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Tran TTV, Tayara H, Chong KT. AMPred-CNN: Ames mutagenicity prediction model based on convolutional neural networks. Comput Biol Med 2024; 176:108560. [PMID: 38754218 DOI: 10.1016/j.compbiomed.2024.108560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/15/2024] [Accepted: 05/05/2024] [Indexed: 05/18/2024]
Abstract
Mutagenicity assessment plays a pivotal role in the safety evaluation of chemicals, pharmaceuticals, and environmental compounds. In recent years, the development of robust computational models for predicting chemical mutagenicity has gained significant attention, driven by the need for efficient and cost-effective toxicity assessments. In this paper, we proposed AMPred-CNN, an innovative Ames mutagenicity prediction model based on Convolutional Neural Networks (CNNs), uniquely employing molecular structures as images to leverage CNNs' powerful feature extraction capabilities. The study employs the widely used benchmark mutagenicity dataset from Hansen et al. for model development and evaluation. Comparative analyses with traditional ML models on different molecular features reveal substantial performance enhancements. AMPred-CNN outshines these models, demonstrating superior accuracy, AUC, F1 score, MCC, sensitivity, and specificity on the test set. Notably, AMPred-CNN is further benchmarked against seven recent ML and DL models, consistently showcasing superior performance with an impressive AUC of 0.954. Our study highlights the effectiveness of CNNs in advancing mutagenicity prediction, paving the way for broader applications in toxicology and drug development.
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Affiliation(s)
- Thi Tuyet Van Tran
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea; Faculty of Information Technology, An Giang University, Long Xuyen 880000, Viet Nam; Vietnam National University-Ho Chi Minh City, Ho Chi Minh 700000, Viet Nam.
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea; Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea.
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2
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Djelassi I, Lancia P, Thuillier I, Ginestar J, Fioravanzo E, Baleydier A. Strategy proposal using QSAR models to approach mutagenicity assessment of non intentionally added substances in recycled plastic resins. Food Chem Toxicol 2024; 187:114597. [PMID: 38492856 DOI: 10.1016/j.fct.2024.114597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/20/2024] [Accepted: 03/12/2024] [Indexed: 03/18/2024]
Abstract
CONTEXT Transition to the use of recycled plastics raises an issue concerning safety assessment of Non Intentionally Added Substances (NIAS). To assess the mutagenic potential of the recycled polyethylene impurities and to evaluate the need to perform in vitro assays on recycled resins, this study lies in identifying existing NIAS associated with recycled Low/High Density Polyethylene and assessing the mutagenicity data-gaps by employing in silico tools. METHODS Quantitative Structure-Activity Relationship (QSAR) models predicting Ames mutagenicity were selected from literature, then NIAS were run to 1/evaluate performances of each model, 2/apply a QSAR strategy on the NIAS molecular space and address data-gaps. RESULTS Among the 165 NIAS identified, experimental Ames results were not found for 50 substances while the substances with experimental data were predominantly negatives. No individual model was able to predict all NIAS due to applicability domain limitations. Taking into account 1/calculated performances, 2/availability of applicability domain, 3/description of the Training Set, an Integrated Strategy was founded including Sarpy, Consensus and Protox to extend the applicability domain. CONCLUSION & PERSPECTIVES Existing data and predictions generated by this strategy suggest a low mutagenic potential of NIAS. Further investigation is needed to explore other genotoxicity mechanisms.
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Affiliation(s)
- Ishan Djelassi
- C.F.E.B Sisley Paris, 32 Avenue des Bethunes, 95310, Saint Ouen L'Aumône, France
| | - Pauline Lancia
- C.F.E.B Sisley Paris, 32 Avenue des Bethunes, 95310, Saint Ouen L'Aumône, France.
| | - Isabelle Thuillier
- C.F.E.B Sisley Paris, 32 Avenue des Bethunes, 95310, Saint Ouen L'Aumône, France
| | - José Ginestar
- C.F.E.B Sisley Paris, 32 Avenue des Bethunes, 95310, Saint Ouen L'Aumône, France
| | - Elena Fioravanzo
- ToxNavigation Ltd., Mole View, 158 Bridge Road, East Molesey, KT9 8HW, UK
| | - Aurélie Baleydier
- C.F.E.B Sisley Paris, 32 Avenue des Bethunes, 95310, Saint Ouen L'Aumône, France
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3
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Uesawa Y. Progress in Predicting Ames Test Outcomes from Chemical Structures: An In-Depth Re-Evaluation of Models from the 1st and 2nd Ames/QSAR International Challenge Projects. Int J Mol Sci 2024; 25:1373. [PMID: 38338650 PMCID: PMC10855369 DOI: 10.3390/ijms25031373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 01/19/2024] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
The Ames/quantitative structure-activity relationship (QSAR) International Challenge Projects, held during 2014-2017 and 2020-2022, evaluated the performance of various predictive models. Despite the significant insights gained, the rules allowing participants to select prediction targets introduced ambiguity in model performance evaluation. This reanalysis identified the highest-performing prediction model, assuming a 100% coverage rate (COV) for all prediction target compounds and an estimated performance variation due to changes in COV. All models from both projects were evaluated using balance accuracy (BA), the Matthews correlation coefficient (MCC), the F1 score (F1), and the first principal component (PC1). After normalizing the COV, a correlation analysis with these indicators was conducted, and the evaluation index for all prediction models in terms of the COV was estimated. In total, using 109 models, the model with the highest estimated BA (76.9) at 100% COV was MMI-VOTE1, as reported by Meiji Pharmaceutical University (MPU). The best models for MCC, F1, and PC1 were all MMI-STK1, also reported by MPU. All the models reported by MPU ranked in the top four. MMI-STK1 was estimated to have F1 scores of 59.2, 61.5, and 63.1 at COV levels of 90%, 60%, and 30%, respectively. These findings highlight the current state and potential of the Ames prediction technology.
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Affiliation(s)
- Yoshihiro Uesawa
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo 204-8588, Japan
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4
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Rossi S, Bussi S, Bonafè R, Incardona C, Vurro E, Visigalli M, Buonsanti F, Fretta R. Mutagenicity assessment of two potential impurities in preparations of 5-amino-2,4,6 triiodoisophthalic acid, a key intermediate in the synthesis of the iodinated contrast agent iopamidol. MUTATION RESEARCH. GENETIC TOXICOLOGY AND ENVIRONMENTAL MUTAGENESIS 2024; 893:503720. [PMID: 38272634 DOI: 10.1016/j.mrgentox.2023.503720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/23/2023] [Accepted: 11/24/2023] [Indexed: 01/27/2024]
Abstract
5-Aminoisophthalic acid and 5-nitroisophthalic acid (5-NIPA) are potential impurities in preparations of 5-amino-2,4,6-triiodoisophthalic acid, which is a key intermediate in the synthesis of the iodinated contrast agent iopamidol. We have studied their mutagenicity in silico (quantitative structure-activity relationships, QSAR) and by the bacterial reverse mutation assay (Ames test). First, the compounds were screened with the tools Derek Nexus™ and Leadscope®. Both compounds were flagged as potentially mutagenic (class 3 under ICH M7). However, contrary to the in silico prediction, neither chemical was mutagenic in the Ames test (plate incorporation method) with or without S9 metabolic activation.
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Affiliation(s)
- Silvia Rossi
- Bracco Imaging SpA, Bracco Research Centre, Via Ribes 5, 10010 Colleretto Giacosa, TO, Italy.
| | - Simona Bussi
- Bracco Imaging SpA, Bracco Research Centre, Via Ribes 5, 10010 Colleretto Giacosa, TO, Italy
| | - Roberta Bonafè
- Bracco Imaging SpA, Global Medical & Regulatory Affairs, Medical Writing, Via Ribes 5, 10010 Colleretto Giacosa, TO, Italy
| | - Carola Incardona
- Bracco Imaging SpA, Bracco Research Centre, Via Ribes 5, 10010 Colleretto Giacosa, TO, Italy
| | - Emanuela Vurro
- Bracco Imaging SpA, Bracco Research Centre, Via Ribes 5, 10010 Colleretto Giacosa, TO, Italy
| | - Massimo Visigalli
- Bracco Imaging SpA, Bracco Research Centre, Via Ribes 5, 10010 Colleretto Giacosa, TO, Italy
| | - Federica Buonsanti
- Bracco Imaging SpA, Bracco Research Centre, Via Ribes 5, 10010 Colleretto Giacosa, TO, Italy
| | - Roberta Fretta
- Bracco Imaging SpA, Bracco Research Centre, Via Ribes 5, 10010 Colleretto Giacosa, TO, Italy
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5
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Pandey SK, Roy K. Development of a read-across-derived classification model for the predictions of mutagenicity data and its comparison with traditional QSAR models and expert systems. Toxicology 2023; 500:153676. [PMID: 37993082 DOI: 10.1016/j.tox.2023.153676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/06/2023] [Accepted: 11/17/2023] [Indexed: 11/24/2023]
Abstract
Mutagenicity is considered an important endpoint from the regulatory, environmental and medical points of view. Due to the wide number of compounds that may be of concern and the enormous expenses (in terms of time, money, and animals) associated with rodent mutagenicity bioassays, this endpoint is a major target for the development of alternative approaches for screening and prediction. The majority of old-aged expert systems and quantitative structure-activity relationship (QSAR) models may show reduced performance over time for their application on newer chemical candidates; thus, researchers constantly try to improve the modeling strategies. In our report, we initially performed traditional classification-based linear discriminant analysis (LDA) QSAR modeling using the benchmark Ames dataset of diverse chemicals (6512 compounds) to recognize the relationship between the molecules and their potential mutagenic behavior. The classical LDA QSAR model is developed from a selected set of 2D descriptors. The LDA QSAR model was developed by using a total of 31 descriptors identified from the analysis of the most discriminating features. Additionally, we have used similarity-derived features obtained from the read-across (RA) to develop an RA-based QSAR model. The developed RA-based LDA QSAR model has better predictivity, transferability, and interpretability compared to the LDA QSAR model, and it uses a very small number of descriptors compared to the classical QSAR model. Different machine learning (ML) models were also developed using the descriptors appearing in the read-across-based LDA QSAR model for comparative studies. We have checked the prediction quality of 216 true external set compounds using the novel similarity-derived RA model. The performance of the OECD toolbox is also compared with the RA-derived LDA QSAR model for a true external set. The current study aimed to explore the significance of the read-across-based algorithm and its application to the most current experimental mutagenicity data to complement already available expert systems.
