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Many therapeutic drugs induce phototoxic skin responses following exposure to solar or artificial ultraviolet radiation sources. Several in vitro model systems have been developed to predict drug phototoxicity but none have been conducted in parallel with controlled clinical phototoxicity studies on systemically administered pharmaceuticals. The in vitro phototoxicity of eight fluoroquinolone (FQ) antibiotics (ciprofloxacin, grepafloxacin, lomefloxacin, norfloxacin, ofloxacin, trovafloxacin, BAYy3118, moxifloxacin) was determined by exposing Chinese hamster fibroblasts to UVA radiation. Cell damage was quantified with standard MTT or neutral red assays and an in vitro phototoxic index calculated (PI(vit)=% cell viability with UVA alone /% cell viability with UVA+FQ) for each endpoint. Clinical photosensitizing ability of the eight systemically administered FQ was investigated using double-blind, placebo and positive controlled, clinical skin phototesting of normal subjects. Minimal erythema doses at 365+/-30nm were determined before and after 6-7 days of FQ ingestion and PI(clin) (minimal erythema dose without FQ/minimal erythema dose with FQ) calculated. Linear regression analysis of PI(vit) vs PI(clin) gave correlations of up to 0.893. Principal components analysis of PI(vit), daily dose, plasma levels and photophysical (absorption) properties of the eight FQ showed that phototoxic (arbitrarily defined as PI(clin)> or =2) and non-phototoxic (PI(clin)<2) FQ could be completely discriminated using these parameters, and that the in vitro models were able to rank the relative phototoxic potential of the eight FQ.

A QSAR model for the eye irritation of cationic surfactants has been constructed using a dataset consisting of the maximum average scores (MAS-accordance to Draize) for 29 in vivo rabbit eye irritation tests on 19 different cationic surfactants. The parameters used were logP (log [octanol/water partition coefficient]) and molecular volume (to model the partition of the surfactants into the membranes of the eye), logCMC (log critical micelle concentration-a measure of the reactivity of the surfactants with the eye) together with surfactant concentration. The model was constructed using neural network analysis. MAS showed strongly positive, non-linear correlations with surfactant concentration and logCMC and a strongly negative, non-linear correlation with logP. The Pearson correlation between the actual and predicted values of MAS was 0.838 showing that around 70% (r(2)=0.702) of the variance in the dataset is explained by the model. This value is consistent with levels of biological variability reported historically for the Draize rabbit eye test. The relationship provides a potentially useful prediction model for the eye irritation potential of new or untested cationic surfactants with physicochemical properties lying within the parameter space of the model.

An international validation study on in vitro tests for skin corrosivity was conducted during 1996 and 1997 under the auspices of the European Centre for the Validation of Alternative Methods (ECVAM). The main objectives of the study were to assess the performances of selected in vitro tests in discriminating between: (a) corrosives (C) and non-corrosives (NC), for selected groups of chemicals (e.g. organic acids, phenols) and/or for all chemicals (single chemical entities only); and (b) known R35 (UN packing group I) and R34 (UN packing groups II & III) chemicals. Each test was evaluated for reliability and relevance by using a test set of 60 coded chemicals. In this paper, the test chemicals used in the validation study are identified; they include organic acids (6C/5NC), organic bases (7C/3NC), neutral organics (9NC), phenols (2C/3NC), inorganic acids (6C/1NC), inorganic bases (2C/2NC), inorganic salts (1C/2NC), electrophiles (3C/5NC) and soaps/surfactants (3NC). The in vivo classifications and important physicochemical properties (e.g. logP, pKa) of the test chemicals are given. The main criterion for including chemicals in the test set was that their corrosivity classifications were based on unequivocal animal data. Where available, structure-activity information was also used to support the corrosivity classifications. Despite the small numbers of chemicals in some of the categories, it was felt that the test set chosen represented the best possible for evaluating the performances of the in vitro tests for predicting skin corrosivity, given the limited availability of unequivocal animal data. The prediction of skin corrosivity from pH data was also investigated for those chemicals with extreme pH values (i.e. pH2 or 11.5). Nine of the 12 strongly acidic or alkaline chemicals in the test set, which were predicted to be C on the basis of their pH values, had also been found to be C in vivo.

