Evaluation of the ability of a battery of three in vitro genotoxicity tests to discriminate rodent carcinogens and non-carcinogens

Kirkland, David; Speit, Günter

doi:10.1016/j.mrgentox.2008.05.002

Cited by 250 publications

(176 citation statements)

References 244 publications

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“…Overall, the low sensitivity of the UDS in detecting rodent carcinogens and/or in vivo genotoxicants highlighted by the analysis of the EURL ECVAM database confirms previous conclusions on the lower predictive value of the UDS compared with TGR and in vivo comet assays provided by the analysis of a smaller data set (Kirkland and Speit, 2008), supporting a more prominent role for the latter assays in regulatory testing strategies, as already recommended in most guidance documents (e.g. EFSA Scientific Committee, 2011;ECHA, 2017).…”

Section: Methodologiessupporting

confidence: 84%

Clarification of some aspects related to genotoxicity assessment

Hardy¹,

Benford²,

Halldórsson³

et al. 2017

EFS2

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The European Commission requested EFSA to provide advice on the following: (1) the suitability of the unscheduled DNA synthesis (UDS) in vivo assay to follow-up positive results in in vitro gene mutation tests; (2) the adequacy to demonstrate target tissue exposure in in vivo studies, particularly in the mammalian erythrocyte micronucleus test; (3) the use of data in a weight-of-evidence approach to conclude on the genotoxic potential of substances and the consequent setting of health-based guidance values. The Scientific Committee concluded that the first question should be addressed in both a retrospective and a prospective way: for future assessments, it is recommended no longer performing the UDS test. For re-assessments, if the outcome of the UDS is negative, the reliability and significance of results should be carefully evaluated in a weight-of-evidence approach, before deciding whether more sensitive tests such as transgenic assay or in vivo comet assay would be needed to complete the assessment. Regarding the second question, the Scientific Committee concluded that it should be addressed in lines of evidence of bone marrow exposure: toxicity to the bone marrow in itself provides sufficient evidence to allow concluding on the validity of a negative outcome of a study. All other lines of evidence of target tissue exposure should be assessed within a weight-of-evidence approach. Regarding the third question, the Scientific Committee concluded that any available data that may assist in reducing the uncertainty in the assessment of the genotoxic potential of a substance should be taken into consideration. If the overall evaluation leaves no concerns for genotoxicity, health-based guidance values may be established. However, if concerns for genotoxicity remain, establishing health-based guidance values is not considered appropriate.

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Section: Methodologiessupporting

confidence: 84%

Clarification of some aspects related to genotoxicity assessment

Hardy¹,

Benford²,

Halldórsson³

et al. 2017

EFS2

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“…Moreover, the performance of both H-SVM and L-SVM is comparable to the five-fold cross validation rates given in the previous section and the reported 50-94% active agent and 80-99% non-active agent identification rates of various VS tools [20] and the 58.8% GT? and 79% GT-rates of Ames tests [12]. Strictly speaking, direct comparison with the reported performances is inappropriate because of the differences in the type, composition and diversity of compounds screened, and in the molecular descriptors, VS tools and their parameters used.…”

Section: Resultsmentioning

confidence: 99%

“…However, incorporation of these compounds is expected to introduce significantly higher level of noise into the training data because of the high-false-positive rates in in vitro data [12,13]. In vitro methods such as Ames tend to show limited sensitivity of 58.…”

Section: Introductionmentioning

confidence: 99%

“…In vitro methods such as Ames tend to show limited sensitivity of 58. .7% and specificity of 30.8-73.9% when used individually, and improved sensitivity of 75.3-92.3% but reduced specificity of 5.0-34.6% when used in combinations, leading to high false-positive rates of 26.1-69.2% for individual and 65.4-95.0% for combination tests [12]. Although factors contributing to and strategies for overcoming false-positives in in vitro data have been discussed [13], and progress has been made in improving the sensitivity and specificity of in vitro methods [14] and in high-throughput [15] tests, high volume in vitro data of sufficiently low false-positive rate is not yet available.…”

Section: Introductionmentioning

confidence: 99%

“…compounds [6,17,18], and are thus expected to be significantly higher in accuracy than in vitro methods. Among various in vitro tests Ames tests has shown the best performance [12]. A dataset of lower percentage of false positives and thus lower noise levels can be constructed from the data derived from these two more accurate assay types.…”

Section: Introductionmentioning

confidence: 99%

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Effect of training data size and noise level on support vector machines virtual screening of genotoxic compounds from large compound libraries

Kumar

Liu

et al. 2011

J Comput Aided Mol Des

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Various in vitro and in-silico methods have been used for drug genotoxicity tests, which show limited genotoxicity (GT+) and non-genotoxicity (GT-) identification rates. New methods and combinatorial approaches have been explored for enhanced collective identification capability. The rates of in-silco methods may be further improved by significantly diversified training data enriched by the large number of recently reported GT+ and GT- compounds, but a major concern is the increased noise levels arising from high false-positive rates of in vitro data. In this work, we evaluated the effect of training data size and noise level on the performance of support vector machines (SVM) method known to tolerate high noise levels in training data. Two SVMs of different diversity/noise levels were developed and tested. H-SVM trained by higher diversity higher noise data (GT+ in any in vivo or in vitro test) outperforms L-SVM trained by lower noise lower diversity data (GT+ in in vivo or Ames test only). H-SVM trained by 4,763 GT+ compounds reported before 2008 and 8,232 GT- compounds excluding clinical trial drugs correctly identified 81.6% of the 38 GT+ compounds reported since 2008, predicted 83.1% of the 2,008 clinical trial drugs as GT-, and 23.96% of 168 K MDDR and 27.23% of 17.86M PubChem compounds as GT+. These are comparable to the 43.1-51.9% GT+ and 75-93% GT- rates of existing in-silico methods, 58.8% GT+ and 79% GT- rates of Ames method, and the estimated percentages of 23% in vivo and 31-33% in vitro GT+ compounds in the "universe of chemicals". There is a substantial level of agreement between H-SVM and L-SVM predicted GT+ and GT- MDDR compounds and the prediction from TOPKAT. SVM showed good potential in identifying GT+ compounds from large compound libraries based on higher diversity and higher noise training data.

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Alkylpyridines and Miscellaneous Organic Nitrogen Compounds

Peterson

2023

Patty's Toxicology

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This chapter provides a broad overview of topics related to the occupational use and exposure to a variety of miscellaneous nitrogen‐containing compounds. Topics addressed include chemical/physical properties, uses, exposure assessment, and toxic effects, as well as standards, regulations, and guidelines for exposure. Some of the chemicals and classes represented in this chapter include pyridine, alkylpyridines, vinylpyridines, aminopyridines, aminopicolines, pyridinethione compounds (pyrithiones), nicotine, the substituted uracils, quaternary herbicides, s ‐triazine herbicides, propazine, cyanazine, ametryn, prometryn, simazine, hexazinone, azides, and hydrazine and its derivatives.

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Evaluation of the ability of a battery of three in vitro genotoxicity tests to discriminate rodent carcinogens and non-carcinogens

Cited by 250 publications

References 244 publications

Clarification of some aspects related to genotoxicity assessment

Clarification of some aspects related to genotoxicity assessment

Effect of training data size and noise level on support vector machines virtual screening of genotoxic compounds from large compound libraries

Alkylpyridines and Miscellaneous Organic Nitrogen Compounds

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