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Affiliation(s)
- Sapna Kumari Pandey
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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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 AND QSAR IN ENVIRONMENTAL RESEARCH 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] [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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Cayley AN, Foster RS, Brigo A, Muster W, Musso A, Kenyon MO, Parris P, White AT, Cohen-Ohana M, Nudelman R, Glowienke S. Assessing the utility of common arguments used in expert review of in silico predictions as part of ICH M7 assessments. Regul Toxicol Pharmacol 2023; 144:105490. [PMID: 37659712 DOI: 10.1016/j.yrtph.2023.105490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 08/24/2023] [Accepted: 08/30/2023] [Indexed: 09/04/2023]
Abstract
Expert review of two predictions, made by complementary (quantitative) structure-activity relationship models, to an overall conclusion is a key component of using in silico tools to assess the mutagenic potential of impurities as part of the ICH M7 guideline. In lieu of a specified protocol, numerous publications have presented best practise guides, often indicating the occurrence of common prediction scenarios and the evidence required to resolve them. A semi-automated expert review tool has been implemented in Lhasa Limited's Nexus platform following collation of these common arguments and assignment to the associated prediction scenarios made by Derek Nexus and Sarah Nexus. Using datasets primarily donated by pharmaceutical companies, an automated analysis of the frequency these prediction scenarios occur, and the likelihood of the associated arguments assigning the correct resolution, could then be conducted. This article highlights that a relatively small number of common arguments may be used to accurately resolve many prediction scenarios to a single conclusion. The use of a standardised method of argumentation and assessment of evidence for a given impurity is proposed to improve the efficiency and consistency of expert review as part of an ICH M7 submission.
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Affiliation(s)
- Alex N Cayley
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK
| | - Robert S Foster
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK.
| | - Alessandro Brigo
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Wolfgang Muster
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Alyssa Musso
- Pfizer Global Research and Development, Drug Safety Research and Development, Eastern Point Road, MS 8274/1317, Groton, CT, 06340, USA
| | - Michelle O Kenyon
- Pfizer Global Research and Development, Drug Safety Research and Development, Eastern Point Road, MS 8274/1317, Groton, CT, 06340, USA
| | - Patricia Parris
- Pfizer Worldwide Research and Development, Drug Safety Research and Development, Ramsgate Road, Sandwich, Kent, CT13 9NJ, UK
| | - Angela T White
- GlaxoSmithKline Pre-Clinical Development, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Mirit Cohen-Ohana
- Teva Pharmaceutical Industries Ltd, Dvora HaNevi'a Street 124, Tel Aviv-Yafo, 6944020, Israel
| | - Raphael Nudelman
- Teva Pharmaceutical Industries Ltd, Dvora HaNevi'a Street 124, Tel Aviv-Yafo, 6944020, Israel
| | - Susanne Glowienke
- Novartis AG, NIBR, Pre-clinical Safety, Fabrikstrasse 16, CH-405, Basel, Switzerland
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8
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Li T, Liu Z, Thakkar S, Roberts R, Tong W. DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application. Regul Toxicol Pharmacol 2023; 144:105486. [PMID: 37633327 DOI: 10.1016/j.yrtph.2023.105486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/14/2023] [Accepted: 08/23/2023] [Indexed: 08/28/2023]
Abstract
The Ames assay is required by the regulatory agencies worldwide to assess the mutagenic potential risk of consumer products. As well as this in vitro assay, in silico approaches have been widely used to predict Ames test results as outlined in the International Council for Harmonization (ICH) guidelines. Building on this in silico approach, here we describe DeepAmes, a high performance and robust model developed with a novel deep learning (DL) approach for potential utility in regulatory science. DeepAmes was developed with a large and consistent Ames dataset (>10,000 compounds) and was compared with other five standard Machine Learning (ML) methods. Using a test set of 1,543 compounds, DeepAmes was the best performer in predicting the outcome of Ames assay. In addition, DeepAmes yielded the best and most stable performance up to when compounds were >30% outside of the applicability domain (AD). Regarding the potential for regulatory application, a revised version of DeepAmes with a much-improved sensitivity of 0.87 from 0.47. In conclusion, DeepAmes provides a DL-powered Ames test predictive model for predicting the results of Ames tests; with its defined AD and clear context of use, DeepAmes has potential for utility in regulatory application.
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Affiliation(s)
- Ting Li
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA
| | - Zhichao Liu
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA
| | - Shraddha Thakkar
- Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA
| | - Ruth Roberts
- ApconiX Ltd, Alderley Park, Alderley Edge, SK10 4TG, UK; University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Weida Tong
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.
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9
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Khondkaryan L, Tevosyan A, Navasardyan H, Khachatrian H, Tadevosyan G, Apresyan L, Chilingaryan G, Navoyan Z, Stopper H, Babayan N. Datasets Construction and Development of QSAR Models for Predicting Micronucleus In Vitro and In Vivo Assay Outcomes. TOXICS 2023; 11:785. [PMID: 37755795 PMCID: PMC10537630 DOI: 10.3390/toxics11090785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/07/2023] [Accepted: 09/11/2023] [Indexed: 09/28/2023]
Abstract
In silico (quantitative) structure-activity relationship modeling is an approach that provides a fast and cost-effective alternative to assess the genotoxic potential of chemicals. However, one of the limiting factors for model development is the availability of consolidated experimental datasets. In the present study, we collected experimental data on micronuclei in vitro and in vivo, utilizing databases and conducting a PubMed search, aided by text mining using the BioBERT large language model. Chemotype enrichment analysis on the updated datasets was performed to identify enriched substructures. Additionally, chemotypes common for both endpoints were found. Five machine learning models in combination with molecular descriptors, twelve fingerprints and two data balancing techniques were applied to construct individual models. The best-performing individual models were selected for the ensemble construction. The curated final dataset consists of 981 chemicals for micronuclei in vitro and 1309 for mouse micronuclei in vivo, respectively. Out of 18 chemotypes enriched in micronuclei in vitro, only 7 were found to be relevant for in vivo prediction. The ensemble model exhibited high accuracy and sensitivity when applied to an external test set of in vitro data. A good balanced predictive performance was also achieved for the micronucleus in vivo endpoint.
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Affiliation(s)
- Lusine Khondkaryan
- Institute of Molecular Biology, NAS RA, Yerevan 0014, Armenia; (L.K.); (G.T.); (L.A.)
- Toxometris.ai, Yerevan 0009, Armenia; (A.T.); (H.N.); (Z.N.)
| | - Ani Tevosyan
- Toxometris.ai, Yerevan 0009, Armenia; (A.T.); (H.N.); (Z.N.)
- YerevaNN, Yerevan 0025, Armenia; (H.K.); (G.C.)
| | | | - Hrant Khachatrian
- YerevaNN, Yerevan 0025, Armenia; (H.K.); (G.C.)
- Department of Informatics and Applied Mathematics, Yerevan State University, Yerevan 0025, Armenia
| | - Gohar Tadevosyan
- Institute of Molecular Biology, NAS RA, Yerevan 0014, Armenia; (L.K.); (G.T.); (L.A.)
- Toxometris.ai, Yerevan 0009, Armenia; (A.T.); (H.N.); (Z.N.)
| | - Lilit Apresyan
- Institute of Molecular Biology, NAS RA, Yerevan 0014, Armenia; (L.K.); (G.T.); (L.A.)
- Toxometris.ai, Yerevan 0009, Armenia; (A.T.); (H.N.); (Z.N.)
| | | | - Zaven Navoyan
- Toxometris.ai, Yerevan 0009, Armenia; (A.T.); (H.N.); (Z.N.)
| | - Helga Stopper
- Institute of Pharmacology and Toxicology, University of Würzburg, 97078 Würzburg, Germany;
| | - Nelly Babayan
- Institute of Molecular Biology, NAS RA, Yerevan 0014, Armenia; (L.K.); (G.T.); (L.A.)
- Toxometris.ai, Yerevan 0009, Armenia; (A.T.); (H.N.); (Z.N.)
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10
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Feeney SV, Lui R, Guan D, Matthews S. Multiple Instance Learning Improves Ames Mutagenicity Prediction for Problematic Molecular Species. Chem Res Toxicol 2023; 36:1227-1237. [PMID: 37477941 DOI: 10.1021/acs.chemrestox.2c00372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
The prediction of Ames mutagenicity continues to be a concern in both regulatory and pharmacological toxicology. Traditional quantitative structure-activity relationship (QSAR) models of mutagenicity make predictions based on molecular descriptors calculated on a chemical data set used in their training. However, it is known that molecules such as aromatic amines can be non-mutagenic themselves but metabolically activated by S9 rodent liver enzyme in Ames tests forming molecules such as iminoquinones or amine substituents that better stabilize mutagenic nitrenium ions in known pathways of mutagenicity. Modern in silico modeling methods can implicitly model these metabolites through consideration of the structural elements relevant to their formation but do not include explicit modeling of these metabolites' potential activity. These metabolites do not have a known individual mutagenicity label and, in their current state, cannot be fitted into a traditional QSAR model. Multiple instance learning (MIL) however can be applied to a group of metabolites and their parent under a single mutagenicity label. Here we trained MIL models on Ames data, first with an aromatic amines data set (n = 457), a class known to require metabolic activation, and subsequently on a larger data set (n = 6505) incorporating multiple molecular species. MIL was shown to be able to predict Ames mutagenicity with performance in line with previously established models (balanced accuracy = 0.778), suggesting its potential utility in Ames prediction applications. Furthermore, the MIL model predicted well on identified hard-to-predict molecule groups relative to the models in which these molecule groups were identified. These results are presumably due to the increased consideration of the metabolic contribution to the mutagenic outcome. Further exploration of MIL as a supplement to existing models could aid in the prediction of chemicals where implicit modeling of metabolites cannot fully grasp their characteristics. This paper demonstrates the potential of an MIL approach to modeling Ames tests with S9 and is particularly relevant to metabolically activated xenobiotic mutagens.