A quantitative structure-activity relationship (QSAR) was derived previously relating European Community (EC) eye irritation classification data of a set of neutral organic chemicals, to log(octanol/water partition coefficient), to the minor principal inertial axes (Ry and Rz) and to dipole moment. Eye irritation scores on a scale of 1-10 for a set of aliphatic alcohols (from the work of Smyth and Carpenter) have been shown to correlate well with the same four physicochemical parameters by means of neural network analysis. The original classification dataset of neutral organic chemicals has been augmented by the addition of a number of the aliphatic alcohols from the Smyth and Carpenter data that could unequivocally be assigned the EC classifications of irritant (those with eye irritation scores of 8 and 9) or non-irritant (scores of 1). Analysis of the extended dataset by both principal components and neural network analysis showed a clear discrimination between irritant and non-irritant chemicals using the same four physicochemical parameters. Predictions of EC eye irritation classifications for aliphatic alcohols with eye scores of 2-7, using the neural network model, showed that alcohols with eye scores of 2 and 3 lie on the classification boundary between irritant and non-irritant whereas those with scores of 4 and above are classified as irritant. These analyses support the validity of the original four-parameter eye irritation QSAR model for neutral organic chemicals. Furthermore, they provide a method for interrelating sets of in vivo data in which the biological response parameters are expressed in quite different formats, providing a means of utilizing historical data and thereby extending the availability of in vivo data suitable for the validation of in vitro alternative methods.

Quantitative structure-activity relationships (QSAR) methods have been derived that relate the severity of skin corrosivity (designated by the EC risk phrases R34 and R35) of acids to parameters that model their skin permeability and cytotoxicity. Skin permeability was modelled by log(octanol/water partition coefficient), molecular volume and melting point, while the cytotoxicity of the acids was accounted for by their pK(a), values and the in vitro cytotoxicity of their sodium salts towards Swiss mouse embryo 3T3 cells. The dataset was analysed using principal components and neural network analysis. The classification predictions from both QSAR methods were in agreement with those in the training set for 26 of the 27 acids. The methods provide useful procedures for the prediction of the skin corrosivity potentials of severely corrosive acids, which avoid the use of experimental animals and demonstrate the value of in vitro cytotoxicity parameters as inputs for QSAR analysis.

Quantitative structure-activity relationships (QSARs) have been derived by relating skin irritation and corrosivity data of neutral and electrophilic organic chemicals to their log(octanol/water partition coefficient) (logP), molecular volume, dipole moment and 1/molecular weight. Datasets were analysed using stepwise regression, discriminant and principal components analysis. Discriminant analysis between irritant and non-irritant neutral and electrophilic organic chemicals using the above parameters, which broadly model skin permeability (logP and molecular volume), 'reactivity' (dipole moment) and l/molecular weight to compensate for the fact that skin irritation/corrosivity testing is carried out using a fixed mass or volume of chemical, was found to discriminate well for only 73.1% of the dataset (67.3% cross-validated). The poor discrimination at the irritant/non-irritant classification boundary is attributed largely to biological variability. Stepwise regression analysis of the Primary Irritation Index (PII) for the same dataset showed a poor correlation (r(2) = 0.422; cross-validated r(2) = 0.201) with a positive dependence on logP and dipole moment and a negative dependence on molecular volume; l/molecular weight was not a significant variable. While this QSAR for PII has little value as a predictive model, mainly because of the large biological variability evident in PII values, it is useful in confirming the putative model for skin irritation. Discriminant analysis using logP, molecular volume and dipole moment, was able to discriminate reasonably well (92.9% well-classified; 92.9% cross-validated) between corrosive and non-corrosive electrophiles. A plot of the first two principal components of the same parameters showed a clear demarcation between corrosive and non-corrosive electrophiles. In contrast to the QSARs for skin irritation, increasing skin corrosivity was found to correlate with decreasing molecular volume, with increasing dipole moment, and with decreasing logP. The predominant parameter in determining the skin corrosivity of electrophilic organic chemicals appears to be the molar dose at which they are tested; this arises because skin corrosivity testing is conducted using a fixed mass or volume of chemical. A stepwise approach to the skin corrosivity/irritation classification of neutral and electrophilic organic chemicals is outlined. The derived QSARs should be useful for the prediction of the skin corrosivity potential of new or untested electrophiles. (Non-electrophilic neutral organic chemicals, as a category, do not generally appear to be corrosive.) Discrimination between some non-irritant and irritant neutral and electrophilic organic chemicals using these techniques is also possible. For a large number of chemicals whose irritation potentials lie in a fairly broad band around the irritant/non-irritant classification boundary, no firm prediction of classification is possible.

