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D W Bristol

Publications and source records attributed to D W Bristol.

10 recordsLinked to original sources
PubMed1996

The NIEHS Predictive-Toxicology Evaluation Project.

The Predictive-Toxicology Evaluation (PTE) project conducts collaborative experiments that subject the performance of predictive-toxicology (PT) methods to rigorous, objective evaluation in a uniquely informative manner. Sponsored by the National Institute of Environmental Health Sciences, it takes advantage of the ongoing testing conducted by the U.S. National Toxicology Program (NTP) to estimate the true error of models that have been applied to make prospective predictions on previously untested, noncongeneric-chemical substances. The PTE project first identifies a group of standardized NTP chemical bioassays either scheduled to be conducted or are ongoing, but not yet complete. The project then announces and advertises the evaluation experiment, disseminates information about the chemical bioassays, and encourages researchers from a wide variety of disciplines to publish their predictions in peer-reviewed journals, using whatever approaches and methods they feel are best. A collection of such papers is published in this Environmental Health Perspectives Supplement, providing readers the opportunity to compare and contrast PT approaches and models, within the context of their prospective application to an actual-use situation. This introduction to this collection of papers on predictive toxicology summarizes the predictions made and the final results obtained for the 44 chemical carcinogenesis bioassays of the first PTE experiment (PTE-1) and presents information that identifies the 30 chemical carcinogenesis bioassays of PTE-2, along with a table of prediction sets that have been published to date. It also provides background about the origin and goals of the PTE project, outlines the special challenge associated with estimating the true error of models that aspire to predict open-system behavior, and summarizes what has been learned to date.

Animals↗
PubMed1995

Summary and recommendations for session B: activity classification and structure-activity relationship modeling for human health risk assessment of toxic substances.

The major theme of Session B explored and assessed the current status of activity-classification (AC)1 and structure-activity-relationship (SAR) methods developed to model adverse health effects that can result when biological systems are exposed to various chemical substances. The output from such models is intended to be used as information that supports risk assessments performed on toxic substances. Speakers gave special attention to the requirements and applications of hazard identification models. Specific aspects of the broad subject matter were augmented and explicated by audience and panel discussions during the 1.5 days available. This format stimulated the exchange of a surprisingly broad range of information and stimulating ideas. In order to gather the diverse aspects of Session B in one place, the Rapporteurs agreed that this summary would aim at providing a comprehensive overview, while Dr. Feldman's would amplify selected points of general interest.

Animals↗
PubMed1993

The induction of rules for predicting chemical carcinogenesis in rodents.

This paper presents results from an ongoing effort in applying a variety of induction-based methods to the problem of predicting the biological activity of noncongeneric (structurally dissimilar) chemicals. It describes initial experiments, the long-term goal of which is to assist toxicologists, cancer researchers, regulators, and others to predict the toxic effects of chemical compounds. We describe a series of experiments in tree and rule induction from a set of example chemicals whose carcinogenicity has been determined from long-term animal studies, and compare the resulting classification accuracy with eight published human and computer predictions for a common set of 44 test chemicals. The accuracy of our system is comparable to the most accurate human expert prediction yet published, and exceeds that of any of the computer-based predictions in the literature. The induced rules provide confirmation of current expert heuristic knowledge in this domain. These early results show that an inductive approach has excellent potential in predictive toxicology.

Animals↗
PubMed1988

Studies on the short-term toxicity of theophylline in rats and mice.

The purpose of these studies was to evaluate the short-term toxicity of theophylline, a compound present in tea and used in a variety of clinical applications. Fourteen-day repeated-dose toxicity studies were conducted in B6C3F1 mice and F344 rats of both sexes. Theophylline was administered in feed (0, 500, 1000, 2000, 4000, and 8000 ppm) or by gavage in corn oil (12.5-twice daily, 25, 50, 50-twice daily, 100, 200, 200-twice daily, and 400 mg/kg). Dosed-feed exposure to theophylline at concentrations up to 8000 ppm induced no significant toxicity except for dose-related uterine hypoplasia in rats. Palatability problems at that level precluded administration of higher concentrations. In the gavage study, 400 mg/kg was acutely toxic for both species, but mice and rats differed in that this same daily dose administered as two separate doses of 200 mg/kg was acutely toxic in rats but not in mice. No dose-related weight gain depression was evident in mice; weight gain was depressed in the majority of dose levels in rats and was pronounced at the higher levels. Clinical signs in mice were squinting and distended testes in males, and in rats, rapid respiration (all doses), squinting, and hunching. Gross necropsies, organ weights, clinical pathology, and pathology identified no target organs in mice, while histopathologic observations in rats suggested heart and stomach as possible target organs. Histopathologic effects in a number of other tissues, including lung, thymus, bone marrow, spleen, and uterus, were considered to reflect agonal changes in treated rats, possibly related to inanition. The results suggest that both species and sex differences exist with respect to sensitivity to theophylline toxicity, with F344 rats being more sensitive than B6C3F1 mice and male rats being more sensitive than female rats.

Animals↗
PubMedDate not supplied

Symbolic, neural, and Bayesian machine learning models for predicting carcinogenicity of chemical compounds.

Experimental programs have been underway for several years to determine the environmental effects of chemical compounds, mixtures, and the like. Among these programs is the National Toxicology Program (NTP) on rodent carcinogenicity. Because these experiments are costly and time-consuming, the rate at which test articles (i.e., chemicals) can be tested is limited. The ability to predict the outcome of the analysis at various points in the process would facilitate informed decisions about the allocation of testing resources. To assist human experts in organizing an empirical testing regime, and to try to shed light on mechanisms of toxicity, we constructed toxicity models using various machine learning and data mining methods, both existing and those of our own devising. These models took the form of decision trees, rule sets, neural networks, rules extracted from trained neural networks, and Bayesian classifiers. As a training set, we used recent results from rodent carcinogenicity bioassays conducted by the NTP on 226 test articles. We performed 10-way cross-validation on each of our models to approximate their expected error rates on unseen data. The data set consists of physical-chemical parameters of test articles, alerting chemical substructures, salmonella mutagenicity assay results, subchronic histopathology data, and information on route, strain, and sex/species for 744 individual experiments. These results contribute to the ongoing process of evaluating and interpreting the data collected from chemical toxicity studies.

Animals↗
PubMedDate not supplied

Chemical analysis of human blood for assessment of environmental exposure to semivolatile organochlorine chemical contaminants.

A chemical method for the quantitative analysis of organochlorine pesticide residues present in human blood was scaled-up to provide increased sensitivity and extended to include organochlorine industrial chemicals. Whole blood samples were extracted with hexane, concentrated, and analyzed without further cleanup by gas chromatography with electron capture detection. The methodology used was validated by conducting recovery studies at 1 and 10 ng/g (ppb) levels. Screening and confirmational analyses were performed by gas chromatography/mass spectrometry on samples collected from potentially exposed residents of the Love Canal area of Niagara Falls, New York and from volunteers in the Research Triangle Park area of North Carolina for 25 specific semivolatile organochlorine contaminants including chlorobenzene and chlorotoluene congeners, hexachloro-1,3-butadiene, pesticides, and polychlorinated biphenyls as Aroclor 1260. Dichlorobenzene, hexachlorobenzene, and beta-hexachlorocyclohexane residues fell in the range of 0.1 to 26 ppb in a high percentage of both the field and volunteer blood samples analyzed. Levels of other organochlorine compounds were either non-detectable or present in sub-ppb ranges.

Chromatography, Gas↗