Biomedical subjects

Ralph L Kodell

Publications and source records attributed to Ralph L Kodell.

14 recordsLinked to original sources

PubMed2006

A probabilistic framework for non-cancer risk assessment.

Risk assessment involves an analysis of the relationship between exposure and health related outcomes to derive an allowable exposure level or to estimate a low-dose risk. Acceptable levels of human exposure for non-cancer effects generally are derived by dividing an experimental no-observed-adverse-effect-level or a lower confidence limit benchmark dose by a product of several uncertainty factors. This paper presents a hierarchical modeling framework for a probabilistic approach to non-cancer risk assessment. The hierarchical model integrates the distributions of uncertainty factors and the distribution of the actual exposure level to construct the dose-response model for the proportion of population at risk and the dose-response model for the expected proportion of population at risk for a given exposure distribution. The proposed approach is based on the use of the BMDL (lower confidence limit on the benchmark dose) as a POD (point of departure) for risk assessment of non-cancer effects.

Animals↗

PubMed2006

Carcinogenicity of malachite green chloride and leucomalachite green in B6C3F1 mice and F344 rats.

Malachite green is a triphenylmethane dye used in the fish industry as an anti-fungal agent. Leucomalachite green is formed by the metabolic reduction of malachite green and persists in the tissues of exposed fish. In this study, we examined the carcinogenicity of malachite green chloride and leucomalachite green. Female F344 rats (48 per group) were fed diets containing 0, 100, 300, or 600 ppm malachite green chloride for 104 weeks, at which time the extent of tumorigenesis was assessed. Additional groups of 48 female and 48 male F344 rats were fed diets containing 0, 91, 272, or 543 ppm leucomalachite green for 104 weeks. Groups of 48 female B6C3F1 mice were fed diets containing 0, 100, 225, or 450 ppm malachite green chloride or 0, 91, 204, or 408 ppm leucomalachite green for 104 weeks. For each of the exposures, food consumption in the treatment groups was similar to the controls. Rats fed malachite green chloride or leucomalachite green had dose-dependent reductions in body weight; in mice, there were no consistent effects upon body weights with either compound. Female rats exposed to malachite green chloride had increased incidences of thyroid gland follicular cell adenoma or carcinoma and hepatocellular adenoma, and a dose-related increasing trend in mammary gland carcinoma. Female rats fed malachite green chloride and female and male rats fed leucomalachite green had a dose-related decreasing trend in the incidence of mononuclear cell leukemia. In male rats fed leucomalachite green there was a decreasing trend in pituitary gland adenoma and an increasing trend in interstitial cell adenoma of the testis. There were no treatment-related neoplasms in female B6C3F1 mice fed malachite green chloride. Female mice fed leucomalachite green had a dose-related increasing trend in the incidence of hepatocellular adenoma or carcinoma, with the incidence being significant in the highest dose group.

Aniline Compounds↗

PubMed2006

Database composition can affect the structure-activity relationship prediction.

The percent active (A) and inactive (I) chemicals in a database can directly affect the sensitivity (% active chemicals predicted correctly) and specificity (% inactive chemicals predicted correctly) of structure-activity relationship (SAR) analyses. Subdividing the National Center for Toxicological Research (NCTR) liver cancer database (NCTRlcdb) into various A/I ratios, which varied from 0.2 to 5.5, resulted in sensitivity/specificity ratios that varied from 0.1 to 6.5. As percent active chemicals increased (increasing A/I ratio), the sensitivity rose, the specificity decreased, and the concordance (% total chemicals predicted correctly) remained fairly constant. The numbers of chemicals in the various data sets ranged from 187 to 999 and appeared to have no affect on any of the 3 predictors of sensitivity, specificity, or concordance.

Animals↗

PubMed2006

Classification methods for the development of genomic signatures from high-dimensional data.

Personalized medicine is defined by the use of genomic signatures of patients to assign effective therapies. We present Classification by Ensembles from Random Partitions (CERP) for class prediction and apply CERP to genomic data on leukemia patients and to genomic data with several clinical variables on breast cancer patients. CERP performs consistently well compared to the other classification algorithms. The predictive accuracy can be improved by adding some relevant clinical/histopathological measurements to the genomic data.