Iodide uptake (IU) by thyrocytes from the plasma against chemical and electrical gradients is by a specific iodide transporter or 'pump'. Perchlorate (ClO(4)(-)) and other univalent, symmetrical anions are competitive inhibitors of iodide uptake (IU), and apparent K(i) for individual anions can be correlated with ion size. This study uses cultured thyrocytes and a broad range of anion size, in particular a series of spherical hexafluoride ions, in order to understand more about the parameters governing the activity of competitive inhibitors of IU. (125)I uptake and organification by cultured porcine thyrocytes was combined with biochemical enzyme inhibition studies on thyroid peroxidase in order to identify specific effects on IU. Known inhibitors were used in a validation phase and demonstrated that a combination of the in vitro thyrocyte (125)I assay and thyroid peroxidase inhibition assay could be used to identify selective inhibitors that can be difficult to identify using thyrocytes alone. Anions of less than, or similar, volume to I(-) (35.0 A(3)) were weak inhibitors with potency increasing proportional to ion size up to an apparent maximum for AsF(6)(-) (94.45 A(3)); this correlation was strong (r = 0.96). PF(6)(-), AsF(6)(-) and SbF(6)(-) were identified as novel inhibitors of IU, showing that the size range of anions active in IU inhibition is greater than that previously identified. The biological significance in vivo of the inhibitory action of the hexafluoride ions is not known. Their potency in this study suggests that these anions may have the potential to affect thyroid function in vivo if they were available systemically.

Quantitative structure-activity relationships (QSARs) relating skin corrosivity data of organic acids, bases and phenols to their log(octanol/water partition coefficient), molecular volume, melting point and pK(a). have been extended to substantially larger datasets. In addition to principal components analysis, as used in earlier work, the datasets have also been analysed using neural networks. Plots of the first two principal components of the four independent variables, which broadly model skin permeability and cytotoxicity, for each of the extended datasets confirmed that the analysis was able to discriminate well between corrosive and non-corrosive chemicals. Neural networks using the same parameters as inputs, were trained to an output in the range 0.0 to 1.0, with non-corrosive chemicals being assigned the value 0 and corrosive chemicals the value 1. As well as yielding classification predictions in agreement with those in the training sets, predicted outputs in the 0 to 1 range gave a useful indication of the confidence of the predicted classification. These QSARs are useful (a) for the prediction of the skin corrosivity potentials of new or untested chemicals and (b) for determining the confidence of predictions in regions of 'biological uncertainty' which exist at the classification threshold between corrosive and non-corrosive chemicals.

The corrosive potential of a series of fatty acids-propanoic acid (C3), butanoic acid (C4), hexanoic acid (C6), octanoic acid (C8), decanoic acid (C10) and dodecanoic acid (C12)-was investigated in the in vitro skin corrosivity test (IVSCT) using both rat skin and human skin. All the fatty acids with alkyl chain lengths up to and including C8 were found to be corrosive to rat skin. When human skin was used, the corrosive/non-corrosive threshold was shifted to around the C6 fatty acid. The results are discussed in the context of a QSAR for the corrosivity of organic acids, with the putative mechanism that corrosivity is a function of the ability of the chemical to permeate the skin together with its cytotoxicity, expressed in this case as acidity (pK(a)). This mechanistic interpretation is consistent with the known differences in barrier properties between rat and human skin.

Mechanistically based quantitative structure-activity relationships (QSARs) have been developed for some well defined toxic endpoints. Principal components analysis has proved to be a useful technique in visualizing the QSARs and allows ready construction of hypotheses to test. The preferred way of testing the hypotheses is to use in vitro methods: thus integrating the use of QSAR and in vitro methods. Principal components analysis affords a basis for more rational selection of test chemicals to examine the utility of an in vitro method and more specifically the selection of test chemicals to be used in the validation of an in vitro method. This is particularly important in the absence of a large number of chemicals which have been tested in animals. In addition, elucidation of the putative mechanism of action of chemicals allows re-evaluation of the appropriateness of an in vitro method for the precise detection of the toxicity of specific classes of substances and more rational design of in vitro methods with improved powers of prediction.

