Nancy B. Sussman

Development, Characterization and Application of Predictive-toxicology Models

The adoption of SAR techniques for risk assessment purposes requires that the predictive performance of models be characterized and optimized. The development of such methods with respect to CASE/MULTICASE are described. Moreover, the effects of size, informational content, ratio of actives/inactives in the model on predictivity must be determined. Characterized models can provide mechanistic insights: nature of toxicophore, reactivity, receptor binding. Comparison of toxicophores among SAR models allows a determination of mechanistic overlaps (e.g., mutagenicity, toxicity, inhibition of gap junctional intercellular communication vs. carcinogenicity). Methods have been developed to combine SAR submodels and thereby improve predictive performance. Now that predictive toxicology methods are gaining acceptance, the development of Good Laboratory Practices is a further priority, as is the development of graduate programs in Computational Toxicology to adequately train the needed professional.

An Epidemiological Study of Exposure to Coal Tar Pitch Volatiles Among Coke Oven Workers

Recent studies of mortality among coke plant workers indicate that there is an excess of respiratory cancer among men employed at the coke ovens and that the mortality is related to work areas and length of exposure to coal tar effluents, the body of information presented in this paper is directed to categorization of coke oven jobs into different work areas in terms of exposure to coal tar pitch volatiles developing an index of cumulative exposure to investigate the dose-response relationship between exposure and mortality. The exposure data have been taken from a study conducted by the Pennsylvania Department of Health, State Division of Occupational Health, and mortality data are based on a long-term study of steelworkers, conducted by the Department of Biostatistics, University of Pittsburgh. A summary index calculated for each worker combining the level of exposure and length of time exposed indicates that, as expected, both these factors are related to the development of cancer, particularly cancers...

Estimation of the optimal data base size for structure-activity analyses: The Salmonella mutagenicity data base

In the present study, the effects of data base size on predictivity, informational content and structural overlap of derived Structure-Activity Relationship (SAR) models were investigated. It was found that indices of predictivity (i.e., sensitivity, specificity, and concordance between experimental and predicted results (OCP) increased with increasing size of the data base until the range is 300-400 chemicals, at which point they plateau. The greater the size of the data base, the greater the informational content of the model; however, the rate of this increase is no longer optimal when the size of the data base exceeds 400 chemicals.

Structure-activity relationships and computer-assisted analysis of respiratory sensitization potential

The mechanism(s) underlying respiratory sensitivity to chemicals is uncertain but is assumed to involve immunologic components with pharmacologic and neurologic involvement. Predictive testing would be valuable to prevent occurrence of hypersensitivity. Several in vitro and in vivo approaches have been used for predictive purposes. In vitro methods have included assessment of the ability of the chemical to undergo reaction with proteins. Computational methods have investigated the relationship between structure and electrophilic potential of chemical allergens. We have initiated a structure-activity evaluation of chemicals associated with elicitation of respiratory sensitization and have utilized a computer-based expert system, MultiCASE. A preliminary database of 39 active chemicals has been established from a literature search of clinical case reports and animal test results. Evaluation of the model has indicated structural alerts for activity which consist of structural fragments as well as physicochemical properties. Further development of the model and evaluation of findings should enable mechanistic insight into the process of respiratory sensitization and recognition of factors which distinguish respiratory sensitizers mechanistically from other chemical allergens such as contact sensitizing chemicals.

The Utility of Structure–Activity Relationship (SAR) Models for Prediction and Covariate Selection in Developmental Toxicity: Comparative Analysis of Logistic Regression and Decision Tree Models

Structure–activity relationship (SAR) models can be used to predict the biological activity of potential developmental toxicants whose adverse effects include death, structural abnormalities, altered growth and functional deficiencies in the developing organism. Physico-chemical descriptors of spatial, electronic and lipophilic properties were used to derive SAR models by two modeling approaches, logistic regression and Classification and Regression Tree (CART), using a new developmental database of 293 chemicals (FDA/TERIS). Both single models and ensembles of models (termed bagging) were derived to predict toxicity. Assessment of the empirical distributions of the prediction measures was performed by repeated random partitioning of the data set. Results showed that both the decision tree and logistic regression derived developmental SAR models exhibited modest prediction accuracy. Bagging tended to enhance the prediction accuracy and reduced the variability of prediction measures compared to the single model for CART-based models but not consistently for logistic-based models. Prediction accuracy of single logistic-based models was higher than single CART-based models but bagged CART-based models were more predictive. Descriptor selection in SAR for the understanding of the developmental mechanism was highly dependent on the modeling approach. Although prediction accuracy was similar in the two modeling approaches, there was inconsistency in the model descriptors.

