Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project-Reference-Cited by-同舟云学术

Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project

Published:2023-12-02 Issue:12 Volume:34 Page:983-1001
ISSN:1062-936X
Container-title:SAR and QSAR in Environmental Research
language:en
Short-container-title:SAR and QSAR in Environmental Research

Author:

Furuhama A.¹^ORCID,Kitazawa A.¹,Yao J.²,Matos dos Santos C.E.³^ORCID,Rathman J.⁴^ORCID,Yang C.⁴^ORCID,Ribeiro J.V.⁴,Cross K.⁵^ORCID,Myatt G.⁵,Raitano G.⁶^ORCID,Benfenati E.⁶^ORCID,Jeliazkova N.⁷^ORCID,Saiakhov R.⁸^ORCID,Chakravarti S.⁸^ORCID,Foster R.S.⁹^ORCID,Bossa C.¹⁰^ORCID,Battistelli C. Laura¹⁰^ORCID,Benigni R.¹⁰¹¹^ORCID,Sawada T.¹²¹³^ORCID,Wasada H.¹²^ORCID,Hashimoto T.¹²^ORCID,Wu M.¹⁴,Barzilay R.¹⁴,Daga P.R.¹⁵^ORCID,Clark R.D.¹⁵^ORCID,Mestres J.¹⁶^ORCID,Montero A.¹⁶,Gregori-Puigjané E.¹⁶,Petkov P.¹⁷,Ivanova H.¹⁷,Mekenyan O.¹⁷,Matthews S.¹⁸^ORCID,Guan D.¹⁸^ORCID,Spicer J.¹⁸^ORCID,Lui R.¹⁸^ORCID,Uesawa Y.¹⁹^ORCID,Kurosaki K.¹⁹,Matsuzaka Y.¹⁹,Sasaki S.¹⁹,Cronin M.T.D.²⁰^ORCID,Belfield S.J.²⁰^ORCID,Firman J.W.²⁰^ORCID,Spînu N.²⁰^ORCID,Qiu M.²¹,Keca J.M.²¹,Gini G.²²^ORCID,Li T.²³,Tong W.²³,Hong H.²³^ORCID,Liu Z.²³²⁴,Igarashi Y.²⁵^ORCID,Yamada H.²⁵,Sugiyama K.-I.¹,Honma M.¹

Affiliation:

1. Division of Genetics and Mutagenesis (DGM), National Institute of Health Sciences (NIHS), Kawasaki, Japan

2. Key Laboratory of Fluorine and Nitrogen Chemistry and Advanced Materials (Chinese Academy of Sciences), Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences (SIOC, CAS), Shanghai, China

3. Department of Computational Toxicology and In Silico Innovations, Altox Ltd, São Paulo-SP, Brazil

4. MN-AM, Nuremberg, Germany/Columbus, OH, USA

5. In Silico Department, Instem, Conshohocken, PA, USA

6. Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS (IRFMN), Milano, Italy

7. IdeaConsult Ltd, Sofia, Bulgaria

8. MultiCASE Inc, Mayfield Height, OH, USA

9. Lhasa Ltd, Leeds, UK

10. Environment and Health Department, Istituto Superiore di Sanità (ISS), Rome, Italy

11. Alpha‐PreTox, Rome, Italy

12. Faculty of Regional Studies, Gifu University, Gifu, Japan

13. xenoBiotic Inc, Gifu, Japan

14. Massachusetts Institute of Technology, Cambridge, MA, USA

15. Simulations Plus, Lancaster, CA, USA

16. Chemotargets, Barcelona, Spain

17. LMC - Bourgas University, Bourgas, Bulgaria

18. Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

19. Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan

20. School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK

21. Evergreen AI, Inc, Toronto, Canada

22. Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy

23. Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (NCTR/FDA), Jefferson, AR, USA

24. Integrative Toxicology, Nonclinical Drug Safety, Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, CT, USA

25. Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Osaka, Japan

Funder

Ministry of Health, Labour and Welfare

Publisher

Informa UK Limited

Subject

Drug Discovery,Molecular Medicine,General Medicine,Bioengineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/1062936X.2023.2284902

Reference21 articles.

1. ICH-M7 (R1) ICH Harmonized Guideline Assessment and control Of DNA reactive (Mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk Current step 4 version dated 31 March 2017 2017. Available at https://database.ich.org/sites/default/files/M7_R1_Guideline.pdf.

2. Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across

3. Benchmark Data Set for in Silico Prediction of Ames Mutagenicity

4. Can in vitro mammalian cell genotoxicity test results be used to complement positive results in the Ames test and help predict carcinogenic or in vivo genotoxic activity? I. Reports of individual databases presented at an EURL ECVAM Workshop

5. Can in vitro mammalian cell genotoxicity test results be used to complement positive results in the Ames test and help predict carcinogenic or in vivo genotoxic activity? II. Construction and analysis of a consolidated database

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