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Affiliation(s)
- Samuel V Feeney
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Raymond Lui
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Davy Guan
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Slade Matthews
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, 2006, Australia
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11
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Kalian AD, Benfenati E, Osborne OJ, Gott D, Potter C, Dorne JLCM, Guo M, Hogstrand C. Exploring Dimensionality Reduction Techniques for Deep Learning Driven QSAR Models of Mutagenicity. TOXICS 2023; 11:572. [PMID: 37505541 PMCID: PMC10384850 DOI: 10.3390/toxics11070572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/28/2023] [Accepted: 06/28/2023] [Indexed: 07/29/2023]
Abstract
Dimensionality reduction techniques are crucial for enabling deep learning driven quantitative structure-activity relationship (QSAR) models to navigate higher dimensional toxicological spaces, however the use of specific techniques is often arbitrary and poorly explored. Six dimensionality techniques (both linear and non-linear) were hence applied to a higher dimensionality mutagenicity dataset and compared in their ability to power a simple deep learning driven QSAR model, following grid searches for optimal hyperparameter values. It was found that comparatively simpler linear techniques, such as principal component analysis (PCA), were sufficient for enabling optimal QSAR model performances, which indicated that the original dataset was at least approximately linearly separable (in accordance with Cover's theorem). However certain non-linear techniques such as kernel PCA and autoencoders performed at closely comparable levels, while (especially in the case of autoencoders) being more widely applicable to potentially non-linearly separable datasets. Analysis of the chemical space, in terms of XLogP and molecular weight, uncovered that the vast majority of testing data occurred within the defined applicability domain, as well as that certain regions were measurably more problematic and antagonised performances. It was however indicated that certain dimensionality reduction techniques were able to facilitate uniquely beneficial navigations of the chemical space.
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Affiliation(s)
- Alexander D Kalian
- Department of Nutritional Sciences, King's College London, Franklin-Wilkins Building, 150 Stamford St., London SE1 9NH, UK
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | | | - David Gott
- Food Standards Agency, 70 Petty France, London SW1H 9EX, UK
| | - Claire Potter
- Food Standards Agency, 70 Petty France, London SW1H 9EX, UK
| | - Jean-Lou C M Dorne
- European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy
| | - Miao Guo
- Department of Engineering, King's College London, Strand Campus, Strand, London WC2R 2LS, UK
| | - Christer Hogstrand
- Department of Analytical, Environmental and Forensic Sciences, King's College London, Franklin-Wilkins Building, 150 Stamford St., London SE1 9NH, UK
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12
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Danieli A, Colombo E, Raitano G, Lombardo A, Roncaglioni A, Manganaro A, Sommovigo A, Carnesecchi E, Dorne JLCM, Benfenati E. The VEGA Tool to Check the Applicability Domain Gives Greater Confidence in the Prediction of In Silico Models. Int J Mol Sci 2023; 24:9894. [PMID: 37373049 PMCID: PMC10298077 DOI: 10.3390/ijms24129894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/31/2023] [Accepted: 06/03/2023] [Indexed: 06/29/2023] Open
Abstract
A sound assessment of in silico models and their applicability domain can support the use of new approach methodologies (NAMs) in chemical risk assessment and requires increasing the users' confidence in this approach. Several approaches have been proposed to evaluate the applicability domain of such models, but their prediction power still needs a thorough assessment. In this context, the VEGA tool capable of assessing the applicability domain of in silico models is examined for a range of toxicological endpoints. The VEGA tool evaluates chemical structures and other features related to the predicted endpoints and is efficient in measuring applicability domain, enabling the user to identify less accurate predictions. This is demonstrated with many models addressing different endpoints, towards toxicity of relevance to human health, ecotoxicological endpoints, environmental fate, physicochemical and toxicokinetic properties, for both regression models and classifiers.
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Affiliation(s)
- Alberto Danieli
- Department of Biotechnology and Life Science, University of Insubria, Via Dunant 3, 21100 Varese, Italy;
| | - Erika Colombo
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | - Giuseppa Raitano
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | - Anna Lombardo
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | - Alessandra Roncaglioni
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | | | | | - Edoardo Carnesecchi
- European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy; (E.C.); (J.-L.C.M.D.)
| | - Jean-Lou C. M. Dorne
- European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy; (E.C.); (J.-L.C.M.D.)
| | - Emilio Benfenati
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
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13
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Medina-Barandica J, Contreras-Puentes N, Tarón-Dunoyer A, Durán-Lengua M, Alviz-Amador A. In-silico study for the identification of potential destabilizers between the spike protein of SARS-CoV-2 and human ACE-2. INFORMATICS IN MEDICINE UNLOCKED 2023; 40:101278. [PMID: 37305192 PMCID: PMC10241490 DOI: 10.1016/j.imu.2023.101278] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/16/2023] [Accepted: 05/16/2023] [Indexed: 06/13/2023] Open
Abstract
The emergence of the new SARS-CoV-2 virus, which causes the disease known as COVID-19, has generated a pandemic that has plunged the world into a health crisis. The infection process is triggered by the direct binding of the receptor-binding domain (RBD) of the spike (S) protein of SARS-CoV-2 to the angiotensin-converting enzyme 2 (ACE2) of the host cell. In the present study, virtual screening techniques such as molecular docking, molecular dynamics, calculation of free energy using the GBSA method, prediction of drug similarity, pharmacokinetic, and toxicological properties of various ligands interacting with the RBD-ACE2 complex were applied. The ligands radotinib, hinokiflavone, and ginkgetin were identified as potential destabilizers of the RBD-ACE2 interaction, which could produce their pharmacological effect by interacting at an allosteric site of ACE2, with affinity energy values of -10.2 ± 0.1, -9.8 ± 0.0, and -9.4 ± 0.0 kcal/mol, indicating strong receptor affinity. The complex with hinokiflavone showed the highest conformational stability and rigidity of the dynamic simulation and also obtained the best binding free energy of the three molecules, with an energy of -215.86 kcal/mol.
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Affiliation(s)
- Jeffry Medina-Barandica
- Pharmacology and Therapeutic Research Group, Faculty of Pharmaceutical Sciences, University of Cartagena, Cartagena, D.T. y C, Colombia
| | - Neyder Contreras-Puentes
- Pharmacology and Therapeutic Research Group, Faculty of Pharmaceutical Sciences, University of Cartagena, Cartagena, D.T. y C, Colombia
- GINUMED, Faculty of Health Sciences, Rafael Nuñez University Corporation, Cartagena D.T. y C., Colombia
| | - Arnulfo Tarón-Dunoyer
- GIBAE Research Group, Faculty of Engineering, University of Cartagena, Cartagena, D.T. y C, Colombia
| | - Marlene Durán-Lengua
- FARMABAC Research Group, Faculty of Medicine, University of Cartagena, Cartagena, D.T. y C, Colombia
| | - Antistio Alviz-Amador
- Pharmacology and Therapeutic Research Group, Faculty of Pharmaceutical Sciences, University of Cartagena, Cartagena, D.T. y C, Colombia
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14
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Tolosa J, Serrano Candelas E, Vallés Pardo JL, Goya A, Moncho S, Gozalbes R, Palomino Schätzlein M. MicotoXilico: An Interactive Database to Predict Mutagenicity, Genotoxicity, and Carcinogenicity of Mycotoxins. Toxins (Basel) 2023; 15:355. [PMID: 37368656 DOI: 10.3390/toxins15060355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 06/29/2023] Open
Abstract
Mycotoxins are secondary metabolites produced by certain filamentous fungi. They are common contaminants found in a wide variety of food matrices, thus representing a threat to public health, as they can be carcinogenic, mutagenic, or teratogenic, among other toxic effects. Several hundreds of mycotoxins have been reported, but only a few of them are regulated, due to the lack of data regarding their toxicity and mechanisms of action. Thus, a more comprehensive evaluation of the toxicity of mycotoxins found in foodstuffs is required. In silico toxicology approaches, such as Quantitative Structure-Activity Relationship (QSAR) models, can be used to rapidly assess chemical hazards by predicting different toxicological endpoints. In this work, for the first time, a comprehensive database containing 4360 mycotoxins classified in 170 categories was constructed. Then, specific robust QSAR models for the prediction of mutagenicity, genotoxicity, and carcinogenicity were generated, showing good accuracy, precision, sensitivity, and specificity. It must be highlighted that the developed QSAR models are compliant with the OECD regulatory criteria, and they can be used for regulatory purposes. Finally, all data were integrated into a web server that allows the exploration of the mycotoxin database and toxicity prediction. In conclusion, the developed tool is a valuable resource for scientists, industry, and regulatory agencies to screen the mutagenicity, genotoxicity, and carcinogenicity of non-regulated mycotoxins.
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Affiliation(s)
- Josefa Tolosa
- Laboratory of Food Chemistry and Toxicology, Faculty of Pharmacy, University of Valencia, Av. Vicent Andrés Estellés, Burjasot, 46100 Valencia, Spain
| | - Eva Serrano Candelas
- ProtoQSAR S.L., CEEI-Technology Park of Valencia, Av. Benjamín Franklin, 12, 46980 Paterna, Spain
| | - José Luis Vallés Pardo
- ProtoQSAR S.L., CEEI-Technology Park of Valencia, Av. Benjamín Franklin, 12, 46980 Paterna, Spain
| | - Addel Goya
- ProtoQSAR S.L., CEEI-Technology Park of Valencia, Av. Benjamín Franklin, 12, 46980 Paterna, Spain
| | - Salvador Moncho
- ProtoQSAR S.L., CEEI-Technology Park of Valencia, Av. Benjamín Franklin, 12, 46980 Paterna, Spain
| | - Rafael Gozalbes
- ProtoQSAR S.L., CEEI-Technology Park of Valencia, Av. Benjamín Franklin, 12, 46980 Paterna, Spain
- Moldrug AI Systems S.L., Olimpia Arozena Torres, 45, 46018 Valencia, Spain
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15
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Chakravarti S. Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments. Chem Res Toxicol 2023. [PMID: 37207298 DOI: 10.1021/acs.chemrestox.2c00368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Structural alerts are molecular substructures assumed to be associated with molecular initiating events in various toxic effects and an integral part of in silico toxicology. However, alerts derived using the knowledge of human experts often suffer from a lack of predictivity, specificity, and satisfactory coverage. In this work, we present a method to build hybrid QSAR models by combining expert knowledge-based alerts and statistically mined molecular fragments. Our objective was to find out if the combination is better than the individual systems. Lasso regularization-based variable selection was applied on combined sets of knowledge-based alerts and molecular fragments, but the variable elimination was only allowed to happen on the molecular fragments. We tested the concept on three toxicity end points, i.e., skin sensitization, acute Daphnia toxicity, and Ames mutagenicity, which covered both classification and regression problems. Results showed the predictive performance of such hybrid models is, indeed, better than the models based solely on expert alerts or statistically mined fragments alone. The method also enables the discovery of activating and mitigating/deactivating features for toxicity alerts and the identification of new alerts, thereby reducing false positive and false negative outcomes commonly associated with generic alerts and alerts with poor coverage, respectively.
