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Furuhama A, Kitazawa A, Yao J, Matos Dos Santos CE, Rathman J, Yang C, Ribeiro JV, Cross K, Myatt G, Raitano G, Benfenati E, Jeliazkova N, Saiakhov R, Chakravarti S, Foster RS, Bossa C, Battistelli CL, Benigni R, Sawada T, Wasada H, Hashimoto T, Wu M, Barzilay R, Daga PR, Clark RD, Mestres J, Montero A, Gregori-Puigjané E, Petkov P, Ivanova H, Mekenyan O, Matthews S, Guan D, Spicer J, Lui R, Uesawa Y, Kurosaki K, Matsuzaka Y, Sasaki S, Cronin MTD, Belfield SJ, Firman JW, Spînu N, Qiu M, Keca JM, Gini G, Li T, Tong W, Hong H, Liu Z, Igarashi Y, Yamada H, Sugiyama KI, Honma M. Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project. SAR QSAR Environ Res 2023; 34:983-1001. [PMID: 38047445 DOI: 10.1080/1062936x.2023.2284902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/13/2023] [Indexed: 12/05/2023]
Abstract
Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.
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Affiliation(s)
- A Furuhama
- Division of Genetics and Mutagenesis (DGM), National Institute of Health Sciences (NIHS), Kawasaki, Japan
| | - A Kitazawa
- Division of Genetics and Mutagenesis (DGM), National Institute of Health Sciences (NIHS), Kawasaki, Japan
| | - J Yao
- 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
| | - C E Matos Dos Santos
- Department of Computational Toxicology and In Silico Innovations, Altox Ltd, São Paulo-SP, Brazil
| | - J Rathman
- MN-AM, Nuremberg, Germany/Columbus, OH, USA
| | - C Yang
- MN-AM, Nuremberg, Germany/Columbus, OH, USA
| | | | - K Cross
- In Silico Department, Instem, Conshohocken, PA, USA
| | - G Myatt
- In Silico Department, Instem, Conshohocken, PA, USA
| | - G Raitano
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS (IRFMN), Milano, Italy
| | - E Benfenati
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS (IRFMN), Milano, Italy
| | | | | | | | | | - C Bossa
- Environment and Health Department, Istituto Superiore di Sanità (ISS), Rome, Italy
| | - C Laura Battistelli
- Environment and Health Department, Istituto Superiore di Sanità (ISS), Rome, Italy
| | - R Benigni
- Environment and Health Department, Istituto Superiore di Sanità (ISS), Rome, Italy
- Alpha-PreTox, Rome, Italy
| | - T Sawada
- Faculty of Regional Studies, Gifu University, Gifu, Japan
- xenoBiotic Inc, Gifu, Japan
| | - H Wasada
- Faculty of Regional Studies, Gifu University, Gifu, Japan
| | - T Hashimoto
- Faculty of Regional Studies, Gifu University, Gifu, Japan
| | - M Wu
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - R Barzilay
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - P R Daga
- Simulations Plus, Lancaster, CA, USA
| | - R D Clark
- Simulations Plus, Lancaster, CA, USA
| | | | | | | | - P Petkov
- LMC - Bourgas University, Bourgas, Bulgaria
| | - H Ivanova
- LMC - Bourgas University, Bourgas, Bulgaria
| | - O Mekenyan
- LMC - Bourgas University, Bourgas, Bulgaria
| | - S Matthews
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - D Guan
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - J Spicer
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - R Lui
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Y Uesawa
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan
| | - K Kurosaki
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan
| | - Y Matsuzaka
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan
| | - S Sasaki
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan
| | - M T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - S J Belfield
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - J W Firman
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - N Spînu
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - M Qiu
- Evergreen AI, Inc, Toronto, Canada
| | - J M Keca
- Evergreen AI, Inc, Toronto, Canada
| | - G Gini
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy
| | - T Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (NCTR/FDA), Jefferson, AR, USA
| | - W Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (NCTR/FDA), Jefferson, AR, USA
| | - H Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (NCTR/FDA), Jefferson, AR, USA
| | - Z Liu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (NCTR/FDA), Jefferson, AR, USA
- Integrative Toxicology, Nonclinical Drug Safety, Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, CT, USA
| | - Y Igarashi
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Osaka, Japan
| | - H Yamada
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Osaka, Japan
| | - K-I Sugiyama
- Division of Genetics and Mutagenesis (DGM), National Institute of Health Sciences (NIHS), Kawasaki, Japan
| | - M Honma
- Division of Genetics and Mutagenesis (DGM), National Institute of Health Sciences (NIHS), Kawasaki, Japan
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