Biomedical subjects

Richard A Carchman

Publications and source records attributed to Richard A Carchman.

4 recordsLinked to original sources

PubMed2006

Testing for additivity at select mixture groups of interest based on statistical equivalence testing methods.

Several assumptions, defined and undefined, are used in the toxicity assessment of chemical mixtures. In scientific practice mixture components in the low-dose region, particularly subthreshold doses, are often assumed to behave additively (i.e., zero interaction) based on heuristic arguments. This assumption has important implications in the practice of risk assessment, but has not been experimentally tested. We have developed methodology to test for additivity in the sense of Berenbaum (Advances in Cancer Research, 1981), based on the statistical equivalence testing literature where the null hypothesis of interaction is rejected for the alternative hypothesis of additivity when data support the claim. The implication of this approach is that conclusions of additivity are made with a false positive rate controlled by the experimenter. The claim of additivity is based on prespecified additivity margins, which are chosen using expert biological judgment such that small deviations from additivity, which are not considered to be biologically important, are not statistically significant. This approach is in contrast to the usual hypothesis-testing framework that assumes additivity in the null hypothesis and rejects when there is significant evidence of interaction. In this scenario, failure to reject may be due to lack of statistical power making the claim of additivity problematic. The proposed method is illustrated in a mixture of five organophosphorus pesticides that were experimentally evaluated alone and at relevant mixing ratios. Motor activity was assessed in adult male rats following acute exposure. Four low-dose mixture groups were evaluated. Evidence of additivity is found in three of the four low-dose mixture groups. The proposed method tests for additivity of the whole mixture and does not take into account subset interactions (e.g., synergistic, antagonistic) that may have occurred and cancelled each other out.

Animals↗

PubMed2005

Chronic nose-only inhalation study in rats, comparing room-aged sidestream cigarette smoke and diesel engine exhaust.

Nose-only exposure of male and female Wistar rats to a surrogate for environmental tobacco smoke, termed room-aged sidestream smoke (RASS), to diesel engine exhaust (DEE), or to filtered, fresh air (sham) was performed 6 hours/day, 7 days/week for 2 years, followed by a 6-month post-exposure period. The particulate concentrations were 3 and 10 mg/m3. Markers of inflammation in bronchoalveolar lavage showed that DEE (but not RASS) produced a dose-related and persistent inflammatory response. Lung weights were increased markedly in the DEE (but not RASS) groups and did not decrease during the 6-month post-exposure period. Bulky lung DNA adducts increased in the RASS groups, but not in the DEE groups. Cell proliferation in the lungs was unaffected by either experimental treatment. Histopathological responses in the RASS groups were minimal and almost completely reversible; lung tumors were similar in number to those seen in the sham-exposed groups. Rats exposed to DEE showed a panoply of dose-related histopathological responses: largely irreversible and in some cases progressive. Malignant and multiple tumors were seen only in the DEE groups; after 30 months, the tumor incidence (predominantly bronchiolo-alveolar adenomas) was 2% in the sham-exposed groups, 5%in the high RASS groups, and 46% in the high DEE groups (sexes combined). Our results suggest that in rats exposed to DEE, but not to RASS, the following series of events occurs: particle deposition in lungs --> lung "overload" --> pulmonary inflammation --> tumorigenesis, without a significant modifying role of cell proliferation or DNA adduct formation.

Adenoma↗

PubMed2005

Thyroid-hormone-disrupting chemicals: evidence for dose-dependent additivity or synergism.

Endocrine disruption from environmental contaminants has been linked to a broad spectrum of adverse outcomes. One concern about endocrine-disrupting xenobiotics is the potential for additive or synergistic (i.e., greater-than-additive) effects of mixtures. A short-term dosing model to examine the effects of environmental mixtures on thyroid homeostasis has been developed. Prototypic thyroid-disrupting chemicals (TDCs) such as dioxins, polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers have been shown to alter thyroid hormone homeostasis in this model primarily by up-regulating hepatic catabolism of thyroid hormones via at least two mechanisms. Our present effort tested the hypothesis that a mixture of TDCs will affect serum total thyroxine (T4) concentrations in a dose-additive manner. Young female Long-Evans rats were dosed via gavage with 18 different polyhalogenated aromatic hydrocarbons [2 dioxins, 4 dibenzofurans, and 12 PCBs, including dioxin-like and non-dioxin-like PCBs] for 4 consecutive days. Serum total T4 was measured via radioimmunoassay in samples collected 24 hr after the last dose. Extensive dose-response functions (based on seven to nine doses per chemical) were determined for individual chemicals. A mixture was custom synthesized with the ratio of chemicals based on environmental concentrations. Serial dilutions of this mixture ranged from approximately background levels to 100-fold greater than background human daily intakes. Six serial dilutions of the mixture were tested in the same 4-day assay. Doses of individual chemicals that were associated with a 30% TH decrease from control (ED30), as well as predicted mixture outcomes were calculated using a flexible single-chemical-required method applicable to chemicals with differing dose thresholds and maximum-effect asymptotes. The single-chemical data were modeled without and with the mixture data to determine, respectively, the expected mixture response (the additivity model) and the experimentally observed mixture response (the empirical model). A likelihood-ratio test revealed statistically significant departure from dose additivity. There was no deviation from additivity at the lowest doses of the mixture, but there was a greater-than-additive effect at the three highest mixtures doses. At high doses the additivity model underpredicted the empirical effects by 2- to 3-fold. These are the first results to suggest dose-dependent additivity and synergism in TDCs that may act via different mechanisms in a complex mixture. The results imply that cumulative risk approaches be considered when assessing the risk of exposure to chemical mixtures that contain TDCs.

Animals↗

PubMed2004

A novel flexible approach for evaluating fixed ratio mixtures of full and partial agonists.

Assessing for interactions among chemicals in a mixture involves the comparison of actual mixture responses to those predicted under the assumption of zero interaction (additivity), based on individual chemical dose-response data. However, current statistical methods do not adequately account for differences in the shapes of the dose-response curves of the individual mixture components, as occurs with mixtures of full and partial receptor agonists. We present here a novel extension of current methods, which overcomes some of these limitations. Flexible single chemical concentration-effect curves combined with a common background parameter are used to describe an additivity surface along each axis. The predicted mixture response under the assumption of additivity is based on the constraint of Berenbaum's definition of additivity. Iterative algorithms are used to estimate mean responses at observed mixture combinations using only single chemical parameters. A full model allowing for different maximum response levels, different thresholds, and different slope parameters for each mixture component is compared to a reduced model under the assumption of additivity. A likelihood-ratio test is used to test the hypothesis of additivity by utilizing the full and reduced model predictions. This approach is useful for mixtures of chemicals with threshold regions and whose component chemicals exhibit differing response maxima (e.g., mixtures of full and partial agonists). The methods are illustrated with a combination of six chemicals in an estrogen receptor-alpha (ER-alpha) reporter gene assay.

Algorithms↗