QSAR Study of Skin Sensitization Using Local Lymph Node Assay Data

Int. J. Mol. Sci. 2004, 5, 56-66 International Journal of Molecular Sciences ISSN 1422-0067 © 2004 by MDPI www.mdpi.org/ijms/ QSAR Study of Skin Sensitization Using Local Lymph Node Assay Data Adam Fedorowicz,1 Lingyi Zheng,2 Harshinder Singh1,2 and Eugene Demchuk1,3 1 National Institute for Occupational Safety and Health, Morgantown, WV. E-mail: Department of Statistics, West Virginia University, Morgantown, WV. 3 School of Pharmacy, West Virginia University, Morgantown, WV. 2 Received: 28 April 2003 / Accepted: 18 July 2003 / Published: 30 January 2004 Abstract: Allergic Contact Dermatitis (ACD) is a common work-related skin disease that often develops as a result of repetitive skin exposures to a sensitizing chemical agent. A variety of experimental tests have been suggested to assess the skin sensitization potential. We applied a method of Quantitative Structure-Activity Relationship (QSAR) to relate measured and calculated physical-chemical properties of chemical compounds to their sensitization potential. Using statistical methods, each of these properties, called molecular descriptors, was tested for its propensity to predict the sensitization potential. A few of the most informative descriptors were subsequently selected to build a model of skin sensitization. In this work sensitization data for the murine Local Lymph Node Assay (LLNA) were used. In principle, LLNA provides a standardized continuous scale suitable for quantitative assessment of skin sensitization. However, at present many LLNA results are still reported on a dichotomous scale, which is consistent with the scale of guinea pig tests, which were widely used in past years. Therefore, in this study only a dichotomous version of the LLNA data was used. To the statistical end, we relied on the logistic regression approach. This approach provides a statistical tool for investigating and predicting skin sensitization that is expressed only in categorical terms of activity and nonactivity. Based on the data of compounds used in this study, our results suggest a QSAR model of ACD that is based on the following descriptors: nDB (number of double bonds), C-003 (number of CHR3 molecular subfragments), GATS6M (autocorrelation coefficient) and HATS6m (GETAWAY descriptor), although the relevance of the identified descriptors to the continuous ACD QSAR has yet to be shown. The proposed QSAR model gives a percentage of positively predicted responses of 83% on the training set of compounds, and in cross validation it correctly identifies 79% of responses. Keywords: ACD, LLNA, binary QSAR, logistic regression, skin sensitization. Int. J. Mol. Sci. 2004, 5 57 Introduction The Bureau of Labor Statistics estimates that occupational skin diseases constitute the second largest group of occupational injuries in the U.S. [1]. Among them, Occupational Contact Dermatitis (OCD) is the most common cause of work-related skin illness comprising up to 95% of registered cases. Allergic Contact Dermatitis (ACD) may lead to severe recurrent forms of OCD because of longlasting memory of the immune system. ACD, which is an adaptive, T-cell mediated immune response [2], usually develops as a result of repetitive skin exposures to a sensitizing chemical agent. At least a single excessive exposure is essential in the development of the immune response. Information that leads to the development of recommended skin exposure limits that would prevent workers from sensitizing overexposures is an important factor impacting public health. A variety of experimental tests have been suggested to assess the skin sensitization potential of a chemical [3]. Unfortunately, many experimental protocols result in a dichotomous conclusion, more appropriate for denial/acceptance decision-making in design and manufacturing of new chemicals rather than for preventive protection of workers occupationally involved with sensitizing chemical agents. The murine Local Lymph Node Assay (LLNA) has the capacity to provide dose response data that can be used as a standardized continuous scale in the quantitative assessment of skin sensitization. A combination of methods in statistics and computational chemistry, commonly referred to as Quantitative Structure-Activity Relationship (QSAR) modeling, complements the experimental approach. A method of QSAR is based on the examination of measured and calculated molecular descriptors, with known biological activity, in this work the sensitization potential, and then relating a few of the most informative descriptors to the target bioactivity. The structure-activity relationships constructed this way provide a means of investigating and predicting the sensitization potential of the chemicals. We rely on LLNA data to quantify the skin sensitization potential [4]. At present, the LLNA data are (1) outnumbered by the long history of guinea pig assays, and (2) often reported as dichotomous and congruous to the guinea pig data. Therefore, the work has been started using LLNA data in a dichotomous format to identify molecular descriptors that may be effective in the continuous-scale LLNA QSAR. The work began from building a database of chemical names, structures, properties and bioactivities, along with the design of appropriate software. Our immediate goal is to identify a pool of potentially informative molecular descriptor classes that are most appropriate for QSAR modeling to predict skin sensitization potential. In the present work, a QSAR based on a logistic regression is proposed. The logistic regression permits construction of standard QSAR equations, in which the activity data are represented only in terms of activity (1) or non-activity (0) values. In order to evaluate molecular properties, which can be associated with LLNA data on skin sensitization, 1204 molecular descriptors were calculated and tested for their significance in predicting the skin sensitization potential. Only a limited number of molecular descriptors were found to be statistically associated with skin sensitization. 58 Int. J. Mol. Sci. 2004, 5 Materials and Methods In the present study, a pool of 54 LLNA-tested compounds was used, of which 25 were sensitizers and 29 were negative controls [5, 6]. The molecular structures of these compounds were first encoded using the SMILES notation and subsequently transformed into three-dimensional co-ordinates using Cerius2 from Accelrys, Inc (Accelrys, San Diego, USA, http://www.accelrys.com/cerius2). The Dragon 2.1 software developed by Milano Chemometrics and QSAR Research Group was used to calculate a total of 1204 molecular descriptors (http://www.disat.unimib.it/chm/Dragon.htm), for each of the studied compounds. The statistical analysis was carried out using the SAS 8.2 statistical package [7]. The linear probability model is inadequate for modeling the probability of positive LLNA sensitization response, since it is heteroscedastic and often leads to uninterpretable results. The logistic r (...truncated)