NC 15.8 134.2 151 cis-Cyclooctene ECETOC (1995) NC –58.8 110.2 152 cis-Jasmone ECETOC (1995) NC 40.2 164.3 153 Citrathal ECETOC (1995) NC 4.8 226.4 154 Cyclamen aldehyde ECETOC (1995) NC 29.1 190.3 155 Diacetyl ECETOC (1995) NC –41.7 86.1 156 Dichloromethane ECETOC (1995) NC –89.5 84.9 157 Diethyl phthalate ECETOC (1995) NC –1.7 222.2 158 Diethylaminopropylamine ECETOC (1995) C 0.7 130.2 159 Dihydromercenol ECETOC (1995) NC –10.6 156.3 160 Dimethyl disulphide ECETOC (1995) NC –69.7 94.2 161 Dimethylbenzylcarbinyl acetate ECETOC (1995) NC 28.3 192.3 162 Dimethyldipropylenetriamine ECETOC (1995) C 40.4 159.3 163 Dimethylisopropylamine ECETOC (1995) C –95.4 87.2 164 Dimethyl butylamine ECETOC (1995) C –70.6 101.2 165 Dipropyl disulphide ECETOC (1995) NC –21.8 150.3 166 Dipropylene glycol ECETOC (1995) NC 6.1 134.2 167 dl-Citronellol ECETOC (1995) NC –12.2 156.3 168 d-Limonene ECETOC (1995) NC –40.8 136.2 169 Dodecanoic (lauric) acid ECETOC (1995) NC 81.9 200.3 170 Erucamide ECETOC (1995) NC 183.4 337.6 171 Ethyl thioethyl methacrylate ECETOC (1995) NC –8.5 174.3 172 Ethyl tiglate ECETOC (1995) NC –53.9 128.2 © 2004 by CRC Press LLC Table 18.3 (continued) Skin Corrosion Data for 277 Organic Chemicals Chemical Source C/NC MP MW 173 Ethyl triglycol methacrylate ECETOC (1995) NC 51.3 246.3 174 Ethyl trimethyl acetate ECETOC (1995) NC –68.4 116.2 175 Eucalyptol ECETOC (1995) NC 8.1 154.3 176 Eugenol ECETOC (1995) NC 60.6 164.2 177 Fluorobenzene ECETOC (1995) NC –73.0 96.1 178 Geraniol ECETOC (1995) NC –10.8 154.3 179 Geranyl dihydrolinalol ECETOC (1995) NC 60.0 292.5 180 Geranyl linalool ECETOC (1995) NC 58.5 290.5 181 Glycol bromoacetate ECETOC (1995) C 1.2 303.9 182 Heptanal ECETOC (1995) NC –43.0 114.2 183 Heptyl butyrate ECETOC (1995) NC 1.7 186.3 184 Heptylamine ECETOC (1995) C –21.6 115.2 185 Hexyl salicylate ECETOC (1995) NC 99.7 222.3 186 Hydroxycitronellal ECETOC (1995) NC 23.4 172.3 187 Isobornyl acetate ECETOC (1995) NC 34.1 196.3 188 Isobutyraldehyde ECETOC (1995) NC –92.1 72.1 189 Isopropanol ECETOC (1995) NC –89.2 60.1 190 Isopropyl isostearate ECETOC (1995) NC 80.6 326.6 191 Isopropyl myristate ECETOC (1995) NC 44.4 270.5 192 Isopropyl palmitate ECETOC (1995) NC 72.0 298.5 193 Isostearic acid ECETOC (1995) NC 125.2 284.5 194 Isostearyl alcohol ECETOC (1995) NC 77.3 270.5 195 Lilestralis lilial ECETOC (1995) NC 46.3 204.3 196 Linalol ECETOC (1995) NC –11.4 154.3 197 Linalol oxide ECETOC (1995) NC 31.1 170.3 198 Linalyl acetate ECETOC (1995) NC –2.1 196.3 199 Methacrolein ECETOC (1995) C –90.6 70.1 200 Methyl 2-methylbutyrate ECETOC (1995) NC –68.4 116.2 201 Methyl caproate ECETOC (1995) NC –44.6 130.2 202 Methyl laurate ECETOC (1995) NC 23.2 214.4 203 Methyl lavender ketone (1-hydroxy-3-decanone) ECETOC (1995) NC 42.7 172.3 204 Methyl