Dataset Information

An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts.

ABSTRACT:

Background

Mutagenicity is the capability of a substance to cause genetic mutations. This property is of high public concern because it has a close relationship with carcinogenicity and potentially with reproductive toxicity. Experimentally, mutagenicity can be assessed by the Ames test on Salmonella with an estimated experimental reproducibility of 85%; this intrinsic limitation of the in vitro test, along with the need for faster and cheaper alternatives, opens the road to other types of assessment methods, such as in silico structure-activity prediction models.A widely used method checks for the presence of known structural alerts for mutagenicity. However the presence of such alerts alone is not a definitive method to prove the mutagenicity of a compound towards Salmonella, since other parts of the molecule can influence and potentially change the classification. Hence statistically based methods will be proposed, with the final objective to obtain a cascade of modeling steps with custom-made properties, such as the reduction of false negatives.

Results

A cascade model has been developed and validated on a large public set of molecular structures and their associated Salmonella mutagenicity outcome. The first step consists in the derivation of a statistical model and mutagenicity prediction, followed by further checks for specific structural alerts in the "safe" subset of the prediction outcome space. In terms of accuracy (i.e., overall correct predictions of both negative and positives), the obtained model approached the 85% reproducibility of the experimental mutagenicity Ames test.

Conclusions

The model and the documentation for regulatory purposes are freely available on the CAESAR website. The input is simply a file of molecular structures and the output is the classification result.

SUBMITTER: Ferrari T

PROVIDER: S-EPMC2913329 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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Publications

An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts.

Ferrari Thomas T Gini Giuseppina G

Chemistry Central journal 20100729

<h4>Background</h4>Mutagenicity is the capability of a substance to cause genetic mutations. This property is of high public concern because it has a close relationship with carcinogenicity and potentially with reproductive toxicity. Experimentally, mutagenicity can be assessed by the Ames test on Salmonella with an estimated experimental reproducibility of 85%; this intrinsic limitation of the in vitro test, along with the need for faster and cheaper alternatives, opens the road to other types ...[more]

PMID: 20678181

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Project description:Greater investment is required in developing new drugs and vaccines against malaria in order to eradicate malaria. These precious funds must be carefully managed to achieve the greatest impact. We evaluate existing efforts to discover and develop new drugs and vaccines for malaria to determine how best malaria R&D can benefit from an enhanced open source approach and how such a business model may operate. We assess research articles, patents, clinical trials and conducted a smaller survey among malaria researchers. Our results demonstrate that the public and philanthropic sectors are financing and performing the majority of malaria drug/vaccine discovery and development, but are then restricting access through patents, 'closed' publications and hidden away physical specimens. This makes little sense since it is also the public and philanthropic sector that purchases the drugs and vaccines. We recommend that a more "open source" approach is taken by making the entire value chain more efficient through greater transparency which may lead to more extensive collaborations. This can, for example, be achieved by empowering an existing organization like the Medicines for Malaria Venture (MMV) to act as a clearing house for malaria-related data. The malaria researchers that we surveyed indicated that they would utilize such registry data to increase collaboration. Finally, we question the utility of publicly or philanthropically funded patents for malaria medicines, where little to no profits are available. Malaria R&D benefits from a publicly and philanthropically funded architecture, which starts with academic research institutions, product development partnerships, commercialization assistance through UNITAID and finally procurement through mechanisms like The Global Fund to Fight AIDS, Tuberculosis and Malaria and the U.S.' President's Malaria Initiative. We believe that a fresh look should be taken at the cost/benefit of patents particularly related to new malaria medicines and consider alternative incentives, like WHO prequalification.

Dataset Information

An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts.

Background

Results

Conclusions

Publications

An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts.

Similar Datasets

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