Algorithms↗

PubMed2005

An age-adjusted bootstrap-based Poly-k test.

The assumption of an asymptotic normal distribution of some test statistics may be invalid in certain dose-response trend tests. For instance, the survival-adjusted Cochran-Armitage test, known as the Poly-k test, is asymptotically standard normal under the null hypothesis. However, the asymptotic normality is not valid if there is a deviation from the tumour onset distribution that is assumed in this test or if the competing risks survival rates differ across groups. We develop an age-adjusted bootstrap-based method to assess the significance of assumed asymptotic normal tests for animal carcinogenicity data. The proposed method differs from conventional bootstrap methods in the aspect of preserving the mortality rate in each dose group under the null hypothesis of equal tumour incidence rates among the groups. We investigate an empirical distribution of the Poly-3 (P3) trend test statistic using the proposed age-adjusted bootstrap-based method and compare it with the P3 test statistic referenced to the assumed standard normal distribution. A simulation study is conducted to evaluate the robustness of these tests to various Weibull-family tumour onset distributions. The proposed method is applied to National Toxicology Program data sets to evaluate a dose-related trend of a test substance on the incidence of neoplasms.

Age Factors↗

PubMed2005

Simultaneous confidence bounds for low-dose risk assessment with nonquantal data.

We study the use of simultaneous confidence bounds for making low-dose inferences in quantitative risk analysis. Confidence limits are constructed for outcomes measured on a continuous scale, assuming a simple linear model for the observed response. From the simultaneous confidence bounds, simultaneous lower limits on the benchmark dose associated with a particular risk are also constructed.

Animals↗

PubMed2005

Multiplicity-adjusted inferences in risk assessment: benchmark analysis with quantal response data.

A primary objective in quantitative risk or safety assessment is characterization of the severity and likelihood of an adverse effect caused by a chemical toxin or pharmaceutical agent. In many cases data are not available at low doses or low exposures to the agent, and inferences at those doses must be based on the high-dose data. A modern method for making low-dose inferences is known as benchmark analysis, where attention centers on the dose at which a fixed benchmark level of risk is achieved. Both upper confidence limits on the risk and lower confidence limits on the "benchmark dose" are of interest. In practice, a number of possible benchmark risks may be under study; if so, corrections must be applied to adjust the limits for multiplicity. In this short note, we discuss approaches for doing so with quantal response data.

Biometry↗

PubMed2005

Model averaging using the Kullback information criterion in estimating effective doses for microbial infection and illness.

Since the National Food Safety Initiative of 1997, risk assessment has been an important issue in food safety areas. Microbial risk assessment is a systematic process for describing and quantifying a potential to cause adverse health effects associated with exposure to microorganisms. Various dose-response models for estimating microbial risks have been investigated. We have considered four two-parameter models and four three-parameter models in order to evaluate variability among the models for microbial risk assessment using infectivity and illness data from studies with human volunteers exposed to a variety of microbial pathogens. Model variability is measured in terms of estimated ED01s and ED10s, with the view that these effective dose levels correspond to the lower and upper limits of the 1% to 10% risk range generally recommended for establishing benchmark doses in risk assessment. Parameters of the statistical models are estimated using the maximum likelihood method. In this article a weighted average of effective dose estimates from eight two- and three-parameter dose-response models, with weights determined by the Kullback information criterion, is proposed to address model uncertainties in microbial risk assessment. The proposed procedures for incorporating model uncertainties and making inferences are illustrated with human infection/illness dose-response data sets.

Food Microbiology↗

PubMed2004

A comparison of microbial dose-response models fitted to human data.

A study of eight mathematical dose-response models for microbial risk assessment was conducted using infectivity and illness data on a variety of microbial pathogens from published studies with human volunteers. The purpose was to evaluate variability among the models for human microbial dose-response data in order to determine whether two-parameter models might suffice for most microbial dose-response data or whether three-parameter models should generally be fitted. Model variability was measured in terms of estimated ED01s and ED10s, with the view that these effective dose levels correspond to the lower and upper limits of the 1-10% risk range generally recommended for establishing benchmark doses in risk assessment. An investigation of the ranks of the ED01 and ED10 values among the models led to the conclusion that the two-parameter models captured at least as much uncertainty as the three-parameter models for the data examined. A further evaluation of the two-parameter models did not result in the selection of one "best" model, but it did provide some insights into the models' relative behavior. The model uncertainty analysis proposed by Kang et al. [Regulat. Toxicol. Pharmacol. 32 (2000) 68] using four two-parameter models was reinforced.