linoleate ECETOC (1995) NC 70.8 294.5 205 Methyl palmitate ECETOC (1995) NC 63.2 270.5 206 Methyl stearate ECETOC (1995) NC 81.6 298.5 207 Methyl trimethyl acetate ECETOC (1995) NC –62.5 116.2 208 Decylidene methyl anthranilate ECETOC (1995) NC 99.9 289.4 209 N,N-Dimethylbenzylamine ECETOC (1995) NC –12.8 135.2 210 Nonanal ECETOC (1995) NC –19.5 142.2 211 Octanoic acid ECETOC (1995) C 48.4 144.2 212 Oleyl propylene diamine dioleate ECETOC (1995) NC 142.1 324.6 213 Phenethyl bromide ECETOC (1995) NC 2.5 185.1 214 4-Isopropylphenylacetaldehyde ECETOC (1995) NC 18.4 162.2 215 4-Mentha-1,8-dien-7-ol ECETOC (1995) NC 11.1 152.2 216 4-tert-Butyl dihydrocinnamaldehyde ECETOC (1995) NC 46.3 190.3 217 Salicylaldehyde ECETOC (1995) NC 42.6 122.1 218 Tetrachloroethylene ECETOC (1995) NC –60.6 165.8 219 Tetrahydrogeranial ECETOC (1995) NC –30.0 156.3 220 Tonalid ECETOC (1995) NC 98.7 244.4 221 Trichloroethylene ECETOC (1995) NC –60.6 165.8 222 1-(2-Aminoethyl)piperazine NIH (1999) C 53.7 129.2 223 1,2-Diaminopropane NIH (1999) C –22.9 74.1 224 1,4-Diaminobutane NIH (1999) C 0.9 88.2 225 2,3-Dimethylcyclohexylamine NIH (1999) C –11.1 127.2 226 2-Ethylhexylamine NIH (1999) C –21.0 129.3 227 2-Mercaptoethanol NIH (1999) C –45.6 78.1 228 3-Diethylaminopropylamine NIH (1999) C 0.7 130.2 229 Acetic acid NIH (1999) C –21.3 60.1 © 2004 by CRC Press LLC The following physicochemical properties, which were considered to be possible predictors of acute skin toxicity, were calculated for the 277 chemicals in Table 18.3: 1. Molecular weight (MW), surface area (MSA), and volume (MV) 2. Log K ow 3. Melting point (MP) Table 18.3 (continued) Skin Corrosion Data for 277 Organic Chemicals Chemical Source C/NC MP MW 230 Acetic anhydride NIH (1999) C –95.1 102.1 231 Acetyl bromide NIH (1999) C –53.0 123.0 232 Benzene sulphonyl chloride NIH (1999) C 61.2 176.6 233 Benzyl chloroformate NIH (1999) C 11.6 170.6 234 Bromoacetic acid NIH (1999) C 29.2 139.0 235 Bromoacetyl bromide NIH (1999) C –1.7 201.9 236 Butanoic acid NIH (1999) C 3.0 88.1 237 Butylamine NIH (1999) C –58.8 73.1 238 Butylbenzene NIH (1999) NC –23.3 134.2 239 Butyric anhydride NIH (1999) C –44.6 158.2 240 Chloroacetic acid NIH (1999) C 10.9 94.5 241 Crotonic acid NIH (1999) C 2.4 86.1 242 Cyanuric chloride NIH (1999) C 68.8 184.4 243 Cyclohexylamine NIH (1999) C –27.1 99.2 244 Dichloroacetic acid NIH (1999) C 24.2 128.9 245 Dichloroacetyl chloride NIH (1999) C –32.5 147.4 246 Dichlorophenyl phosphine NIH (1999) C –4.9 179.0 247 Dicyclohexylamine NIH (1999) C 27.7 181.3 248 Diethylamine NIH (1999) C –79.7 73.1 249 Diethylene triamine NIH (1999) C 17.8 103.2 250 Dimethylcarbamyl chloride NIH (1999) C -15.9 107.5 251 Dodecyl trichlorosilane NIH (1999) C 51.0 303.8 252 Ethanolamine NIH (1999) C –27.6 61.1 253 Ethylene diamine