Bruce Allen Consulting

Chapel Hill, NC, United States

Bruce Allen Consulting

Chapel Hill, NC, United States

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Cao X.,U.S. Food and Drug Administration | Mittelstaedt R.A.,U.S. Food and Drug Administration | Pearce M.G.,U.S. Food and Drug Administration | Allen B.C.,Bruce Allen Consulting | And 4 more authors.
Environmental and Molecular Mutagenesis | Year: 2014

The assumption that mutagens have linear dose-responses recently has been challenged. In particular, ethyl methanesulfonate (EMS), a DNA-reactive mutagen and carcinogen, exhibited sublinear or thresholded dose-responses for LacZ mutation in transgenic Muta™Mouse and for micronucleus (MN) frequency in CD1 mice (Gocke E and Müller L [2009]: Mutat Res 678:101-107). In order to explore variables in establishing genotoxicity dose-responses, we characterized the genotoxicity of EMS using gene mutation assays anticipated to have lower spontaneous mutant frequencies (MFs) than Muta™Mouse. Male gpt-delta transgenic mice were treated daily for 28 days with 5 to 100 mg/kg EMS, and measurements were made on: (i) gpt MFs in liver, lung, bone marrow, kidney, small intestine, and spleen; and (ii) Pig-a MFs in peripheral blood reticulocytes (RETs) and total red blood cells. MN induction also was measured in peripheral blood RETs. These data were used to calculate Points of Departure (PoDs) for the dose responses, i.e., no-observed-genotoxic-effect-levels (NOGELs), lower confidence limits of threshold effect levels (Td-LCIs), and lower confidence limits of 10% benchmark response rates (BMDL10s). Similar PoDs were calculated from the published EMS dose-responses for LacZ mutation and CD1 MN induction. Vehicle control gpt and Pig-a MFs were 13-40-fold lower than published vehicle control LacZ MFs. In general, the EMS genotoxicity dose-responses in gpt-delta mice had lower PoDs than those calculated from the Muta™Mouse and CD1 mouse data. Our results indicate that the magnitude and possibly the shape of mutagenicity dose responses differ between in vivo models, with lower PoDs generally detected by gene mutation assays with lower backgrounds. © 2014 Wiley Periodicals, Inc.

PubMed | Bruce Allen Consulting, Indiana University Bloomington and U.S. National Institute for Occupational Safety and Health
Type: | Journal: Risk analysis : an official publication of the Society for Risk Analysis | Year: 2016

Human variability is a very important factor considered in human health risk assessment for protecting sensitive populations from chemical exposure. Traditionally, to account for this variability, an interhuman uncertainty factor is applied to lower the exposure limit. However, using a fixed uncertainty factor rather than probabilistically accounting for human variability can hardly support probabilistic risk assessment advocated by a number of researchers; new methods are needed to probabilistically quantify human population variability. We propose a Bayesian hierarchical model to quantify variability among different populations. This approach jointly characterizes the distribution of risk at background exposure and the sensitivity of response to exposure, which are commonly represented by model parameters. We demonstrate, through both an application to real data and a simulation study, that using the proposed hierarchical structure adequately characterizes variability across different populations.

Thomas R.S.,Hamner Institutes for Health Sciences | Clewell H.J.,Hamner Institutes for Health Sciences | Allen B.C.,Bruce Allen Consulting | Yang L.,Hamner Institutes for Health Sciences | And 2 more authors.
Mutation Research - Genetic Toxicology and Environmental Mutagenesis | Year: 2012

The traditional approach for performing a chemical risk assessment is time and resource intensive leading to a limited number of published assessments on which to base human health decisions. In comparison, most contaminated sites contain chemicals without published reference values or cancer slope factors that are not considered quantitatively in the overall hazard index calculation. The integration of transcriptomic technology into the risk assessment process may provide an efficient means to evaluate quantitatively the health risks associated with data poor chemicals. In a previous study, female B6C3F1 mice were exposed to multiple concentrations of five chemicals that were positive for lung and/or liver tumor formation in a two-year rodent cancer bioassay. The mice were exposed for a period of 13 weeks and the target tissues were analyzed for traditional histological and organ weight changes and transcriptional changes using microarrays. In this study, the dose-response changes in gene expression were analyzed using a benchmark dose (BMD) approach and the responses grouped based on pathways. A comparison of the transcriptional BMD values with those for the traditional non-cancer and cancer apical endpoints showed a high degree of correlation for specific pathways. Many of the correlated pathways have been implicated in non-cancer and cancer disease pathogenesis. The results demonstrate that transcriptomic changes in pathways can be used to estimate non-cancer and cancer points-of-departure for use in quantitative risk assessments and have identified potential toxicity pathways involved in chemically induced mouse lung and liver responses. © 2012 Elsevier B.V.