Algorithms↗

PubMed2004

Multiple-testing strategy for analyzing cDNA array data on gene expression.

An objective of many functional genomics studies is to estimate treatment-induced changes in gene expression. cDNA arrays interrogate each tissue sample for the levels of mRNA for hundreds to tens of thousands of genes, and the use of this technology leads to a multitude of treatment contrasts. By-gene hypotheses tests evaluate the evidence supporting no effect, but selecting a significance level requires dealing with the multitude of comparisons. The p-values from these tests order the genes such that a p-value cutoff divides the genes into two sets. Ideally one set would contain the affected genes and the other would contain the unaffected genes. However, the set of genes selected as affected will have false positives, i.e., genes that are not affected by treatment. Likewise, the other set of genes, selected as unaffected, will contain false negatives, i.e., genes that are affected. A plot of the observed p-values (1 - p) versus their expectation under a uniform [0, 1] distribution allows one to estimate the number of true null hypotheses. With this estimate, the false positive rates and false negative rates associated with any p-value cutoff can be estimated. When computed for a range of cutoffs, these rates summarize the ability of the study to resolve effects. In our work, we are more interested in selecting most of the affected genes rather than protecting against a few false positives. An optimum cutoff, i.e., the best set given the data, depends upon the relative cost of falsely classifying a gene as affected versus the cost of falsely classifying a gene as unaffected. We select the cutoff by a decision-theoretic method analogous to methods developed for receiver operating characteristic curves. In addition, we estimate the false discovery rate and the false nondiscovery rate associated with any cutoff value. Two functional genomics studies that were designed to assess a treatment effect are used to illustrate how the methods allowed the investigators to determine a cutoff to suit their research goals.

Amphetamine↗

PubMed2003

Estimation of k for the poly-k test with application to animal carcinogenicity studies.

This paper extends the survival-adjusted Cochran-Armitage test in order to achieve improved robustness to a variety of tumour onset distributions. The Cochran-Armitage test is routinely applied for detecting a linear trend in the incidence of a tumour of interest across dose groups. To improve the robustness to the effects of differential mortality across groups, Bailer and Portier introduced the poly-3 test by a survival adjustment using a fractional weighting scheme for subjects not at full risk of tumour development. The performance of the poly-3 test depends on how closely it represents the correct specification of the time-at-risk weight in the data. Bailer and Portier further suggested that this test can be improved by using a general k reflecting the shape of the tumour onset distribution. In this paper, we propose a method to estimate k by equating the empirical lifetime tumour incidence rate obtained from the data based on the fractional weighting scheme to a separately estimated cumulative lifetime tumour incidence rate. This poly-k test with the statistically estimated k appears to perform better than the poly-3 test which is conducted without prior knowledge of the tumour onset distribution. Our simulation shows that the proposed method improves the robustness to various tumour onset distributions in addition to the robustness to the effects of mortality achieved by the poly-3 test. Large sample properties are shown via simulations to illustrate the consistency of the proposed method. The proposed methods are applied to analyse two real data sets. One is to find a dose-related linear trend on animal carcinogenicity, and the other is to test an effect of calorie restriction on experimental animals.

2-Acetylaminofluorene↗

PubMed2003

Comparison of methods for estimating the number of true null hypotheses in multiplicity testing.