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Affiliation(s)
- Suman Chakravarti
- MultiCASE Inc., 23811 Chagrin Blvd, Suite 305, Beachwood, Ohio 44122, United States
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16
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Lemée P, Fessard V, Habauzit D. Prioritization of mycotoxins based on mutagenicity and carcinogenicity evaluation using combined in silico QSAR methods. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 323:121284. [PMID: 36804886 DOI: 10.1016/j.envpol.2023.121284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 02/01/2023] [Accepted: 02/12/2023] [Indexed: 06/18/2023]
Abstract
Mycotoxins and their metabolites are a family of compounds that contains a great diversity of both structure and biological properties. Information on their toxicity is spread within several databases and in scientific literature. Due to the number of molecules and their structure diversity, the cost and time required for hazard evaluation of each compound is unrealistic. In that purpose, new approach methodologies (NAMs) can be applied to evaluate such large set of molecules. Among them, quantitative structure-activity relationship (QSAR) in silico models could be useful to predict the mutagenic and carcinogenic properties of mycotoxins. First, a complete list of 904 mycotoxins and metabolites was built. Then, some known mycotoxins were used to determine the best QSAR tools for mutagenicity and carcinogenicity predictions. The best tool was further applied to the whole list of 904 mycotoxins. At the end, 95 mycotoxins were identified as both mutagen and carcinogen and should be prioritized for further evaluation.
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Affiliation(s)
- Pierre Lemée
- ANSES (French Agency for Food, Environmental and Occupational Health & Safety), Toxicology of Contaminants Unit, Fougères, France
| | - Valérie Fessard
- ANSES (French Agency for Food, Environmental and Occupational Health & Safety), Toxicology of Contaminants Unit, Fougères, France
| | - Denis Habauzit
- ANSES (French Agency for Food, Environmental and Occupational Health & Safety), Toxicology of Contaminants Unit, Fougères, France.
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17
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Thakkar S, Slikker W, Yiannas F, Silva P, Blais B, Chng KR, Liu Z, Adholeya A, Pappalardo F, Soares MDLC, Beeler P, Whelan M, Roberts R, Borlak J, Hugas M, Torrecilla-Salinas C, Girard P, Diamond MC, Verloo D, Panda B, Rose MC, Jornet JB, Furuhama A, Fang H, Kwegyir-Afful E, Heintz K, Arvidson K, Burgos JG, Horst A, Tong W. Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective. Regul Toxicol Pharmacol 2023; 140:105388. [PMID: 37061083 DOI: 10.1016/j.yrtph.2023.105388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/07/2023] [Accepted: 04/12/2023] [Indexed: 04/17/2023]
Abstract
In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new technologies with regulatory applications through the annual conference Global Summit on Regulatory Science (GSRS). The 11th annual GSRS conference (GSRS21) focused on "Regulatory Sciences for Food/Drug Safety with Real-World Data (RWD) and Artificial Intelligence (AI)." The conference discussed current advancements in both AI and RWD approaches with a specific emphasis on how they impact regulatory sciences and how regulatory agencies across the globe are pursuing the adaptation and oversight of these technologies. There were presentations from Brazil, Canada, India, Italy, Japan, Germany, Switzerland, Singapore, the United Kingdom, and the United States. These presentations highlighted how various agencies are moving forward with these technologies by either improving the agencies' operation and/or preparing regulatory mechanisms to approve the products containing these innovations. To increase the content and discussion, the GSRS21 hosted two debate sessions on the question of "Is Regulatory Science Ready for AI?" and a workshop to showcase the analytical data tools that global regulatory agencies have been using and/or plan to apply to regulatory science. Several key topics were highlighted and discussed during the conference, such as the capabilities of AI and RWD to assist regulatory science policies for drug and food safety, the readiness of AI and data science to provide solutions for regulatory science. Discussions highlighted the need for a constant effort to evaluate emerging technologies for fit-for-purpose regulatory applications. The annual GSRS conferences offer a unique platform to facilitate discussion and collaboration across regulatory agencies, modernizing regulatory approaches, and harmonizing efforts.
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Affiliation(s)
- Shraddha Thakkar
- Center for Drug Evaluations and Research (CDER), Food and Drug Administration (FDA), USA
| | - William Slikker
- National Center for Toxicological Research (NCTR), Food and Drug Administration (FDA), USA
| | | | | | | | - Kern Rei Chng
- National Centre for Food Science, Singapore Food Agency (SFA), Singapore
| | - Zhichao Liu
- National Center for Toxicological Research (NCTR), Food and Drug Administration (FDA), USA
| | - Alok Adholeya
- The Energy and Resources Institute (TERI), New Delhi, India
| | | | | | - Patrick Beeler
- Swissmedic, Bern, Switzerland; University of Zurich, Zurich, Switzerland
| | | | | | | | | | | | | | - Matthew C Diamond
- Center for Devices and Radiological Health (CDRH), Food and Drug Administration (FDA), USA
| | | | - Binay Panda
- Jawaharlal Nehru University (JNU), New Delhi, India
| | | | | | | | - Hong Fang
- National Center for Toxicological Research (NCTR), Food and Drug Administration (FDA), USA
| | - Ernest Kwegyir-Afful
- Center for Food Safety and Applied Nutrition (CFSAN), Food and Drug Administration (FDA), USA
| | - Kasey Heintz
- Center for Food Safety and Applied Nutrition (CFSAN), Food and Drug Administration (FDA), USA
| | - Kirk Arvidson
- Center for Food Safety and Applied Nutrition (CFSAN), Food and Drug Administration (FDA), USA
| | | | | | - Weida Tong
- National Center for Toxicological Research (NCTR), Food and Drug Administration (FDA), USA.
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18
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Lou C, Yang H, Deng H, Huang M, Li W, Liu G, Lee PW, Tang Y. Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods. J Cheminform 2023; 15:35. [PMID: 36941726 PMCID: PMC10029263 DOI: 10.1186/s13321-023-00707-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 03/06/2023] [Indexed: 03/23/2023] Open
Abstract
Chemical mutagenicity is a serious issue that needs to be addressed in early drug discovery. Over a long period of time, medicinal chemists have manually summarized a series of empirical rules for the optimization of chemical mutagenicity. However, given the rising amount of data, it is getting more difficult for medicinal chemists to identify more comprehensive chemical rules behind the biochemical data. Herein, we integrated a large Ames mutagenicity data set with 8576 compounds to derive mutagenicity transformation rules for reversing Ames mutagenicity via matched molecular pairs analysis. A well-trained consensus model with a reasonable applicability domain was constructed, which showed favorable performance in the external validation set with an accuracy of 0.815. The model was used to assess the generalizability and validity of these mutagenicity transformation rules. The results demonstrated that these rules were of great value and could provide inspiration for the structural modifications of compounds with potential mutagenic effects. We also found that the local chemical environment of the attachment points of rules was critical for successful transformation. To facilitate the use of these mutagenicity transformation rules, we integrated them into ADMETopt2 ( http://lmmd.ecust.edu.cn/admetsar2/admetopt2/ ), a free web server for optimization of chemical ADMET properties. The above-mentioned approach would be extended to the optimization of other toxicity endpoints.
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Affiliation(s)
- Chaofeng Lou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Hongbin Yang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Hua Deng
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Mengting Huang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Philip W Lee
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
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19
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Cavasotto CN, Scardino V. Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point. ACS OMEGA 2022; 7:47536-47546. [PMID: 36591139 PMCID: PMC9798519 DOI: 10.1021/acsomega.2c05693] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/28/2022] [Indexed: 05/29/2023]
Abstract
Machine learning (ML) models to predict the toxicity of small molecules have garnered great attention and have become widely used in recent years. Computational toxicity prediction is particularly advantageous in the early stages of drug discovery in order to filter out molecules with high probability of failing in clinical trials. This has been helped by the increase in the number of large toxicology databases available. However, being an area of recent application, a greater understanding of the scope and applicability of ML methods is still necessary. There are various kinds of toxic end points that have been predicted in silico. Acute oral toxicity, hepatotoxicity, cardiotoxicity, mutagenicity, and the 12 Tox21 data end points are among the most commonly investigated. Machine learning methods exhibit different performances on different data sets due to dissimilar complexity, class distributions, or chemical space covered, which makes it hard to compare the performance of algorithms over different toxic end points. The general pipeline to predict toxicity using ML has already been analyzed in various reviews. In this contribution, we focus on the recent progress in the area and the outstanding challenges, making a detailed description of the state-of-the-art models implemented for each toxic end point. The type of molecular representation, the algorithm, and the evaluation metric used in each research work are explained and analyzed. A detailed description of end points that are usually predicted, their clinical relevance, the available databases, and the challenges they bring to the field are also highlighted.