In vitro human skin permeability coefficient data collected by Flynn (1990) have been analysed using multiple regression analysis. An improved model for the prediction of permeability coefficients has been derived by the inclusion of the melting point as an independent variable in addition to the octanol-water partition coefficient (as logP) and molecular volume. (The solubility of chemicals in water is related to logP and melting point through an algorithm; Suzuki, Journal of Computer-Aided Molecular Design 1991, 5, 149-166.) Examination of the dataset using principal components analysis confirmed that it could be divided into three distinct groups of chemicals-steroids, other pharmacologically active molecules and small molecules. Regression analysis of the individual groups revealed a very high level of correlation with the model for the steroids and small molecules, but a poor correlation for the pharmacologically active molecules. A sub-set of hydrocortisone derivatives within the steroid group had measured permeability coefficients which were around 1.5 orders of magnitude greater than values predicted from the small molecule model. By grouping together the small molecules and the steroids (excluding the hydrocortisone set)-60 molecules-a quantitative structure-activity relationship (QSAR) has been derived for their permeability coefficients which is dependent on logP, molecular volume and melting point, and which explains more than 90% of the variability in the data. Although yet to be tested experimentally, this QSAR is expected to give accurate predictions of in vitro human skin permeability coefficients within the constraints of the dataset.

There are currently no in vitro methods suitable for the prospective identification of skin sensitizers (contact allergens). Knowledge relating chemical structure to toxicity can be programmed into expert systems. An historical database, containing results of 294 defined single substances tested in the guinea pig maximization test (GPMT) carried out according to a single protocol, has been used to derive a set of structural alerts for skin sensitization. Where possible, the approach used was to group the substances according to their most likely mechanism of reaction with skin proteins. Where no mechanism could be identified, structural alerts were derived empirically for groups of molecules with similar chemical functionality. This process has currently resulted in the production of 40 structure-activity rules which have been incorporated into the expert system DEREK. This system forms an integral part of a strategic approach to the identification of contact allergens.

There are currently no in vitro methods for the identification of skin sensitizers (contact allergens). Knowledge relating chemical structure to toxicity can be programmed into expert systems. An historical database containing results of 294 defined single substances tested in the guinea pig maximization test to a single protocol has been used to derive a set of structural alerts for skin sensitization. Where possible, the approach used was to group the substances according to their most likely mechanism of reaction with skin proteins. Where no mechanism could be identified, structural alerts were derived empirically for groups of molecules with similar chemical functionality. This process has currently resulted in the production of 40 structure-activity rules, which have been incorporated into the expert system DEREK. Rulebases of this type have potential for use as a preliminary screen in toxicological hazard identification and may ultimately lead to a reduction in the use of laboratory animals.

A number of chemicals induce a toxic syndrome in male rats-referred to as alpha(2mu)-globulin nephropathy-that is characterized by an accumulation of the urinary protein alpha(2mu)-globulin in renal lysosomes, subsequent cytotoxicity and cell death. Binding affinity to alpha(2mu)-globulin has been identified as one of the determinants for alpha(2mu)-globulin nephropathy. A quantitative structure-activity relationship (QSAR) has been derived by multiple regression analysis relating this binding to negative charge density of the binding molecule and its molecular volume. Previous data, correlating aliphatic and alicyclic hydrocarbon structures with ability to induce renal lysosome accumulation in male rats, were analysed by the technique of principal components analysis applied to the molecular volumes and principal inertial axes of alcohols predicted to be derived from the hydrocarbons. Mapping of the first and second principal components showed clustering of molecules relative to their biological activity. A combination of the two QSARs is useful in identifying molecules with a potential to cause alpha(2mu)-globulin nephropathy.

The key to determining whether a chemical has the ability to behave as a contact allergen must reside ultimately in the structure and properties of that chemical rather than in the immune system. Quantitative structure-activity relationships (QSARs) have been demonstrated for a small number of series of chemicals in the past, using the relative alkylation index model based on this principle. In the present work, a carefully chosen range of phenyl benzoate esters has been synthesized such that they would have a single mechanism of action but otherwise would span important areas of parameter space-reactivity, skin penetration characteristics and biological response. Computer-based methods of analysis were used to generate a QSAR. The model showed that molecular volume and particularly the calculated logarithm of the partition coefficient (ClogP) were key parameters. Surprisingly, while chemical reactivity is a requirement for skin sensitization, it was not found to be an important variable in the QSAR. In conclusion, the study confirms that within a series of related chemicals it is possible to derive a useful QSAR.

An in vitro screening procedure is described whereby potential photoallergens are irradiated with ultraviolet (UV) light in the presence of monomeric human serum albumin. UV spectroscopy before and after irradiation and after passage of the reaction mixture through Sephadex G-10, is used to determine whether or not the test compound has bound to the albumin. Using this method and others employing radiolabelled compounds, nine photoallergens have been shown to bind to protein under the influence of UV light. In contrast, a cinnamate sunscreen, which absorbs UV light but is not a photoallergen, does not bind under these conditions. The method is proposed as an in vitro screening procedure for potential photoallergens.