When a large number of statistical tests is performed, the chance of false positive findings could increase considerably. The traditional approach is to control the probability of rejecting at least one true null hypothesis, the familywise error rate (FWE). To improve the power of detecting treatment differences, an alternative approach is to control the expected proportion of errors among the rejected hypotheses, the false discovery rate (FDR). When some of the hypotheses are not true, the error rate from either the FWE- or the FDR-controlling procedure is usually lower than the designed level. This paper compares five methods used to estimate the number of true null hypotheses over a large number of hypotheses. The estimated number of true null hypotheses is then used to improve the power of FWE- or FDR-controlling methods. Monte Carlo simulations are conducted to evaluate the performance of these methods. The lowest slope method, developed by Benjamini and Hochberg (2000) on the adaptive control of the FDR in multiple testing with independent statistics, and the mean of differences method appear to perform the best. These two methods control the FWE properly when the number of nontrue null hypotheses is small. A data set from a toxicogenomic microarray experiment is used for illustration.

Animals↗

PubMed2003

Evaluation of cell proliferation in rat tissues with BrdU, PCNA, Ki-67(MIB-5) immunohistochemistry and in situ hybridization for histone mRNA.

The standard method for assessment of cell proliferation in paraffin-embedded tissue sections is 5-bromodeoxyuridine (BrdU) immunohistochemistry (IHC). BrdU can be administered to laboratory animals via IP injections, is readily incorporated into nuclei during the DNA synthetic phase of the cell cycle, and is detected with an anti-BrdU antibody. This method has several disadvantages, and an accurate method for evaluation of proliferative activity that can substitute for BrdU IHC, when necessary, is of great interest to investigators. Alternative methods for detection of proliferating cells in tissue sections are proliferating cell nuclear antigen (PCNA) IHC, Ki-67 IHC, and in situ hybridization (ISH) for histone mRNA. To determine the optimal choice, we analyzed the correlation of anti-PCNA, anti-Ki-67(MIB-5), and histone mRNA labeling indices (LIs) with anti-BrdU LI in rat highly replicative (renewing) tissues. The correlation between anti-BrdU and histone mRNA LIs, as well as the correlation between anti-BrdU and anti-Ki-67 LIs, was statistically significant. There was no significant correlation between anti-BrdU and anti-PCNA LIs. These results suggest that both ISH for histone mRNA and IHC with MIB-5 are preferable techniques for assessment of cell proliferation in rat paraffin-embedded renewing tissues compared to PCNA IHC. They can substitute for BrdU IHC when necessary.

Animals↗

PubMed2002

A procedure for developing risk-based reference doses.

Reference doses (RfDs) for toxic substances based on a no observed adverse effect level (NOAEL) or lowest observed adverse effect level (LOAEL) are established to restrict human exposures to only nontoxic or minimally toxic levels. In order to calculate a risk-based RfD, for the point of departure it is necessary to replace a NOAEL or LOAEL by a benchmark dose (BMD) estimated to be associated with a specified level of estimable risk in or near the low end of an experimental dose range. Then the RfD is calculated by dividing the BMD by a series of uncertainty factors. Among these uncertainty factors is one for interindividual sensitivity, typically assigned a value of 10. If information is available on interindividual sensitivity, this default factor can be replaced with a factor expected to provide protection for a specified proportion of a population. Examination of published databases suggests interindividual effects often to be approximately log-normal. For example, in order to illustrate the procedure for establishing a risk-based RfD, a standard deviation of the logarithm (base e) of individual sensitivity of 1.7 was selected, i.e., a factor of 5.5. This value is near the upper range of values reported in the literature (D. Hattis et al., 1999, in "Characterizing Human Variability in the Risk Assessment Process," ILSI Press, Washington, DC). Using this information in combination with an RfD based on a benchmark dose associated with a specified level of risk, the risk at the RfD can be estimated. For example, a benchmark dose associated with a risk of 10% divided by 60, to account for interindividual variation, is expected to limit risk at the RfD to about 1 in 10,000. If the standard deviation of the logarithm (base e) of individual sensitivity is 1.2, a more typical value, the divisor is approximately 20. These would replace an RfD having an unknown risk based on the LOAEL divided by 100. Or, an RfD with a specified level of risk could be estimated. The estimate of risk can be improved for a specific case by replacing an overall estimate of the standard deviation for interindividual variability by an estimate of the standard deviation for a particular class of chemicals and/or biological endpoint, if available. Risks can be substantially lower for smaller values of interindividual variability. Determination of an RfD still would require an additional uncertainty factor for animal to human extrapolation.

Adult↗