Biomedical subjects

L K Teuschler

Publications and source records attributed to L K Teuschler.

6 recordsLinked to original sources

PubMed2005

A unifying concept for assessing toxicological interactions: changes in slope.

Robust statistical methods are important to the evaluation of toxicological interactions (i.e., departures from additivity) among chemicals in a mixture. However, different concepts of joint toxic action as applied to the statistical analysis of chemical mixture toxicology data or as used in environmental risk assessment often appear to conflict with one another. A unifying approach for application of statistical methodology in chemical mixture toxicology research is based on consideration of change(s) in slope. If the slope of the dose-response curve of one chemical does not change in the presence of other chemicals, then there is no interaction between the first chemical and the others. Conversely, if the rate of change in the response with respect to dose of the first chemical changes in the presence of the other chemicals, then an interaction is said to exist. This concept of zero interaction is equivalent to the usual approach taken in additivity models in the statistical literature. In these additivity models, the rate of change in the response as a function of the i(th) chemical does not change in the presence of other chemicals in a mixture. It is important to note that Berenbaum's (1985, J. Theor. Biol. 114, 413-431) general and fundamental definition of additivity does not require the chemicals in the mixture to have a common toxic mode of action nor to have similarly shaped dose response curves. We show an algebraic equivalence between these statistical additivity models and the definition of additivity given by Berenbaum.

Complex Mixtures↗

PubMed2001

Using dose addition to estimate cumulative risks from exposures to multiple chemicals.

The Food Quality Protection Act (FQPA) of 1996 requires the EPA to consider the cumulative risk from exposure to multiple chemicals that have a common mechanism of toxicity. Three methods, hazard index (HI), point-of-departure index (PODI), and toxicity equivalence factor (TEF), have commonly been considered to estimate the cumulative risk. These methods are based on estimates of ED(10) (point of departure) and reference doses from the dose-response functions of individual chemicals. They do not incorporate the actual dose-response function of the mixture from multiple chemical exposures. Dose addition is considered to be an appropriate approach to cumulative risk assessment because it assumes that the chemicals of interest act in accordance with a common mode of action (a similar action). This paper proposes a formal statistical procedure to estimate the cumulative risk by fitting the dose-response model of the mixture under dose addition. The relative potency between two chemicals is estimated directly from the joint dose response model of the mixture. An example data set of four drugs representing four chemicals is used to illustrate the proposed procedure and compare it to the HI, PODI, and TEF methods.

Dose-Response Relationship, Drug↗

PubMed2000

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.

Alanine Transaminase↗

PubMed1999

Health risk above the reference dose for multiple chemicals.

Recent work indicates that the regression of toxicity data viewed as categories of pathological staging is useful for exploring the likely health risk at doses above a Reference Dose (RfD), which is an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Toxic effects, which may include both quantal and continuous data, are classified into ordered categories of total toxic severity (e.g., none, mild, adverse, severe). These severity categories are regressed on explanatory variables, such as dose or exposure duration, to estimate the probability of observing an adverse or severe effect. In this paper, categorical regression has been expanded to compare the likely risks across multiple chemicals when exposures are above their RfDs. Existing health risk data for diazinon, disulfoton, S-ethyl dipropylthiocarbamate, fenamiphos, and lindane were analyzed. As expected, the estimated risks of adverse effects above the RfD varied among the chemicals. For example, at 10-fold above the RfD these risks were modeled to be 0.002, 0.0001, 0.0007, 0.002, and 0.02, respectively. The results and impacts of this analysis indicate that categorical regression is a useful screening tool to analyze risks above the RfD for specific chemicals and suggest its application in evaluating comparative risks where multiple chemical exposures exist.

Animals↗

PubMed1997

Categorical regression of toxicity data: a case study using aldicarb.

Categorical regression is a mathematical tool that can be adapted to estimate potential health risk from chemical exposures. By regressing ordered categories of toxic severity or pathological staging on exposure dose, this method can estimate the likelihood of observing any of the categories of severity at any dose level. Depending on the nature of the available data, these estimates can take the form of incidence rates for any of the categories in an exposed population or the probability of a new study conducted at a specified dose level being classified as one of the categories. Categorical regression is illustrated using toxicity data on aldicarb. For aldicarb, the data fall into three different groups: human clinical studies, dietary exposures in experimental animals, and accidental human exposure by contaminated crops. The U.S. EPA has assessed this literature and developed a reference dose (RfD) of 0.001 mg/kg-day. The results of applying categorical regression to data from human clinical studies suggests a maximum likelihood risk estimate of adverse effects of 0.008% at a 10-fold higher dose than the RfD when blood cholinesterase inhibition is not considered as an adverse effect. When blood cholinesterase inhibition of 20% or more is considered as an adverse effect, a maximum likelihood risk estimate of adverse effects is 0.1% at a dose 10-fold higher than the RfD.

Aldicarb↗

PubMed1995

Current and future risk assessment guidelines, policy, and methods development for chemical mixtures.

Humans are typically exposed to low doses of combinations of chemicals rather than to one or two chemicals at a time, yet most of the available toxicity data provide information on single chemicals or binary pairs, rather than on whole mixtures. The use of existing interactions study data for the quantitative risk assessment of chemical mixtures is problematic. These studies generally lack the necessary statistical characterizations to be useful in quantitative risk assessment procedures. The U.S. EPA developed guidelines for risk assessment for chemical mixtures in 1986 and is currently in the process of making revisions. Significant advances have been made in both the theoretical development and application of procedures such as dose addition, response addition, toxicity equivalence factors, comparative potency and interactions data characterizations. Details on the current revisions to the guidelines are given, along with information on the research efforts that have influenced these revisions or that represent future directions in chemical mixtures risk assessment.

Drug Interactions↗