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Affiliation(s)
- Claudio N. Cavasotto
- Computational
Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones
en Medicina Traslacional (IIMT), CONICET-Universidad
Austral, Pilar, B1629AHJ Buenos Aires, Argentina
- Austral
Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, B1629AHJ Buenos Aires, Argentina
- Facultad
de Ciencias Biomédicas, Facultad de Ingenierá, Universidad Austral, Pilar, B1630FHB Buenos
Aires, Argentina
| | - Valeria Scardino
- Austral
Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, B1629AHJ Buenos Aires, Argentina
- Meton
AI, Inc., Wilmington, Delaware 19801, United
States
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20
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Martínez MJ, Sabando MV, Soto AJ, Roca C, Requena-Triguero C, Campillo NE, Páez JA, Ponzoni I. Multitask Deep Neural Networks for Ames Mutagenicity Prediction. J Chem Inf Model 2022; 62:6342-6351. [PMID: 36066065 DOI: 10.1021/acs.jcim.2c00532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The Ames mutagenicity test constitutes the most frequently used assay to estimate the mutagenic potential of drug candidates. While this test employs experimental results using various strains of Salmonella typhimurium, the vast majority of the published in silico models for predicting mutagenicity do not take into account the test results of the individual experiments conducted for each strain. Instead, such QSAR models are generally trained employing overall labels (i.e., mutagenic and nonmutagenic). Recently, neural-based models combined with multitask learning strategies have yielded interesting results in different domains, given their capabilities to model multitarget functions. In this scenario, we propose a novel neural-based QSAR model to predict mutagenicity that leverages experimental results from different strains involved in the Ames test by means of a multitask learning approach. To the best of our knowledge, the modeling strategy hereby proposed has not been applied to model Ames mutagenicity previously. The results yielded by our model surpass those obtained by single-task modeling strategies, such as models that predict the overall Ames label or ensemble models built from individual strains. For reproducibility and accessibility purposes, all source code and datasets used in our experiments are publicly available.
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Affiliation(s)
- María Jimena Martínez
- ISISTAN (CONICET - UNCPBA) Campus Universitario - Paraje Arroyo Seco, 7000, Tandil, Argentina
| | - María Virginia Sabando
- Institute for Computer Science and Engineering, UNS-CONICET, 8000, Bahía Blanca, Argentina.,Department of Computer Science and Engineering, Universidad Nacional del Sur, 8000, Bahía Blanca, Argentina
| | - Axel J Soto
- Institute for Computer Science and Engineering, UNS-CONICET, 8000, Bahía Blanca, Argentina.,Department of Computer Science and Engineering, Universidad Nacional del Sur, 8000, Bahía Blanca, Argentina
| | - Carlos Roca
- CIB Margarita Salas (CSIC) Ramiro de Maeztu, 9. 28740, Madrid, Spain
| | | | - Nuria E Campillo
- CIB Margarita Salas (CSIC) Ramiro de Maeztu, 9. 28740, Madrid, Spain.,Instituto de Ciencias Matemáticas (CSIC), Nicolás Cabrera, no13-15, Campus de Cantoblanco, UAM, CP 28049, Madrid, Spain
| | - Juan A Páez
- Instituto de Química Médica. Consejo Superior de Investigaciones Científicas (CSIC), Juan de la Cierva 3, 28006, Madrid, Spain
| | - Ignacio Ponzoni
- Institute for Computer Science and Engineering, UNS-CONICET, 8000, Bahía Blanca, Argentina.,Department of Computer Science and Engineering, Universidad Nacional del Sur, 8000, Bahía Blanca, Argentina
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21
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Jesus JBDE, Conceição RADA, Machado TR, Barbosa MLC, Domingos TFS, Cabral LM, Rodrigues CR, Abrahim-Vieira B, Souza AMTDE. Toxicological assessment of SGLT2 inhibitors metabolites using in silico approach. AN ACAD BRAS CIENC 2022; 94:e20211287. [PMID: 36197362 DOI: 10.1590/0001-3765202220211287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 02/01/2022] [Indexed: 11/22/2022] Open
Abstract
Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are the latest class of drugs approved to treat type 2 DM (T2DM). Although adverse effects are often caused by a metabolite rather than the drug itself, only the safety assessment of disproportionate drug metabolites is usually performed, which is of particular concern for drugs of chronic use, such as SGLT2i. Bearing this in mind, in silico tools are efficient strategies to reveal the risk assessment of metabolites, being endorsed by many regulatory agencies. Thereby, the goal of this study was to apply in silico methods to provide the metabolites toxicity assessment of the SGLT2i. Toxicological assessment from SGLT2i metabolites retrieved from the literature was estimated using the structure and/or statistical-based alert implemented in DataWarrior and ADMET predictorTM softwares. The drugs and their metabolites displayed no mutagenic, tumorigenic or cardiotoxic risks. Still, M1-2 and M3-1 were recognized as potential hepatotoxic compounds and M1-2, M1-3, M3-1, M3-2, M3-3 and M4-3, were estimated to have very toxic LD50 values in rats. All SGLT2i and the metabolites M3-4, M4-1 and M4-2, were predicted to have reproductive toxicity. These results support the awareness that metabolites may be potential mediators of drug-induced toxicities of the therapeutic agents.
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Affiliation(s)
- Jéssica B DE Jesus
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
| | - Raissa A DA Conceição
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
| | - Thayná R Machado
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
| | - Maria L C Barbosa
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
| | - Thaisa F S Domingos
- BIODATA Computing Services & Consulting, Rua Aloísio Teixeira, 278, Parque Tecnológico, Cidade Universitária, 21941-850 Rio de Janeiro, RJ, Brazil
| | - Lucio M Cabral
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
| | - Carlos R Rodrigues
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
| | - Bárbara Abrahim-Vieira
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
| | - Alessandra M T DE Souza
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
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22
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Li X, He X, Le Y, Guo X, Bryant MS, Atrakchi AH, McGovern TJ, Davis-Bruno KL, Keire DA, Heflich RH, Mei N. Genotoxicity evaluation of nitrosamine impurities using human TK6 cells transduced with cytochrome P450s. Arch Toxicol 2022; 96:3077-3089. [PMID: 35882637 DOI: 10.1007/s00204-022-03347-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 07/14/2022] [Indexed: 12/15/2022]
Abstract
Many nitrosamines are recognized as mutagens and potent rodent carcinogens. Over the past few years, nitrosamine impurities have been detected in various drugs leading to drug recalls. Although nitrosamines are included in a 'cohort of concern' because of their potential human health risks, most of this concern is based on rodent cancer and bacterial mutagenicity data, and there are little data on their genotoxicity in human-based systems. In this study, we employed human lymphoblastoid TK6 cells transduced with human cytochrome P450 (CYP) 2A6 to evaluate the genotoxicity of six nitrosamines that have been identified as impurities in drug products: N-nitrosodiethylamine (NDEA), N-nitrosoethylisopropylamine (NEIPA), N-nitroso-N-methyl-4-aminobutanoic acid (NMBA), N-nitrosomethylphenylamine (NMPA), N-nitrosodiisopropylamine (NDIPA), and N-nitrosodibutylamine (NDBA). Using flow cytometry-based assays, we found that 24-h treatment with NDEA, NEIPA, NMBA, and NMPA caused concentration-dependent increases in the phosphorylation of histone H2A.X (γH2A.X) in CYP2A6-expressing TK6 cells. Metabolism of these four nitrosamines by CYP2A6 also caused significant increases in micronucleus frequency as well as G2/M phase cell-cycle arrest. In addition, nuclear P53 activation was found in CYP2A6-expressing TK6 cells exposed to NDEA, NEIPA, and NMPA. Overall, the genotoxic potency of the six nitrosamine impurities in our test system was NMPA > NDEA ≈ NEIPA > NMBA > NDBA ≈ NDIPA. This study provides new information on the genotoxic potential of nitrosamines in human cells, complementing test results generated from traditional assays and partially addressing the issue of the relevance of nitrosamine genotoxicity for humans. The metabolically competent human cell system reported here may be a useful model for risk assessment of nitrosamine impurities found in drugs.
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Affiliation(s)
- Xilin Li
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Xiaobo He
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Yuan Le
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Xiaoqing Guo
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Matthew S Bryant
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Aisar H Atrakchi
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - Timothy J McGovern
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - Karen L Davis-Bruno
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - David A Keire
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - Robert H Heflich
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Nan Mei
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.
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23
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Kim J, Jung S, Bae W, Lee K, Lee Y. LC–MS/MS and in silico analysis of pharmaceutical impurities in a combination drug for hypertension. SEPARATION SCIENCE PLUS 2022. [DOI: 10.1002/sscp.202200070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jong‐Sil Kim
- College of Pharmacy Chungbuk National University Choengju South Korea
- R&D Center Dongkook Pharm. Co., Ltd. Gyeonggi South Korea
| | - Seo‐Hyeon Jung
- College of Pharmacy Chungbuk National University Choengju South Korea
| | - Woo‐Hyun Bae
- College of Pharmacy Chungbuk National University Choengju South Korea
| | - Kye‐Wan Lee
- R&D Center Dongkook Pharm. Co., Ltd. Gyeonggi South Korea
| | - Yong‐Moon Lee
- College of Pharmacy Chungbuk National University Choengju South Korea
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24
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Furukawa A, Ono S, Yamada K, Torimoto N, Asayama M, Muto S. A local QSAR model based on the stability of nitrenium ions to support the ICH M7 expert review on the mutagenicity of primary aromatic amines. Genes Environ 2022; 44:10. [PMID: 35313995 PMCID: PMC8935809 DOI: 10.1186/s41021-022-00238-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 02/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Aromatic amines, often used as intermediates for pharmaceutical synthesis, may be mutagenic and therefore pose a challenge as metabolites or impurities in drug development. However, predicting the mutagenicity of aromatic amines using commercially available, quantitative structure-activity relationship (QSAR) tools is difficult and often requires expert review. In this study, we developed a shareable QSAR tool based on nitrenium ion stability. RESULTS The evaluation using in-house aromatic amine intermediates revealed that our model has prediction accuracy of aromatic amine mutagenicity comparable to that of commercial QSAR tools. The effect of changing the number and position of substituents on the mutagenicity of aromatic amines was successfully explained by the change in the nitrenium ion stability. Furthermore, case studies showed that our QSAR tool can support the expert review with quantitative indicators. CONCLUSIONS This local QSAR tool will be useful as a quantitative support tool to explain the substituent effects on the mutagenicity of primary aromatic amines. By further refinement through method sharing and standardization, our tool can support the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 expert review with quantitative indicators.