Occupational medical history taking: How are today's physicians doing? A cross-sectional investigation of the frequency of occupational history taking by physicians in a Major U.S. Teaching center

Occupational illness plays a prominent role in the health of society, yet physicians frequently neglect occupational history-taking both in clinical practice and in medical education. This study sought to examine the trends as well as related factors that influence the taking of occupationally related histories. A total of 2050 charts were reviewed for occupational information as well as several patient demographics. Physicians obtained gender and age histories in approximately 99% of their patients; however; they only completed an occupational history in 27.8%. Characteristics such as smoking, male gender, family cancer history, middle age, and medical (vs. surgical) admission were all correlated with obtaining an occupational history. Physicians continue to do a poor job of occupational history-taking and medical education must correct the situation.

Using alternative comparison populations to assess occupation-related mortality risk results for the high nickel alloys workers cohort

The focus of this article is to examine how the choice of comparison group affects the identification and interpretation of cause-specific health risks in occupational cohorts when different external control populations are used. The mortality experience of approximately 31,000 high nickel alloys workers is compared with the total US population and to local populations in geographic proximity to the plants. Generally, the patterns of relative risks derived for the total cohort and various subgroups are similar across the different comparison populations. Estimated elevated risks are usually lower when cohort mortality is compared with that of local populations. An overall significant 13% risk for lung cancer is noted when compared with that of the total US population. However, no significant excess is identified when local populations are used. Subset analysis identified significant excesses of colon cancer among nonwhite males (50%-150%) and kidney cancer among white male workers employed in melting (approximately 100%), irrespective of the comparison population.

Decision tree SAR models for developmental toxicity based on an FDA/TERIS database.

Humans are exposed to thousands of environmental chemicals for which no developmental toxicity information is available. Structure-activity relationships (SARs) are models that could be used to efficiently predict the biological activity of potential developmental toxicants. However, at this time, no adequate SAR models of developmental toxicity are available for risk assessment. In the present study, a new developmental database was compiled by combining toxicity information from the Teratogen Information System (TERIS) and the Food and Drug Administration (FDA) guidelines. We implemented a decision tree modeling procedure, using Classification and Regression Tree software and a model ensemble approach termed bagging. We then assessed the empirical distributions of the prediction accuracy measures of the single and ensemble-based models, achieved by repeating our modeling experiment many times by repeated random partitioning of the working database. The decision tree developmental SAR models exhibited modest prediction accuracy. Bagging tended to enhance the accuracy of prediction. Also, the model ensemble approach reduced the variability of prediction measures compared to the single model approach. Further research with data derived from animal species- and endpoint-specific components of an extended and refined FDA/TERIS database has the potential to derive SAR models that would be useful in the developmental risk assessment of the thousands of untested chemicals.

Discriminating Between Individuals with and without Musculoskeletal Disorders of the Upper Extremity by Means of Items Related to Computer Keyboard Use

Introduction Identifying postures and behaviors during keyboard use that can discriminate between individuals with and without musculoskeletal disorders of the upper extremity (MSD-UE) is important for developing intervention strategies. This study explores the ability of models built from items of the Keyboard-Personal Computer Style instrument (K-PeCS) to discriminate between subjects who have MSD-UE and those who do not. Methods Forty-two subjects, 21 with diagnosed MSD-UE (cases) and 21 without MSD-UE (controls), were videotaped while using their keyboards at their onsite computer workstations. These video clips were rated using the K-PeCS. The K-PeCS items were used to generate models to discriminate between cases and controls using Classification and Regression Tree (CART) methods. Results Two CART models were generated; one that could accurately discriminate between cases and controls when the cases had any diagnosis of MSD-UE (69% accuracy) and one that could accurately discriminate between cases and controls when the cases had neck-related MSD-UE (93% accuracy). Both models had the same single item, “neck flexion angle greater than 20°”. In both models, subjects who did not have a neck flexion angle of greater than 20° were accurately identified as controls. Conclusions The K-PeCS item “neck flexion greater than 20°” can discriminate between subjects with and without MSD-UE. Further research with a larger sample is needed to develop models that have greater accuracy.

Evaluating clinical case report data for SAR modeling of allergic contact dermatitis

Clinical case reports can be important sources of information for alerting health professionals to the existence of possible health hazards. Isolated case reports, however, are weak evidence of causal relationships between exposure and disease because they do not provide an indication of the frequency of a particular exposure leading to a disease event. A database of chemicals causing allergic contact dermatitis (ACD) was compiled to discern structure-activity relationships. Clinical reports repre sented a considerable fraction of the data. Multiple Computer Automated Structure Evaluation (MultiCASE) was used to create a structure-activity model to be used in predicting the ACD activity of untested chemicals. We examined how the predictive ability of the model was influenced by including the case report data in the model. In addition, the model was used to predict the activity of chemicals identified from clinical case reports. The following results were obtained: • When chemicals which were identified as dermal sensitizers by only one or two case reports were included in the model, the specificity of the model was reduced. • Less than one half of these chemicals were predicted to be active by the most highly evidenced model. • These chemicals possessed substructures not pre viously encountered by any of the models. We conclude that chemicals classified as sensitizers based on isolated clinical case reports be excluded from our model of ACD. The approach described here for evaluating activity of chemicals based on sparse evidence should be considered for use with other endpoints of toxicity when data are correspondingly limited.