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V Chankong

Publications and source records attributed to V Chankong.

9 recordsLinked to original sources
PubMed1993

MRI gradient waveform design by numerical optimization.

This manuscript describes a method of gradient waveform design by nonlinear constrained optimization. Methods of formulation and solution of the waveform optimization problem are briefly described for minimization of root mean squared current and minimization of waveform moments. Waveforms generated using these objectives are presented and compared with those obtained with other objectives. The method uses waveforms which are defined as a set of discrete amplitudes in order to remove artificial constraints on waveform shape imposed by "multilobe" designs. These point-to-point amplitudes are the parameters determined in the optimization procedure which includes knowledge of the specific imaging conditions and the specific gradient hardware system. Some beneficial results of this design approach are: a) physically realizable waveforms which optimally achieve specific imaging and motion artifact reduction goals, b) waveforms which are guaranteed to be optimal with respect to one of several possible objective, c) less reliance on the experience of the designer, and d) a potential reduction in waveform design time.

Humans↗
PubMed1986

Invited contribution: an objective approach to the development of short-term tests predictive of carcinogenicity.

The Carcinogenicity Prediction and Battery Selection procedure was developed to address two problems: (1) the identification of highly predictive, yet cost-effective, batteries of short-term tests and (2) the objective prediction of the potential carcinogenicity of chemicals based upon the results of short-term tests even when a mixture of positive and negative results is obtained. In the present report the usefulness of the Carcinogenicity Prediction and Battery Selection procedure is demonstrated using benzo[a]pyrene, benzoin and diethylstilbestrol as examples. In addition, its applicability in the analysis of all the possible outcomes of a battery is illustrated together with an analysis of the worth of additional testing.

Animals↗
PubMed1985

The carcinogenicity prediction and battery selection (CPBS) method: a Bayesian approach.

Recently, a large number of relatively inexpensive in vitro short-term tests have been developed to help predict the carcinogenicity of chemicals. The carcinogenicity prediction and battery selection (CPBS) method utilizes the results of such short-term tests to screen for chemicals that are most likely to cause cancer. The method is an integrated approach for analyzing large, often sparsely filled, data bases containing short-term test results, which often have only marginal representation of known non-carcinogens. The CPBS method is developed for the purpose of (i) determining the reliability and predictive capability of individual and batteries of short-term tests, and (ii) developing a strategy for formulating and selecting optimally preferred batteries of short-term tests for screening chemicals for further testing. The term 'optimally preferred' connotes the best acceptable combination of tests in terms of trade-offs among the multiple attributes of each test and resulting battery (e.g., cost, sensitivity, specificity, etc). The CPBS method consists of 5 major tasks: (1) data consolidation, (2) parameter estimation, (3) predictivity calculation, (4) battery selection and (5) risk assessment. Although there is a great need for more research and improvement, the CPBS method at its present stage should add an important method to the maze of the thousands of new chemicals that are introduced into drugs, foods, consumer goods and to the environment every year. This method should also provide an enhanced identification procedure for classifying chemicals more accurately as suspected carcinogens or non-carcinogens.

Carcinogens↗
PubMed1985

Cluster analysis in predicting the carcinogenicity of chemicals using short-term assays.

Cluster analysis can be a useful tool for exploratory data analysis to uncover natural groupings in data, and initiate new ideas and hypotheses about such groupings. When applied to short-term assay results, it provides and improves estimates for the sensitivity and specificity of assays, provides indications of association between assays and, in turn, which assays can be substituted for one another in a battery, and allows a data base containing test results on chemicals of unknown carcinogenicity to be linked to a data base for which animal carcinogenicity data are available. Cluster analysis was applied to the Gene-Tox data base (which contains short-term test results on chemicals of both known and unknown carcinogenicity). The results on chemicals of known carcinogenicity were different from those obtained when the entire data base was analyzed. This suggests that the associations (and possibly the sensitivities and specificities) which are based on chemicals of known carcinogenicity may not be representative of the true measures. Cluster analysis applied to the total data base should be useful in improving these estimates. Many of the associations between the assays which were found through the use of cluster analysis could be 'validated' based on previous knowledge of the mechanistic basis of the various tests, but some of the associations were unsuspected. These associations may be a reflection of a non-ideal data base. As additional data becomes available and new clustering techniques for handling non-ideal data bases are developed, results from such analyses could play an increasing role in strengthening prediction schemes which utilize short-term tests results to screen chemicals for carcinogenicity, such as the carcinogenicity and battery selection (CPBS) method (Chankong et al., 1985).

Carcinogens↗
PubMed1985

Application of the carcinogenicity prediction and battery selection (CPBS) method to the Gene-Tox data base.

The carcinogenicity prediction and battery selection (CPBS) method (Chankong et al., 1985) utilizes the results of short-term tests to predict the carcinogenicity of chemicals and select batteries of tests that are capable of giving accurate predictions at reasonable costs. The CPBS method has been applied to the data compiled under the aegis of the Gene-Tox Program of the U.S. Environmental Protection Agency as a demonstration of the method on a typical data base. A number of batteries were selected by the methodology as having superior performance characteristics. The Bayesian predictions resulting from most of the selected 3-assay batteries were very good (greater than 90% of the carcinogens were correctly identified). It was also found that the 3-assay batteries of specified composition gave generally more accurate predictions than batteries of 4 or more assays of unspecified composition. A number of problems which may have affected our results have been identified: (1) the reliability of the sensitivities and specificities of the individual assays, (2) the prior probability that a chemical is a carcinogen was assumed to be 0.5, and (3) we have not (as yet) taken into account that some of the carcinogens are non-genotoxic and will produce false negative assays results. We are currently investigating approaches to take these factors into consideration. Our analysis also indicates that more testing of chemicals for carcinogenicity (especially probable non-carcinogens) is needed to further enhance the predictive capability of the CPBS method.

Carcinogens↗
PubMedDate not supplied

Carcinogenicity prediction and battery selection procedure: an in-depth analysis of cyclamate and its major metabolite cyclohexylamine.

The carcinogenicity prediction and battery selection (CPBS) method can be used to predict the probable carcinogenicity of a chemical based on the results of a battery of short-term assays. The method uses Bayesian statistics and the estimated performance characteristics of the assays (i.e., sensitivity and specificity). For routine use, the prior probability of carcinogenicity (or of noncarcinogenicity) is assumed to be unknown and is assigned a nondiscriminatory value of 0.5, i.e., the chemical is assumed to have an equal probability of being a carcinogen or a noncarcinogen, which implies that the expert's intuition regarding the chemical's potential as a carcinogen, based on structural features, known metabolic transformation, or potential electrophilicity, is not taken into consideration. In the present study, it is shown with cyclamate and its metabolite cyclohexlamine that when a battery of assays is used, assigning values to the prior probability between 0.1 and 0.9 has no significant effect on the predicted carcinogenicity. In the view of the fact that the performance of short-term tests is calibrated against known carcinogens and noncarcinogens, and since in the available data bases there is a preponderance of carcinogens, it may be argued that the estimation of sensitivities may be biased toward elevated values. It is shown, however, that when a battery of assays is used, assigning decreased values to the sensitivity does not result in significant effects on the predicted activity of the two test chemicals. Frequently, in the compilation of short-term results within a single assay, different laboratories may report varying results. Heretofore the "consensus result" was derived by majority rule. Because the variability in results may have biological significance, and in view of the fact that for a widely used sweetner one might be even more risk-adverse, a modification of Bayes's formula was derived to take these differences into consideration when calculating the probable carcinogenicity of cyclamate and its major metabolite.

Biotransformation↗