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Affiliation(s)
- Ayaka Furukawa
- Safety Research Laboratories, Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa, 251-8555, Japan.
| | - Satoshi Ono
- Discovery Technology Laboratories, Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 1000, Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa, 227-0033, Japan.
| | - Katsuya Yamada
- Safety Research Laboratories, Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa, 251-8555, Japan
| | - Nao Torimoto
- Discovery Technology Laboratories, Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 1000, Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa, 227-0033, Japan
| | - Mahoko Asayama
- Safety Research Laboratories, Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa, 251-8555, Japan
| | - Shigeharu Muto
- Safety Research Laboratories, Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa, 251-8555, Japan
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25
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Coppi A, Davies R, Wegesser T, Ishida K, Karmel J, Han J, Aiello F, Xie Y, Corbett MT, Parsons AT, Monticello TM, Minocherhomji S. Characterization of false positive, contaminant-driven mutagenicity in impurities associated with the sotorasib drug substance. Regul Toxicol Pharmacol 2022; 131:105162. [DOI: 10.1016/j.yrtph.2022.105162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/14/2022] [Accepted: 03/17/2022] [Indexed: 10/18/2022]
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26
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OUP accepted manuscript. Mutagenesis 2022; 37:191-202. [DOI: 10.1093/mutage/geac010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 04/09/2022] [Indexed: 11/14/2022] Open
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27
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Honma M, Yamada M, Yasui M, Horibata K, Sugiyama KI, Masumura K. Genotoxicity assessment of food-flavoring chemicals used in Japan. Toxicol Rep 2022; 9:1008-1012. [DOI: 10.1016/j.toxrep.2022.04.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/22/2022] [Accepted: 04/24/2022] [Indexed: 11/30/2022] Open
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28
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Manganelli S, Gamba A, Colombo E, Benfenati E. Using VEGAHUB Within a Weight-of-Evidence Strategy. Methods Mol Biol 2022; 2425:479-495. [PMID: 35188643 DOI: 10.1007/978-1-0716-1960-5_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Industrial needs and regulatory requirements have played a significant role in accelerating the use of nontesting methods including in silico tools as alternatives to animal testing. The main interest is not solely on the use of in silico tools, or in read-across, but on better toxicological safety assessment of substances, and for this purpose more advanced, integrated strategies have to be implemented. VEGAHUB wants to promote this broader view, not necessarily focused on a specific approach. Applying multiple tools and complementary approaches instead of one technique may provide more elements for a more robust evaluation, but at the same time it is important to have a conceptual scheme to integrate multiple, heterogeneous lines of evidence. We will show how the user can benefit from the diversity of tools available within the platform VEGAHUB for assessing the biological properties of chemical substances on an example of (non)mutagenicity.
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Affiliation(s)
| | - Alessio Gamba
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Erika Colombo
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
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29
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Mombelli E, Raitano G, Benfenati E. In Silico Prediction of Chemically Induced Mutagenicity: A Weight of Evidence Approach Integrating Information from QSAR Models and Read-Across Predictions. Methods Mol Biol 2022; 2425:149-183. [PMID: 35188632 DOI: 10.1007/978-1-0716-1960-5_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Information on genotoxicity is an essential piece of information in the framework of several regulations aimed at evaluating chemical toxicity. In this context, QSAR models that can predict Ames genotoxicity can conveniently provide relevant information. Indeed, they can be straightforwardly and rapidly used for predicting the presence or absence of genotoxic hazards associated with the interactions of chemicals with DNA. Nevertheless, and despite their ease of use, the main interpretative challenge is related to a critical assessment of the information that can be gathered, thanks to these tools. This chapter provides guidance on how to use freely available QSAR and read-across tools provided by VEGA HUB and on how to interpret their predictions according to a weight-of-evidence approach.
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Affiliation(s)
- Enrico Mombelli
- INERIS-Institut National de l'Environnement Industriel et des Risques, Verneuil-en-Halatte, France.
| | - Giuseppa Raitano
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Laboratory of Environmental Chemistry and Toxicology, Milan, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Laboratory of Environmental Chemistry and Toxicology, Milan, Italy
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30
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Chakravarti SK, Saiakhov RD. MultiCASE Platform for In Silico Toxicology. Methods Mol Biol 2022; 2425:497-518. [PMID: 35188644 DOI: 10.1007/978-1-0716-1960-5_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Predictive and computational toxicology, a highly scientific and research-based field, is rapidly progressing with wider acceptance by regulatory agencies around the world. Almost every aspect of the field has seen fundamental changes during the last decade due to the availability of more data, usage, and acceptance of a variety of predictive tools and an increase in the overall awareness. Also, the influence from the recent explosive developments in the field of artificial intelligence has been significant. However, the need for sophisticated, easy to use and well-maintained software platforms for in silico toxicological assessments remains very high. The MultiCASE suite of software is one such platform that consists of an integrated collection of software programs, tools, and databases. While providing easy-to-use and highly useful tools that are relevant at present, it has always remained at the forefront of research and development by inventing new technologies and discovering new insights in the area of QSAR, artificial intelligence, and machine learning. This chapter gives the background, an overview of the software and databases involved, and a brief description of the usage methodology with the aid of examples.
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31
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Muller C, Rabal O, Diaz Gonzalez C. Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2390:383-407. [PMID: 34731478 DOI: 10.1007/978-1-0716-1787-8_16] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their interactions with biological targets. In recent years, artificial intelligence (AI) has made remarkable modeling progress, driven by new algorithms and by the increase in computing power and storage capacities, which allow the processing of large amounts of data in a short time. This review provides the current state of the art of AI methods applied to drug discovery, with a focus on structure- and ligand-based virtual screening, library design and high-throughput analysis, drug repurposing and drug sensitivity, de novo design, chemical reactions and synthetic accessibility, ADMET, and quantum mechanics.
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Affiliation(s)
- Christophe Muller
- Evotec (France) SAS, Computational Drug Discovery, Integrated Drug Discovery, Toulouse, France
| | - Obdulia Rabal
- Evotec (France) SAS, Computational Drug Discovery, Integrated Drug Discovery, Toulouse, France
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32
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Sleight TW, Sexton CN, Mpourmpakis G, Gilbertson LM, Ng CA. A Classification Model to Identify Direct-Acting Mutagenic Polycyclic Aromatic Hydrocarbon Transformation Products. Chem Res Toxicol 2021; 34:2273-2286. [PMID: 34662518 DOI: 10.1021/acs.chemrestox.1c00187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are a complex group of environmental contaminants, many having long environmental half-lives. As these compounds degrade, the changes in their structure can result in a substantial increase in mutagenicity compared to the parent compound. Over time, each individual PAH can potentially degrade into several thousand unique transformation products, creating a complex, constantly evolving set of intermediates. Microbial degradation is the primary mechanism of their transformation and ultimate removal from the environment, and this process can result in mutagenic activation similar to the metabolic activation that can occur in multicellular organisms. The diversity of the potential intermediate structures in PAH-contaminated environments renders hazard assessment difficult for both remediation professionals and regulators. A mixture of structural and energetic descriptors has proven effective in existing studies for classifying which PAH transformation products will be mutagenic. However, most existing studies of environmental PAH mutagens primarily focus on nitrogenated derivatives, which are prevalent in the atmosphere and not as relevant in soil. Additionally, PAH products commonly found in the environment can range from as large as five rings to as small as a single ring, requiring a broadly inclusive methodology to comprehensively evaluate mutagenic potential. We developed a combination of supervised and unsupervised machine learning methods to predict environmentally induced PAH mutagenicity with improved performance over currently available tools. K-means clustering with principal component analysis allows us to identify molecular clusters that we hypothesize to have similar mechanisms of action. Recursive feature elimination identifies the most influential descriptors. The cluster-specific regression outperforms available classifiers in predicting direct-acting mutagens resulting from the microbial biodegradation of PAHs and provides direction for future studies evaluating the environmental hazards resulting from PAH biodegradation.
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Affiliation(s)
- Trevor W Sleight
- Civil & Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Caitlin N Sexton
- Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Giannis Mpourmpakis
- Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Leanne M Gilbertson
- Civil & Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.,Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Carla A Ng
- Civil & Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.,Environmental and Occupational Health, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
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33
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Chu CSM, Simpson JD, O'Neill PM, Berry NG. Machine learning - Predicting Ames mutagenicity of small molecules. J Mol Graph Model 2021; 109:108011. [PMID: 34555723 DOI: 10.1016/j.jmgm.2021.108011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/29/2021] [Accepted: 08/18/2021] [Indexed: 10/20/2022]
Abstract
In modern drug discovery, detection of a compound's potential mutagenicity is crucial. However, the traditional method of mutagenicity detection using the Ames test is costly and time consuming as the compounds need to be synthesised and then tested and the results are not always accurate and reproducible. Therefore, it would be advantageous to develop robust in silico models which can accurately predict the mutagenicity of a compound prior to synthesis to overcome the inadequacies of the Ames test. After curation of a previously defined compound mutagenicity library, over 5000 molecules had their chemical fingerprints and molecular properties calculated. Using 8 classification modelling algorithms, including support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGB), a total of 112 predictive models have been constructed. Their performance has been assessed using 10-fold cross validation and a hold-out test set and some of the top performing models have been assessed using the y-randomisation approach. As a result, we have found SVM and XGB models to have good performance during the 10-fold cross validation (AUROC >0.90, sensitivity >0.85, specificity >0.75, balanced accuracy >0.80, Kappa >0.65) and on the test set (AUROC >0.65, sensitivity >0.65, specificity >0.60, balanced accuracy >0.65, Kappa >0.30). We have also identified molecular properties that are the most influential for mutagenicity prediction when combined with chemical molecular fingerprints. Using the Class A mutagenic compounds from the Ames/QSAR International Challenge Project, we were able to verify our models perform better, predicting more mutagens correctly then the StarDrop Ames mutagenicity prediction and TEST mutagenicity prediction.
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Affiliation(s)
- Charmaine S M Chu
- Department of Chemistry, University of Liverpool, Crown Street, Liverpool, L69 7ZD, UK.
| | - Jack D Simpson
- Department of Chemistry, University of Liverpool, Crown Street, Liverpool, L69 7ZD, UK
| | - Paul M O'Neill
- Department of Chemistry, University of Liverpool, Crown Street, Liverpool, L69 7ZD, UK
| | - Neil G Berry
- Department of Chemistry, University of Liverpool, Crown Street, Liverpool, L69 7ZD, UK.
