Predicting carcinogenicity by using batteries of dependent short-term tests.

Kim, Byung Soo; Margolin, Barry H.

doi:10.1289/ehp.94102s1127

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…Statistical inference -the estimation of standard errors, confidence intervals and test sta-tistics for the comparison of indices obtained for two or more alternative methods -are based on the theory of proportions and binomial sampling. A classical example of the use of this approach has been the evaluation of the predictivity of carcinogenicity from the results of short-term tests for mutagenicity (69). The highs and lows of this validation are illustrative for the difficulties and the complexity of validations in general.…”

Section: Discussionmentioning

confidence: 99%

Biostatistical Methods for the Validation of Alternative Methods for In Vitro Toxicity Testing

Edler

Ittrich

2003

Altern Lab Anim

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Statistical methods for the validation of toxicological in vitro test assays are developed and applied. Validation is performed either in comparison with in vivo assays or in comparison with other in vitro assays of established validity. Biostatistical methods are presented which are of potential use and benefit for the validation of alternative methods for the risk assessment of chemicals, providing at least an equivalent level of protection through in vitro toxicity testing to that obtained through the use of current in vivo methods. Characteristic indices are developed and determined. Qualitative outcomes are characterised by the rates of false-positive and false-negative predictions, sensitivity and specificity, and predictive values. Quantitative outcomes are characterised by regression coefficients derived from predictive models. The receiver operating characteristics (ROC) technique, applicable when a continuum of cut-off values is considered, is discussed in detail, in relation to its use for statistical modelling and statistical inference. The methods presented are examined for their use for the proof of safety and for toxicity detection and testing. We emphasise that the final validation of toxicity testing is human toxicity, and that the in vivo test itself is only a predictor with an inherent uncertainty. Therefore, the validation of the in vitro test has to account for the vagueness and uncertainty of the “gold standard” in vivo test. We address model selection and model validation, and a four-step scheme is proposed for the conduct of validation studies. Gaps and research needs are formulated to improve the validation of alternative methods for in vitro toxicity testing.

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Section: Discussionmentioning

confidence: 99%

Biostatistical Methods for the Validation of Alternative Methods for In Vitro Toxicity Testing

Edler

Ittrich

2003

Altern Lab Anim

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“…The CPBS was applied to the Gene-Tox Database from the US. EPA and the carcinogenicity results from the National Toxicology Program (NTP), [83][84][85] and many CPBS techniques were applied in investigations using CASE/ MultiCASE (Section 5.8).…”

Section: Activity-activity Relationships (Aar)mentioning

confidence: 99%

Knowledge discovery and data mining in toxicology

Helma

Gottmann

Krämer

2000

Stat Methods Med Res

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Knowledge discovery and data mining tools are gaining increasing importance for the analysis of toxicological databases. This paper gives a survey of algorithms, capable to derive interpretable models from toxicological data, and presents the most important application areas. The majority of techniques in this area were derived from symbolic machine learning, one commercial product was developed especially for toxicological applications. The main application area is presently the detection of structure-activity relationships, very few authors have used these techniques to solve problems in epidemiological and clinical toxicology. Although the discussed algorithms are very flexible and powerful, further research is required to adopt the algorithms to the specific learning problems in this area, to develop improved representations of chemical and biological data and to enhance the interpretability of the derived models for toxicological experts.

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Evaluation of the standard battery of in vitro genotoxicity tests to predict in vivo genotoxicity through mathematical modeling: A report from the 8th International Workshop on Genotoxicity Testing

Luijten,

van Benthem,

Morita

et al. 2024

Environ and Mol Mutagen

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In human health risk assessment of chemicals and pharmaceuticals, identification of genotoxicity hazard usually starts with a standard battery of in vitro genotoxicity tests, which is needed to cover all genotoxicity endpoints. The individual tests included in the battery are not designed to pick up all endpoints. This explains why resulting data can appear contradictory, thereby complicating accurate interpretation of the findings. Such interpretation could be improved through application of mathematical modeling. One of the advantages of mathematical modeling is that the strengths and weaknesses of each test are taken into account. Furthermore, the generated predictions are objective and convey the associated uncertainties. This approach was explored by the working group “Predictivity of In Vitro Genotoxicity Testing,” convened in the context of the 8th International Workshop on Genotoxicity Testing (IWGT). Specifically, we applied mathematical modeling to a database with publicly available in vitro and in vivo data for genotoxicity. The results indicate that a mammalian in vitro clastogenicity test and a mammalian cell gene mutation test together provide strong predictive weight‐of‐evidence for evaluating genotoxic hazard of a substance, although they are better in predicting absence of genotoxic potential than in predicting presence of genotoxic potential. Remarkably, the bacterial reverse mutation (Ames) test did not significantly change these predictions when used in combination with in vitro mutagenicity and clastogenicity tests using cells of mammalian origin. However, in case only data from a bacterial reverse mutation test are available for the assessment of genotoxic potential, these do bear weight of evidence and thus can be used. Genotoxicity assays are generally executed in tiers, in which the bacterial reverse mutation test often is the starting point. Thus, it is reasonable to suspect that early in development test results from the bacterial reverse mutation test have influenced the composition of the database studied here. We performed several tests on the robustness of the database used for the analyses presented here, and the forthcoming results do not indicate a strong bias. Further research comparing in vitro genotoxicity data with in vivo data for additional compounds will provide more insights whether it is indeed time to reconsider the composition of the standard in vitro genotoxicity battery.

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Predicting carcinogenicity by using batteries of dependent short-term tests.

Cited by 8 publications

References 13 publications

Biostatistical Methods for the Validation of Alternative Methods for In Vitro Toxicity Testing

Biostatistical Methods for the Validation of Alternative Methods for In Vitro Toxicity Testing

Knowledge discovery and data mining in toxicology

Evaluation of the standard battery of in vitro genotoxicity tests to predict in vivo genotoxicity through mathematical modeling: A report from the 8th International Workshop on Genotoxicity Testing

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