Dataset Information

P53 and the Carcinogenicity of Chronic Inflammation.

ABSTRACT: Chronic inflammation is a major cancer predisposition factor. Constitutive activation of the inflammation-driving NF-?B pathway commonly observed in cancer or developed in normal tissues because of persistent infections or endogenous tissue irritating factors, including products of secretion by senescent cells accumulating with age, markedly represses p53 functions. In its turn, p53 acts as a suppressor of inflammation helping to keep it within safe limits. The antagonistic relationship between p53 and NF-?B is controlled by multiple mechanisms and reflects cardinal differences in organismal responses to intrinsic and extrinsic cell stresses driven by these two transcription factors, respectively. This provides an opportunity for developing drugs to treat diseases associated with inappropriate activity of either p53 or NF-?B through targeting the opposing pathway. Several drug candidates of this kind are currently in clinical testing. These include anticancer small molecules capable of simultaneous suppression of p53 and activation of NF-?B and NF-?B-activating biologics that counteract p53-mediated pathologies associated with systemic genotoxic stresses such as acute radiation syndrome and side effects of cancer treatment.

SUBMITTER: Gudkov AV

PROVIDER: S-EPMC5088512 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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p53 and the Carcinogenicity of Chronic Inflammation.

Gudkov Andrei V AV Komarova Elena A EA

Cold Spring Harbor perspectives in medicine 20161101 11

Chronic inflammation is a major cancer predisposition factor. Constitutive activation of the inflammation-driving NF-κB pathway commonly observed in cancer or developed in normal tissues because of persistent infections or endogenous tissue irritating factors, including products of secretion by senescent cells accumulating with age, markedly represses p53 functions. In its turn, p53 acts as a suppressor of inflammation helping to keep it within safe limits. The antagonistic relationship between ...[more]

PMID: 27549311

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Project description:BackgroundOne of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration.ResultsModels for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B.ConclusionCarcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible.

Dataset Information

P53 and the Carcinogenicity of Chronic Inflammation.

Publications

p53 and the Carcinogenicity of Chronic Inflammation.

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