Thomas S.R.,Hamner Institutes for Health Sciences | Wesselkamper S.C.,U.S. Environmental Protection Agency | Wang N.C.Y.,U.S. Environmental Protection Agency | Zhao Q.J.,U.S. Environmental Protection Agency | And 9 more authors.
Toxicological Sciences | Year: 2013

The number of legacy chemicals without toxicity reference values combined with the rate of new chemical development is overwhelming the capacity of the traditional risk assessment paradigm. More efficient approaches are needed to quantitatively estimate chemical risks. In this study, rats were dosed orally with multiple doses of six chemicals for 5 days and 2, 4, and 13 weeks. Target organs were analyzed for traditional histological and organ weight changes and transcriptional changes using microarrays. Histological and organ weight changes in this study and the tumor incidences in the original cancer bioassays were analyzed using benchmark dose (BMD) methods to identify noncancer and cancer points of departure. The dose-response changes in gene expression were also analyzed using BMD methods and the responses grouped based on signaling pathways. A comparison of transcriptional BMD values for the most sensitive pathway with BMD values for the noncancer and cancer apical endpoints showed a high degree of correlation at all time points. When the analysis included data from an earlier study with eight additional chemicals, transcriptional BMD values for the most sensitive pathway were significantly correlated with noncancer (r = 0.827, p = 0.0031) and cancer-related (r = 0.940, p = 0.0002) BMD values at 13 weeks. The average ratio of apical-to-transcriptional BMD values was less than two, suggesting that for the current chemicals, transcriptional perturbation did not occur at significantly lower doses than apical responses. Based on our results, we propose a practical framework for application of transcriptomic data to chemical risk assessment. © The Author 2013. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved.

Yoon M.,Hamner Institutes for Health Sciences | Kedderis G.L.,Independent Consultant | Yang Y.,Hamner Institutes for Health Sciences | Allen B.C.,Bruce Allen Consulting | And 2 more authors.
ACS Symposium Series | Year: 2012

The goal of this research was to demonstrate a process for developing a human physiologically based pharmacokinetic (PBPK) model based to the greatest extent possible on in vitro to in vivo extrapolation using studies with animal and human cells. The in vitro studies were conducted to estimate parameters for carbaryl clearance from the body and its interactions with cholinesterases (ChEs), which were identified as uncertain parameters in previous modeling studies for carbaryl in rats. The in vitro PK and PD data were extrapolated to the whole body using biologically based scaling processes to predict the disposition and ChE inhibition dynamics of carbaryl in vivo. The validity of the approach was evaluated using published kinetic data for rats. Data gaps identified in the current study were the need for in vitro methods for estimating intestinal absorption and pre-hepatic metabolism. This proposed modeling approach can serve as a template for developing models for other environmental chemicals using in vitro data. © 2012 American Chemical Society.

Yang Y.,Hamner Institutes for Health Sciences | Allen B.C.,Bruce Allen Consulting | Tan Y.-M.,Hamner Institutes for Health Sciences | Liao K.H.,Hamner Institutes for Health Sciences | Clewell III H.J.,Hamner Institutes for Health Sciences
Journal of Toxicology and Environmental Health - Part A: Current Issues | Year: 2010

As the initial effort in a multi-step uncertainty analysis of a biologically based cancer model for formaldehyde, a Markov chain Monte Carlo (MCMC) analysis was performed for a compartmental model that predicts DNA-protein cross-links (DPX) produced by formaldehyde exposure. The Bayesian approach represented by the MCMC analysis integrates existing knowledge of the model parameters with observed, formaldehyde-DPX-specific data, providing a statistically sound basis for estimating model output uncertainty. Uncertainty and variability were evaluated through a hierarchical structure, where interindividual variability was considered for all model parameters and that variability was assumed to be uncertain on population levels. The uncertainty of the population mean and that of the population variance were significantly reduced through the MCMC analysis. Our investigation highlights several issues that must be dealt with in many real-world analyses (e.g., issues of parameters' nonidentifiability due to limited data) while demonstrating the feasibility of conducting a comprehensive quantitative uncertainty evaluation. The current analysis can be viewed as a case study, for a relatively simple model, illustrating some of the constraints that analysts will face when applying Bayesian approaches to biologically or physiologically based models of increasing complexity.

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