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Development of new QSAR approaches in occupational contact dermatitis.
Fedorowicz-A; Afshari-H; Zheng-L; Singh-H; Demchuk-E
Proceedings of the International Conference on Occupational and Environmental Exposures of Skin to Chemicals: Science & Policy, Arlington, VA, September 8-11, 2002. Morgantown, WV: National Institute for Occupational Safety and Health, 2002 Sep; :1-3
The Bureau of Labor Statistics estimates that occupational skin diseases constitute the second largest group of occupational injuries in the U.S.. 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 long-lasting memory of the immune system. ACD usually develops as a result of repetitive skin exposures to a sensitizing chemical agent. Usually at least a single excessive exposure is essential in the development of the immune response. A variety of experimental tests have been suggested to assess the skin sensitization potential of a chemical. Providing information that leads to the development of recommended skin exposure limits that would prevent workers from sensitizing overexposures may be an important factor in predictive testing. Unfortunately, many experimental protocols result in a dichotomous conclusion, more appropriate for denial/acceptance decision-making in chemical manufacturing rather than for protection of workers occupationally involved with sensitizing chemical agents. Essentially only one of them, the murine Local Lymph Node Assay (LLNA), has the capacity to provide a reliable 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 physical-chemical properties, called molecular descriptors, of many chemical compounds with known biological activity, in this work the sensitization potential, and then relating a few of the informative descriptors to the target bioactivity. The structure-activity relationships constructed this way provide a means of investigating and predicting the toxicological effect of a chemical with yet unknown sensitization potential. We rely on LLNA data to quantify the skin sensitization potential. At present the LLNA data are (1) outnumbered by the long history of guinea pig assays, and (2) often reported as a dichotomous scale congruous to the guinea pig data. Therefore, the work has been started using the dichotomous LLNA data 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 design of appropriate software. Our immediate goal is to identify a pool of potentially informative molecular descriptors and chemical classes that are most appropriate for QSAR modeling to predict LLNA results. In the present work a QSAR based on a generalized linear model of 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 are significantly associated with LLNA data on skin sensitization, 1203 molecular descriptors were calculated and tested for their significance in predicting the skin sensitization potential. Only a small number of molecular descriptors were found to be statistically significantly associated with skin sensitization. At this stage we were able to define a statistically significant QSAR on 54 selected compounds, which includes three molecular descriptors. These specialized descriptors from computational chemistry are (1) the number of double bonds, nDB, (2) mass-weighted Geary graph autocorrelation coefficient of the sixth lag, GATS6m, and (3) the spatial first component accessibility directional unweighted holistic invariant molecular descriptor index, E1u. A QSAR model built on only these three descriptors using logistic regression is successful as shown by the chi-square goodness of fit test, indicating the suitability of these descriptors to predict the sensitization potential. We are in the process of refining these results by: (1) the investigation of the probability of correct classification of a compound using the fitted logistic regression based on these three descriptors and (2) the study of interrelationships between various descriptors and their effect on the fitting of the logistic regression. These results suggest that a comprehensive QSAR model of ACD may be built by using only few appropriate parameters, although the relevance of identified descriptors to the continuous-scale ACD QSAR has yet to be shown. Further work will be focused on populating the QSAR database with continuous-scale ACD data and molecular descriptors that are obtained in the present study. New predictive QSARs are expected to be useful in screening large sets of compounds for their potential impact on skin sensitization, and thus may suggest a useful order of priorities in experimental testing.
Occupational-hazards; Occupational-exposure; Occupational-health; Statistical-analysis; Skin-disorders; Skin-exposure; Skin-irritants; Sensitization; Dermatitis; Dermatosis; Quantitative-analysis; Mathematical-models; Computer-models
Proceedings of the International Conference on Occupational and Environmental Exposures of Skin to Chemicals: Science & Policy, Arlington, VA, September 8-11, 2002
Page last reviewed: March 11, 2019
Content source: National Institute for Occupational Safety and Health Education and Information Division