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34
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Benigni R. In silico assessment of genotoxicity. Combinations of sensitive structural alerts minimize false negative predictions for all genotoxicity endpoints and can single out chemicals for which experimentation can be avoided. Regul Toxicol Pharmacol 2021; 126:105042. [PMID: 34506881 DOI: 10.1016/j.yrtph.2021.105042] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 08/27/2021] [Accepted: 09/03/2021] [Indexed: 11/15/2022]
Abstract
Genotoxicity assessment of chemicals has a crucial role in most regulations. Due to labor, time, cost, and animal welfare issues, attention is being given to (Q)SAR methods. A strategic application of alternative methods is to first use a sequence of conservative (very sensitive) (Q)SARs and/or in vitro models to arrive at the conclusion that no further testing is necessary for negatives, and to use mechanistically based, Weight-Of-Evidence approach to evaluate the chemicals showing positive results. The ICH M7 guideline to detect DNA-reactive impurities in drugs follows these lines (recommending solely (Q)SAR in step 1). However, ICH M7 focuses only on Ames test. Here a large database of more than 6000 chemicals positive in at least one endpoint (in vitro gene mutations or chromosomal aberrations, in vivo micronucleus, aneugenicity) were analyzed with structural alerts implemented in the OECD QSAR Toolbox, resulting in maximum 3% false negatives. These promising results indicate that it may be possible to extend the approach to the whole range of genotoxicity endpoints required by regulations. Since structural alerts may generate false positives, cautious follow-up of positives is recommended (with e.g., statistically based QSARs, read across of similar chemicals, expert judgement, and experimentation when necessary).
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Arnesdotter E, Rogiers V, Vanhaecke T, Vinken M. An overview of current practices for regulatory risk assessment with lessons learnt from cosmetics in the European Union. Crit Rev Toxicol 2021; 51:395-417. [PMID: 34352182 DOI: 10.1080/10408444.2021.1931027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Risk assessments of various types of chemical compounds are carried out in the European Union (EU) foremost to comply with legislation and to support regulatory decision-making with respect to their safety. Historically, risk assessment has relied heavily on animal experiments. However, the EU is committed to reduce animal experimentation and has implemented several legislative changes, which have triggered a paradigm shift towards human-relevant animal-free testing in the field of toxicology, in particular for risk assessment. For some specific endpoints, such as skin corrosion and irritation, validated alternatives are available whilst for other endpoints, including repeated dose systemic toxicity, the use of animal data is still central to meet the information requirements stipulated in the different legislations. The present review aims to provide an overview of established and more recently introduced methods for hazard assessment and risk characterisation for human health, in particular in the context of the EU Cosmetics Regulation (EC No 1223/2009) as well as the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) Regulation (EC 1907/2006).
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Affiliation(s)
- Emma Arnesdotter
- Department of Pharmaceutical and Pharmacological Sciences, Research Group of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Vera Rogiers
- Department of Pharmaceutical and Pharmacological Sciences, Research Group of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Tamara Vanhaecke
- Department of Pharmaceutical and Pharmacological Sciences, Research Group of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Mathieu Vinken
- Department of Pharmaceutical and Pharmacological Sciences, Research Group of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Brussels, Belgium
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Gallego V, Naveiro R, Roca C, Ríos Insua D, Campillo NE. AI in drug development: a multidisciplinary perspective. Mol Divers 2021; 25:1461-1479. [PMID: 34251580 PMCID: PMC8342381 DOI: 10.1007/s11030-021-10266-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 06/29/2021] [Indexed: 01/09/2023]
Abstract
The introduction of a new drug to the commercial market follows a complex and long process that typically spans over several years and entails large monetary costs due to a high attrition rate. Because of this, there is an urgent need to improve this process using innovative technologies such as artificial intelligence (AI). Different AI tools are being applied to support all four steps of the drug development process (basic research for drug discovery; pre-clinical phase; clinical phase; and postmarketing). Some of the main tasks where AI has proven useful include identifying molecular targets, searching for hit and lead compounds, synthesising drug-like compounds and predicting ADME-Tox. This review, on the one hand, brings in a mathematical vision of some of the key AI methods used in drug development closer to medicinal chemists and, on the other hand, brings the drug development process and the use of different models closer to mathematicians. Emphasis is placed on two aspects not mentioned in similar surveys, namely, Bayesian approaches and their applications to molecular modelling and the eventual final use of the methods to actually support decisions. Promoting a perfect synergy.
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Affiliation(s)
- Víctor Gallego
- Institute of Mathematical Sciences (ICMAT-CSIC), Nicolás Cabrera 13-15, 28049, Madrid, Spain
| | - Roi Naveiro
- Institute of Mathematical Sciences (ICMAT-CSIC), Nicolás Cabrera 13-15, 28049, Madrid, Spain
| | - Carlos Roca
- AItenea Biotech S.L. Parque Científico de Madrid, Faraday, 7, 28049, Madrid, Spain
| | - David Ríos Insua
- ICMAT-CSIC and Dept. of Statistics and OR, U. Compl. Madrid, Madrid, Spain
| | - Nuria E Campillo
- CIB-Margarita Salas (CSIC), Ramiro de Maeztu, 9, 28040, Madrid, Spain.
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Tcheremenskaia O, Benigni R. Toward regulatory acceptance and improving the prediction confidence of in silico approaches: a case study of genotoxicity. Expert Opin Drug Metab Toxicol 2021; 17:987-1005. [PMID: 34078212 DOI: 10.1080/17425255.2021.1938540] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Introduction: Genotoxicity is an imperative component of the human health safety assessment of chemicals. Its secure forecast is of the utmost importance for all health prevention strategies and regulations.Areas covered: We surveyed several types of alternative, animal-free approaches ((quantitative) structure-activity relationship (Q)SAR, read-across, Adverse Outcome Pathway, Integrated Approaches to Testing and Assessment) for genotoxicity prediction within the needs of regulatory frameworks, putting special emphasis on data quality and uncertainties issues.Expert opinion: (Q)SAR models and read-across approaches for in vitro bacterial mutagenicity have sufficient reliability for use in prioritization processes, and as support in regulatory decisions in combination with other types of evidence. (Q)SARs and read-across methodologies for other genotoxicity endpoints need further improvements and should be applied with caution. It appears that there is still large room for improvement of genotoxicity prediction methods. Availability of well-curated high-quality databases, covering a broader chemical space, is one of the most important needs. Integration of in silico predictions with expert knowledge, weight-of-evidence-based assessment, and mechanistic understanding of genotoxicity pathways are other key points to be addressed for the generation of more accurate and trustable results.
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Affiliation(s)
- Olga Tcheremenskaia
- Environmental and Health Department, Istituto Superiore Di Sanità (ISS), Rome, Italy, Rome, Italy
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Hung C, Gini G. QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction. Mol Divers 2021; 25:1283-1299. [PMID: 34146224 DOI: 10.1007/s11030-021-10250-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 06/08/2021] [Indexed: 11/30/2022]
Abstract
Deep neural networks are effective in learning directly from low-level encoded data without the need of feature extraction. This paper shows how QSAR models can be constructed from 2D molecular graphs without computing chemical descriptors. Two graph convolutional neural network-based models are presented with and without a Bayesian estimation of the prediction uncertainty. The property under investigation is mutagenicity: Models developed here predict the output of the Ames test. These models take the SMILES representation of the molecules as input to produce molecular graphs in terms of adjacency matrices and subsequently use attention mechanisms to weight the role of their subgraphs in producing the output. The results positively compare with current state-of-the-art models. Furthermore, our proposed model interpretation can be enhanced by the automatic extraction of the substructures most important in driving the prediction, as well as by uncertainty estimations.
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Development of a new quantitative structure-activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™. Genes Environ 2021; 43:16. [PMID: 33931133 PMCID: PMC8088067 DOI: 10.1186/s41021-021-00182-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 02/24/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Food flavors are relatively low molecular weight chemicals with unique odor-related functional groups that may also be associated with mutagenicity. These chemicals are often difficult to test for mutagenicity by the Ames test because of their low production and peculiar odor. Therefore, application of the quantitative structure-activity relationship (QSAR) approach is being considered. We used the StarDrop™ Auto-Modeller™ to develop a new QSAR model. RESULTS In the first step, we developed a new robust Ames database of 406 food flavor chemicals consisting of existing Ames flavor chemical data and newly acquired Ames test data. Ames results for some existing flavor chemicals have been revised by expert reviews. We also collected 428 Ames test datasets for industrial chemicals from other databases that are structurally similar to flavor chemicals. A total of 834 chemicals' Ames test datasets were used to develop the new QSAR models. We repeated the development and verification of prototypes by selecting appropriate modeling methods and descriptors and developed a local QSAR model. A new QSAR model "StarDrop NIHS 834_67" showed excellent performance (sensitivity: 79.5%, specificity: 96.4%, accuracy: 94.6%) for predicting Ames mutagenicity of 406 food flavors and was better than other commercial QSAR tools. CONCLUSIONS A local QSAR model, StarDrop NIHS 834_67, was customized to predict the Ames mutagenicity of food flavor chemicals and other low molecular weight chemicals. The model can be used to assess the mutagenicity of food flavors without actual testing.
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Yang X, Zhang Z, Li Q, Cai Y. Quantitative structure-activity relationship models for genotoxicity prediction based on combination evaluation strategies for toxicological alternative experiments. Sci Rep 2021; 11:8030. [PMID: 33850191 PMCID: PMC8044236 DOI: 10.1038/s41598-021-87035-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 03/23/2021] [Indexed: 11/10/2022] Open
Abstract
Mutagenicity exerts adverse effects on humans. Conventional methods cannot simultaneously predict the toxicity of a large number of compounds. Most mutagenicity prediction models are based on a single experimental type and lack other experimental combination data as support, resulting in limited application scope and predictive ability. In this study, we partitioned data from GENE-TOX, CPDB, and Chemical Carcinogenesis Research Information System according to the weight-of-evidence method for modelling. In our data set, in vivo and in vitro experiments in groups as well as prokaryotic and eukaryotic cell experiments were included in accordance with the ICH guideline. We compared the two experimental combinations mentioned in the weight-of-evidence method and reintegrated the experimental data into three groups. Nine sub-models and three fusion models were established using random forest (RF), support vector machine (SVM), and back propagation (BP) neural network algorithms. When fusing base models under the same algorithm according to the ensemble rules, all models showed excellent predictive performance. The RF, SVM, and BP fusion models reached a prediction accuracy rate of 83.4%, 80.5%, 79.0% respectively. The area under the curve (AUC) reached 0.853, 0.897, 0.865 respectively. Therefore, the established fusion QSAR models can serve as an early warning system for mutagenicity of compounds.
