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  • 1.
    Honma, Masamitsu
    et al.
    Division of Genetics and Mutagenesis, National Institute of Health Sciences, Kawasaki Ku, Japan.
    Kitazawa, Airi
    Division of Genetics and Mutagenesis, National Institute of Health Sciences, Kawasaki Ku, Japan.
    Cayley, Alex
    Lhasa Limited, Leeds, England.
    Williams, Richard V.
    Lhasa Limited, Leeds, England.
    Barber, Chris
    Lhasa Limited, Leeds, England.
    Hanser, Thierry
    Lhasa Limited, Leeds, England.
    Saiakhov, Roustem
    MultiCASE Inc., Beachwood, USA.
    Chakravarti, Suman
    MultiCASE Inc., Beachwood, USA.
    Myatt, Glenn J.
    Leadscope Inc., Columbus, USA.
    Cross, Kevin P.
    Leadscope Inc., Columbus, USA.
    Benfenati, Emilio
    Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy.
    Raitano, Giuseppa
    Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy.
    Mekenyan, Ovanes
    Laboratory of Mathematical Chemistry, As Zlatarov University, Bourgas, Bulgaria.
    Petkov, Petko
    Laboratory of Mathematical Chemistry, As Zlatarov University, Bourgas, Bulgaria.
    Bossa, Cecilia
    Istituto Superiore di Sanita', Rome, Italy.
    Benigni, Romualdo
    Istituto Superiore di Sanita', Rome, Italy; Alpha-Pretox, Rome, Italy.
    Battistelli, Chiara Laura
    Istituto Superiore di Sanita', Rome, Italy.
    Giuliani, Alessandro
    Istituto Superiore di Sanita', Rome, Italy.
    Tcheremenskaia, Olga
    Istituto Superiore di Sanita', Rome, Italy.
    DeMeo, Christine
    Prous Institute, Barcelona, Spain.
    Norinder, Ulf
    Unit of Toxicology Sciences, Karolinska Institute, Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
    Koga, Hiromi
    Fujitsu Kyushu Systems Limited, Fukuoka, Japan.
    Jose, Ciloy
    Fujitsu Kyushu Systems Limited, Fukuoka, Japan.
    Jeliazkova, Nina
    IdeaConsult Ltd., Sofia, Bulgaria.
    Kochev, Nikolay
    IdeaConsult Ltd., Sofia, Bulgaria; Department of Analytical Chemistry and Computer Chemistry, University of Plovdiv, Plovdiv, Bulgaria.
    Paskaleva, Vesselina
    Department of Analytical Chemistry and Computer Chemistry, University of Plovdiv, Plovdiv, Bulgaria.
    Yang, Chihae
    Molecular Networks GmbH, Nürnberg, Germany; Altamira LLC, Columbus, USA.
    Daga, Pankaj R.
    Simulations Plus Inc., Lancaster, USA.
    Clark, Robert D.
    Simulations Plus Inc., Lancaster, USA.
    Rathman, James
    Molecular Networks GmbH, Nürnberg, Germany; Altamira LLC, Columbus, USA; Ohio State University, Columbus, USA.
    Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project2019In: Mutagenesis, ISSN 0267-8357, E-ISSN 1464-3804, Vol. 34, no 1, p. 3-16Article in journal (Refereed)
    Abstract [en]

    The International Conference on Harmonization (ICH) M7 guideline allows the use of in silicoapproaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure–activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis,National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.

  • 2.
    Norinder, Ulf
    et al.
    Swetox, Unit of Toxicology Sciences, Karolinska Institute, Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
    Ahlberg, Ernst
    AstraZeneca R&D Gothenburg, Mölndal, Sweden.
    Carlsson, Lars
    Computer Learning Research Centre, University of London Egham, Surrey, England.
    Predicting Ames Mutagenicity Using Conformal Prediction in the Ames/QSAR International Challenge Project2019In: Mutagenesis, ISSN 0267-8357, E-ISSN 1464-3804, Vol. 34, no 1, p. 33-40Article in journal (Refereed)
    Abstract [en]

    Valid and predictive models for classifying Ames mutagenicity have been developed using conformal prediction. The models are Random Forest models using signature molecular descriptors. The investigation indicates, on excluding not-strongly mutagenic compounds (class B), that the validity for mutagenic compounds is increased for the predictions based on both public and the Division of Genetics and Mutagenesis, National Institute of Health Sciences of Japan (DGM/NIHS) data while less so when using only the latter data source. The former models only result in valid predictions for the majority, non-mutagenic, class whereas the latter models are valid for both classes, i.e. mutagenic and non-mutagenic compounds. These results demonstrate the importance of data consistency manifested through the superior predictive quality and validity of the models based only on DGM/NIHS generated data compared to a combination of this data with public data sources.

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