William Stiteler

Group contribution method for predicting probability and rate of aerobic biodegradation

Two independent training sets were used to develop four mathematical models for predicting aerobic biodegradability from chemical structure. All four of the models are based on multiple regressions against counts of 36 preselected chemical substructures plus molecular weight. Two of the models, based on linear and nonlinear regressions, calculate the probability of rapid biodegradation and can be used to classify chemicals as rapidly or not rapidly biodegradable. The training set for these models consisted of qualitative summary evaluations of all available experimental data on biodegradability for 295 chemicals. The other two models allow semi-quantitative prediction of primary and ultimate biodegradation rates using multiple linear regression

URINARY CADMIUM ELIMINATION AS A BIOMARKER OF EXPOSURE FOR EVALUATING A CADMIUM DIETARY EXPOSURE - BIOKINETICS MODEL

The Cadmium Dietary Exposure Model (CDEM) utilizes national survey data on food cadmium concentrations and food consumption patterns to estimate dietary intakes in the U.S. population. The CDEM has been linked to a modification of the cadmium biokinetic model of Kjellström and Nordberg (KNM) to derive predictions of kidney and urinary cadmium that reflect U.S. dietary cadmium intake and related variability. Variability in dietary cadmium intake was propagated through the KNM using a Monte Carlo approach. The model predicts a mean peak kidney cadmium burden of approximately 3.5 mg and a 5th-95th percentile range of 2.2-5.1 mg in males. The corresponding peak renal cortex cadmium concentration in males is 15 µg/g wet cortex (10-22, 5th-95th percentile). Predicted kidney cadmium levels in females were higher than males: 5.1 (3.3-7.6) mg total kidney, 29 (19-43) µg/g wet cortex. Predicted urinary cadmium in males and females agreed with empirical estimates based on the NHANES III, with females predicted and observed to excrete approximately twice the amount of cadmium in urine than males. An explanation for the higher urinary cadmium excretion in females is proposed that is consistent with the NHANES III data as well as experimental studies in humans and animals. Females may absorb a larger fraction of ingested dietary cadmium than males, and this difference may be the result of lower iron body stores in females compared to males. This would suggest that females may be at greater risk of developing cadmium toxicity than males. The predicted 5th-95th percentile values for peak kidney cadmium burden are approximately 60% of the peak kidney burden (8-11 mg) predicted for a chronic intake at the U.S. Environmental Protection Agency (EPA) chronic reference dose of 1 µg/kg-d.

A new biodegradation prediction model specific to petroleum hydrocarbons

A new predictive model for determining quantitative primary biodegradation half-lives of individual petroleum hydrocarbons has been developed. This model uses a fragment-based approach similar to that of several other biodegradation models, such as those within the Biodegradation Probability Program (BIOWIN) estimation program. In the present study, a half-life in days is estimated using multiple linear regression against counts of 31 distinct molecular fragments. The model was developed using a data set consisting of 175 compounds with environmentally relevant experimental data that was divided into training and validation sets. The original fragments from the Ministry of International Trade and Industry BIOWIN model were used initially as structural descriptors and additional fragments were then added to better describe the ring systems found in petroleum hydrocarbons and to adjust for nonlinearity within the experimental data. The training and validation sets had r2 values of 0.91 and 0.81, respectively.

Development of a predictive model for biodegradability based on BIODEG, the evaluated biodegradation data base

A file of evaluated biodegradation data was used to develop a model for predicting aerobic biodegradability from chemical structure alone. Chemicals were initially divided into three groups: (i) chemicals that degrade rapidly under most environmental conditions without requiring acclimation; (ii) chemicals that degrade slowly or not at all; and (iii) chemicals that are biodegradable, but only after an acclimation period. Chemicals in the first two groups were then used to develop a model for classifying chemicals as rapidly or not rapidly biodegradable. The model is based on linear regression against 34 preselected substructures, and correctly classifies 92% (211 or 229) of the chemicals in the final training set.

The identification and testing of interaction patterns.

This paper presents a method for identifying and assessing the significance of interaction patterns among various chemicals and chemical classes of importance to regulatory toxicologists. To this end, efforts were made to assemble and evaluate experimental data on toxicologically significant interactions, to use this information to characterize the consistency of toxicological interactions, and to define classes of compounds that display similar toxicological interactions. The motivation for this effort is to be able to propose hypotheses, which can be validated by experimentation, on how 2 or more chemicals will interact.

A MULTIPLE-PURPOSE DESIGN APPROACH TO THE EVALUATION OF RISKS FROM MIXTURES OF DISINFECTION BY-PRODUCTS*

Drinking water disinfection has effectively eliminated much of the morbidity and mortality associated with waterborne infectious diseases in the United States. Various disinfection processes, however, produce certain types and amounts of disinfection by-products (DBPs), including trihalomethanes (THM), haloacetic acids, haloacetonitriles, and bromate, among others. Human health risks from the ubiquitous exposure to complex mixtures of DBPs are of concern because existing epidemiologic and toxicologic studies suggest the existence of systemic or carcinogenic effects. Researchers from several organizations have developed a multiple-purpose design approach to this problem that combines efficient laboratory experimental designs with statistical models to provide data on critical research issues (e.g., estimation of human health risk from low-level DBP exposures, evaluation of additivity assumptions as useful for risk characterization, estimation of health risks from different drinking water treatment options). A series of THM experiments have been designed to study embryonic development, mortality and cancer in Japanese medaka (Oryzias latipes) and liver and kidney endpoints in female CD-1 mice. The studies are to provide dose-response data for specific mixtures of the 4 THMs, for the single chemicals, and for binary combinations. The dose-levels and mixing ratios for these experiments were selected to be useful for development and refinement of three different statistical methods: testing for departures from dose-additivity; development of an interactions-based hazard index; and use of proportional-response addition as a risk characterization method. Preliminary results suggest that dose-additivity is a reasonable risk assessment assumption for DBPs. The future of mixtures research will depend on such collaborative efforts that maximize the use of resources and focus on issues of high relevance to the risk assessment of human health.