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Affiliation(s)
- Xiaotong Yang
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China
| | - Zhengbao Zhang
- Guangdong Province Center for Disease Control and Prevention, Guangzhou, China
| | - Qing Li
- Guangdong Province Center for Disease Control and Prevention, Guangzhou, China.
| | - Yongming Cai
- College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, China.
- Guangdong Provincial TCM Precision Medicine Big Data Engineering Technology Research Center, Guangzhou, China.
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Petkov P, Ivanova H, Schultz T, Mekenyan O. Criteria for assessing the reliability of toxicity predictions: I. TIMES Ames mutagenicity model. ACTA ACUST UNITED AC 2021. [DOI: 10.1016/j.comtox.2020.100143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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42
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Multi-Time-Scale Features for Accurate Respiratory Sound Classification. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10238606] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85%±3% and an precision of 80%±8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation.
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Screening for Ames mutagenicity of food flavor chemicals by (quantitative) structure-activity relationship. Genes Environ 2020; 42:32. [PMID: 33292765 PMCID: PMC7706032 DOI: 10.1186/s41021-020-00171-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 11/19/2020] [Indexed: 12/20/2022] Open
Abstract
Background (Quantitative) Structure-Activity Relationship ((Q)SAR) is a promising approach to predict the potential adverse effects of chemicals based on their structure without performing toxicological studies. We evaluate the mutagenicity of food flavor chemicals by (Q) SAR tools, identify potentially mutagenic chemicals, and verify their mutagenicity by actual Ames test. Results The Ames mutagenicity of 3942 food flavor chemicals was predicted using two (Q)SAR) tools, DEREK Nexus and CASE Ultra. Three thousand five hundred seventy-five chemicals (91%) were judged to be negative in both (Q) SAR tools, and 75 chemicals (2%) were predicted to be positive in both (Q) SAR tools. When the Ames test was conducted on ten of these positive chemicals, nine showed positive results. Conclusion The (Q) SAR method can be used for screening the mutagenicity of food flavors. Supplementary Information The online version contains supplementary material available at 10.1186/s41021-020-00171-1.
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Krebs J, McKeague M. Green Toxicology: Connecting Green Chemistry and Modern Toxicology. Chem Res Toxicol 2020; 33:2919-2931. [DOI: 10.1021/acs.chemrestox.0c00260] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Johanna Krebs
- Pharmacology and Therapeutics, Faculty of Medicine, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec, Canada H3G 1Y6
- Department of Health Sciences and Technology, ETH Zürich, Universitätstrasse 2, Zurich, Switzerland CH 8092
| | - Maureen McKeague
- Pharmacology and Therapeutics, Faculty of Medicine, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec, Canada H3G 1Y6
- Faculty of Science, Chemistry, McGill University, 801 Sherbrooke Street West, Montreal, Quebec, Canada H3A 0B8
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Snodin DJ. A Primer for Pharmaceutical Process Development Chemists and Analysts in Relation to Impurities Perceived to Be Mutagenic or “Genotoxic”. Org Process Res Dev 2020. [DOI: 10.1021/acs.oprd.0c00343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- David J. Snodin
- Xiphora Biopharma Consulting, 9 Richmond Apartments, Redland Court Road, Bristol BS6 7BG, U.K
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46
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Barhdadi S, Mertens B, Van Bossuyt M, Van De Maele J, Anthonissen R, Canfyn M, Courselle P, Rogiers V, Deconinck E, Vanhaecke T. Identification of flavouring substances of genotoxic concern present in e-cigarette refills. Food Chem Toxicol 2020; 147:111864. [PMID: 33217530 DOI: 10.1016/j.fct.2020.111864] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 10/20/2020] [Accepted: 11/12/2020] [Indexed: 12/15/2022]
Abstract
E-cigarettes have become very popular, a trend that has been stimulated by the wide variety of available e-liquid flavours. Considering the large number of e-liquid flavours (>7000), there is an urgent need to establish a screening strategy to prioritize the flavouring substances of highest concern for human health. In the present study, a prioritization strategy combining analytical screening, in silico tools and literature data was developed to identify potentially genotoxic e-liquid flavourings. Based on the analysis of 129 e-liquids collected on the Belgian market, 60 flavourings with positive in silico predictions for genotoxicity were identified. By using literature data, genotoxicity was excluded for 33 of them whereas for 5, i.e. estragole, safrole, 2-furylmethylketon, 2,5-dimethyl-4-hydroxyl-3(2H)-furanone and transhexanal, there was a clear concern for in vivo genotoxicity. A selection of 4 out of the remaining 22 flavourings was tested in two in vitro genotoxicity assays. Three out of the four tested flavourings induced gene mutations and chromosome damage in vitro, whereas equivocal results were obtained for the fourth compound. Thus, although there is a legislative framework which excludes the use of CMR compounds in e-liquids, flavourings of genotoxic concern are present and might pose a health risk for e-cigarette users.
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Affiliation(s)
- Sophia Barhdadi
- Scientific Direction of Chemical and Physical Health Risks, Sciensano, Brussels, Belgium; Faculty of Medicines and Pharmacy, Department of in Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Birgit Mertens
- Scientific Direction of Chemical and Physical Health Risks, Sciensano, Brussels, Belgium; Department of Biomedical Sciences, University of Antwerp, Wilrijk, Belgium
| | - Melissa Van Bossuyt
- Scientific Direction of Chemical and Physical Health Risks, Sciensano, Brussels, Belgium; Faculty of Medicines and Pharmacy, Department of in Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Jolien Van De Maele
- Scientific Direction of Chemical and Physical Health Risks, Sciensano, Brussels, Belgium
| | - Roel Anthonissen
- Scientific Direction of Chemical and Physical Health Risks, Sciensano, Brussels, Belgium
| | - Michael Canfyn
- Scientific Direction of Chemical and Physical Health Risks, Sciensano, Brussels, Belgium
| | - Patricia Courselle
- Scientific Direction of Chemical and Physical Health Risks, Sciensano, Brussels, Belgium
| | - Vera Rogiers
- Faculty of Medicines and Pharmacy, Department of in Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Eric Deconinck
- Scientific Direction of Chemical and Physical Health Risks, Sciensano, Brussels, Belgium
| | - Tamara Vanhaecke
- Faculty of Medicines and Pharmacy, Department of in Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel (VUB), Brussels, Belgium.
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Chakravarti SK. Reason Vectors: Abstract Representation of Chemistry–Biology Interaction Outcomes, for Reasoning and Prediction. J Chem Inf Model 2020; 60:4614-4628. [DOI: 10.1021/acs.jcim.0c00601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Suman K. Chakravarti
- MultiCASE Inc., 23811 Chagrin Blvd., Suite 305, Beachwood, Ohio 44122, United States
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Herrmann K, Holzwarth A, Rime S, Fischer BC, Kneuer C. (Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation? PEST MANAGEMENT SCIENCE 2020; 76:3316-3325. [PMID: 32223060 DOI: 10.1002/ps.5828] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 03/29/2020] [Indexed: 06/10/2023]
Abstract
The assessment of human health risks resulting from the presence of metabolites in groundwater and food residues has become an important element in pesticide authorisation. In this context, the evaluation of mutagenicity is of particular interest and a paradigm shift from exposure-triggered testing to in silico-based screening has been recommended in the European Food Safety Authority (EFSA) Guidance on the establishment of the residue definition for dietary risk assessment. In addition, it is proposed to apply in silico predictions when experimental mutagenicity testing is not possible due to a lack of sufficient quantities of the pesticide metabolite. This, combined with animal welfare and economic considerations, has led to a situation where an increasing number of in silico studies are submitted to regulatory authorities. Whilst there is extensive experience with in silico predictions for mutagenicity in the chemical and pharmaceutical industry, their suitability in pesticide regulation is still insufficiently considered. Therefore, we herein discuss critical issues that need to be resolved to successfully implement (Quantitative) Structure-Activity Relationship ((Q)SAR) as an accepted tool in pesticide regulation. For illustration purposes, the results of a pilot study are included. The presented study highlights a need for further improvement regarding the predictivity and applicability domain of (Q)SAR systems for pesticides and their metabolites, but also raises other questions such as model selection, establishment of acceptance criteria, harmonised approaches to the combination of model outputs into overall conclusions, adequate reporting and data sharing. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Kristin Herrmann
- Department Pesticides Safety, German Federal Institute for Risk Assessment, Berlin, Germany
| | - Andrea Holzwarth
- Department Pesticides Safety, German Federal Institute for Risk Assessment, Berlin, Germany
| | - Soyub Rime
- Department Pesticides Safety, German Federal Institute for Risk Assessment, Berlin, Germany
| | - Benjamin C Fischer
- Department Pesticides Safety, German Federal Institute for Risk Assessment, Berlin, Germany
| | - Carsten Kneuer
- Department Pesticides Safety, German Federal Institute for Risk Assessment, Berlin, Germany
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Goel R, Valerio LG. Predicting the mutagenic potential of chemicals in tobacco products using in silico toxicology tools. Toxicol Mech Methods 2020; 30:672-678. [DOI: 10.1080/15376516.2020.1805836] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Reema Goel
- United States Food and Drug Administration, Division of Nonclinical Science, Office of Science, Center for Tobacco Products, Calverton, MD, USA
| | - Luis G. Valerio
- United States Food and Drug Administration, Division of Nonclinical Science, Office of Science, Center for Tobacco Products, Calverton, MD, USA
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Abstract
![]()
As
a field, computational toxicology is concerned with using in silico models to predict and understand the origins of
toxicity. It is fast, relatively inexpensive, and avoids the ethical
conundrum of using animals in scientific experimentation. In this
perspective, we discuss the importance of computational models in
toxicology, with a specific focus on the different model types that
can be used in predictive toxicological approaches toward mutagenicity
(SARs and QSARs). We then focus on how quantum chemical methods, such
as density functional theory (DFT), have previously been used in the
prediction of mutagenicity. It is then discussed how DFT allows for
the development of new chemical descriptors that focus on capturing
the steric and energetic effects that influence toxicological reactions.
We hope to demonstrate the role that DFT plays in understanding the
fundamental, intrinsic chemistry of toxicological reactions in predictive
toxicology.
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Affiliation(s)
- Piers A Townsend
- Centre for Sustainable Chemical Technologies, Department of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom
| | - Matthew N Grayson
- Department of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom
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