NIH (1999) C –23.8 60.1 254 Formic acid NIH (1999) C –25.0 46.0 255 Fumaryl chloride NIH (1999) C 6.8 153.0 256 Hexanoic acid NIH (1999) C 26.2 116.2 257 Hexanol NIH (1999) NC –37.9 102.2 258 Maleic acid NIH (1999) NC 84.1 116.1 259 Maleic anhydride NIH (1999) C –51.6 98.1 260 Mercaptoacetic acid NIH (1999) C 18.8 92.1 261 Nonanol NIH (1999) NC –3.2 144.3 262 2-Anisoyl chloride NIH (1999) C 36.7 170.6 263 Octadecyl trichlorosilane NIH (1999) C 107.7 387.9 264 Octyl trichlorosilane NIH (1999) C 8.1 247.7 265 Pentanoyl (valeryl) chloride NIH (1999) C –42.4 120.6 266 Phenyl acetyl chloride NIH (1999) C 13.7 154.6 267 Phenyl trichlorosilane NIH (1999) C 5.8 211.6 268 Propanoic acid NIH (1999) C –9.0 74.1 269 Pyrrolidine NIH (1999) C –36.0 71.1 270 Tetraethylenepentamine NIH (1999) C 112.7 189.3 271 Tributylamine NIH (1999) NC 0.8 185.4 272 Trichloroacetic acid NIH (1999) C 26.7 163.4 273 Trichlorotoluene NIH (1999) NC 10.4 195.5 274 Triethanolamine NIH (1999) NC 83.3 149.2 275 Triethylene tetramine NIH (1999) C 68.2 146.2 276 Trifluoroacetic acid NIH (1999) C –24.0 114.0 277 Undecanol NIH (1999) NC 18.7 172.3 Note: C = corrosive; MP = melting point (rC); MW = molecular weight (g/mol); NC = non-corrosive. © 2004 by CRC Press LLC [...]... NC C 15. 49 63.78 126.04 2.26 1 05. 75 140.42 98.31 96. 05 143.83 117.38 1 15. 65 106.41 50 .87 117.84 79.23 112.89 78.22 64.19 104 .55 91.04 44.42 102.89 93.16 1 15. 11 94. 95 103.39 104.63 3 .55 108.83 120.08 132.81 43.34 134.39 107 .55 98.63 44. 05 122.64 94.44 1 35. 96 80.19 112.49 101.39 73 .57 139. 35 97.22 129.99 108. 75 95. 55 136.42 122.38 110.30 101.19 101.44 116.31 117.32 2.30 49.76 16.17 3.79 36.21 148 .54 108.10... 70 .56 13.99 11.11 55 .96 33.24 9. 85 77.09 8.17 88.39 2.12 108.30 99.06 11.87 4. 25 103.29 55 .02 3.28 161.64 59 .56 104.20 3.82 12.22 113.97 178.77 3.17 136.18 101.49 3 .55 151 .71 348 .54 64.14 5. 12 3.98 49.02 4.67 1 15. 60 102.33 2 .53 107.76 53 .89 Table 18 .5 (continued) EPISKIN Data for the 60 Chemicals Tested in the ECVAM Skin Corrosivity Study Classification Chemical 56 57 58 59 60 EPISKIN 3 min EPISKIN 1h... 16.27 54 .50 106.72 121.01 41.83 28.23 38.39 96.97 22.30 15. 30 91.00 66.28 12.11 117.33 8.09 112.61 22.08 94.21 104 .59 14.34 6.16 99.06 121.21 15. 25 176.61 104.67 101.41 3 .58 34.37 110.84 200.93 4.20 116.04 104.44 3.29 158 .09 355 .39 118.92 7.01 3.80 91.06 7.72 101 .50 109.76 25. 02 103.18 88.34 2.64 25. 36 16.28 3.43 21.07 156 .59 99.79 89.67 8.87 39.40 69.14 112.69 17.60 22.46 17.07 70 .56 13.99 11.11 55 .96... 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 Hexanoic acid 1,2-Diaminopropane Carvacrol Boron trifluoride dihydrate Methacrolein Phenethyl bromide 3,3 -Dithiodipropionic acid Isopropanol 2-Methoxyphenol (Guaiacol) 2,4-Xylidine (2,4-Dimethylaniline) 2-Phenylethanol (phenylethylalcohol) Dodecanoic (lauric) acid 3-Methoxypropylamine Allyl bromide Dimethyldipropylenetriamine... chloride Potassium hydroxide (5% aq.) Butyl propanoate 2-tert-Butylphenol Sodium carbonate (50 % aq.) Sulfuric acid (10% wt.) Isostearic acid Methyl palmitate Phosphorus tribromide 65/ 35 Octanoic/decanoic acids 4,4 -Methylene-bis-(2,6-ditert-butylphenol) 2-Bromobutane Phosphorus pentachloride 4-( Methylthio)-benzaldehyde 70/30 Oleine/octanoic acid Hydrogenated tallow amine 2-Methylbutyric acid Sodium undecylenate... bisulphite 1-( 2-Aminoethyl)piperazine 1,9-Decadiene Phosphoric acid 10-Undecenoic acid 4-Amino-1,2,4-triazole Known (in vivo) Classification pH Predicted Classification 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 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... Skin Corrosion Classifications and pH Data for 44 Chemicals Chemical 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 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... 66 85 70 88 88 68 62 88 75 85 79 69 64 86 73 86 83 32 38 12 25 15 21 30 34 15 30 12 12 Statistics based on the application of CM 18.1 to its training set of 189 organic liquids Cross-validated statistics based on the three-fold cross-validation of CM 18.1 Statistics based on the application of CM (18.2) to its training set of 44 chemicals Cross-validated statistics based on the the three-fold cross-validation... Skin Corrosion Chemical 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 45 46 47 48 49 50 51 Hexanoic acid 1,2-Diaminopropane Carvacrol Boron trifluoride dehydrate Methacrolein Phenethyl bromide 3,3 -Dithiodipropionic acid Isopropanol 2-Methoxyphenol (Guaiacol) 2,4-Xylidine (2,4-Dimethylaniline) 2-Phenylethanol (phenylethylalcohol)... (lauric) acid 3-Methoxypropylamine Allyl bromide Dimethyldipropylenetriamine Methyl trimethylacetate Dimethylisopropylamine Tetrachloroethylene Ferric (iron [III]) chloride Butyl propanoate 2-tert-Butylphenol Isostearic acid Methyl palmitate Phosphorus tribromide 65/ 35 Octanoic/decanoic acids 4,4 -Methylene-bis-(2,6-ditert-butylphenol) 2-Bromobutane Phosphorus pentachloride 4-( Methylthio)-benzaldehyde . NC 53 .8 151 .1 95 1-Decanol ECETOC (19 95) NC 7.9 158 .3 96 1-Formyl-1-methyl-4(4-methyl-3-penten-1-yl )-3 -cyclohexane ECETOC (19 95) NC 46 .5 208.4 97 2,3-Dichloroproprionitrile ECETOC (19 95) NC –21.2. (19 95) NC 108.9 197.2 102 2,4-Hexadienal ECETOC (19 95) NC 56 .2 96.1 103 2,4-Xylidine ECETOC (19 95) NC 34.7 1 35. 2 104 2 , 5- Methylene-6-propyl-3-cyclo-hexen-carbaldehyde ECETOC (19 95) NC 15. 2 164.3 1 05. 1-Bromo-4-chlorobutane ECETOC (19 95) NC –33.6 171 .5 92 1-Bromo- 4- uorobenzene ECETOC (19 95) NC –19.1 1 75. 0 93 1-Bromohexane ECETOC (19 95) NC –41.6 1 65. 1 94 1-Bromopentane ECETOC (19 95) NC 53 .8