51
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Mechanistic roles of microRNAs in hepatocarcinogenesis: A study of thioacetamide with multiple doses and time-points of rats. Sci Rep 2017; 7:3054. [PMID: 28596526 PMCID: PMC5465221 DOI: 10.1038/s41598-017-02798-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Accepted: 04/19/2017] [Indexed: 02/06/2023] Open
Abstract
Environmental chemicals exposure is one of the primary factors for liver toxicity and hepatocarcinoma. Thioacetamide (TAA) is a well-known hepatotoxicant and could be a liver carcinogen in humans. The discovery of early and sensitive microRNA (miRNA) biomarkers in liver injury and tumor progression could improve cancer diagnosis, prognosis, and management. To study this, we performed next generation sequencing of the livers of Sprague-Dawley rats treated with TAA at three doses (4.5, 15 and 45 mg/kg) and four time points (3-, 7-, 14- and 28-days). Overall, 330 unique differentially expressed miRNAs (DEMs) were identified in the entire TAA-treatment course. Of these, 129 DEMs were found significantly enriched for the “liver cancer” annotation. These results were further complemented by pathway analysis (Molecular Mechanisms of Cancer, p53-, TGF-β-, MAPK- and Wnt-signaling). Two miRNAs (rno-miR-34a-5p and rno-miR-455-3p) out of 48 overlapping DEMs were identified to be early and sensitive biomarkers for TAA-induced hepatocarcinogenicity. We have shown significant regulatory associations between DEMs and TAA-induced liver carcinogenesis at an earlier stage than histopathological features. Most importantly, miR-34a-5p is the most suitable early and sensitive biomarker for TAA-induced hepatocarcinogenesis due to its consistent elevation during the entire treatment course.
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52
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Gumber K, Sidhu A, Sharma VK. In silico rationalized novel low molecular weight 1,2,4-triazolyldithiocarbamates: Design, synthesis, and mycocidal potential. RUSS J APPL CHEM+ 2017. [DOI: 10.1134/s1070427217060222] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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53
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Zhang L, Ai H, Chen W, Yin Z, Hu H, Zhu J, Zhao J, Zhao Q, Liu H. CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods. Sci Rep 2017; 7:2118. [PMID: 28522849 PMCID: PMC5437031 DOI: 10.1038/s41598-017-02365-0] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 04/10/2017] [Indexed: 01/11/2023] Open
Abstract
Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models (http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/).
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Affiliation(s)
- Li Zhang
- School of Life Science, Liaoning University, Shenyang, 110036, China.,Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China
| | - Haixin Ai
- School of Life Science, Liaoning University, Shenyang, 110036, China.,Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China.,Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China
| | - Wen Chen
- School of Information, Liaoning University, Shenyang, 110036, China
| | - Zimo Yin
- School of Information, Liaoning University, Shenyang, 110036, China
| | - Huan Hu
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Junfeng Zhu
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Jian Zhao
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Qi Zhao
- Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China.,School of Mathematics, Liaoning University, Shenyang, 110036, China
| | - Hongsheng Liu
- School of Life Science, Liaoning University, Shenyang, 110036, China. .,Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China. .,Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China.
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54
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Kanode R, Chandra S, Sharma S. Application of bacterial reverse mutation assay for detection of non-genotoxic carcinogens. Toxicol Mech Methods 2017; 27:376-381. [DOI: 10.1080/15376516.2017.1300616] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Rewan Kanode
- Drug Safety Assessment, Novel Drug Discovery & Development, Lupin Limited (Research Park), Taluka-Mulshi, Pune, India
| | - Saurabh Chandra
- Drug Safety Assessment, Novel Drug Discovery & Development, Lupin Limited (Research Park), Taluka-Mulshi, Pune, India
| | - Sharad Sharma
- Drug Safety Assessment, Novel Drug Discovery & Development, Lupin Limited (Research Park), Taluka-Mulshi, Pune, India
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55
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Floris M, Raitano G, Medda R, Benfenati E. Fragment Prioritization on a Large Mutagenicity Dataset. Mol Inform 2016; 36. [PMID: 28032691 DOI: 10.1002/minf.201600133] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Accepted: 12/11/2016] [Indexed: 11/08/2022]
Abstract
The identification of structural alerts is one of the simplest tools used for the identification of potentially toxic chemical compounds. Structural alerts have served as an aid to quickly identify chemicals that should be either prioritized for testing or for elimination from further consideration and use. In the recent years, the availability of larger datasets, often growing in the context of collaborative efforts and competitions, created the raw material needed to identify new and more accurate structural alerts. This work applied a method to efficiently mine large toxicological dataset for structural alert showing a strong statistical association with mutagenicity. In details, we processed a large Ames mutagenicity dataset comprising 14,015 unique molecules obtained by joining different data sources. After correction for multiple testing, we were able to assign a probability value to each fragment. A total of 51 rules were identified, with p-value < 0.05. Using the same method, we also confirmed the statistical significance of several mutagenicity rules already present and largely recognized in the literature. In addition, we have extended the application of our method by predicting the mutagenicity of an external data set.
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Affiliation(s)
- Matteo Floris
- CRS4 - Center for advanced studies, research and development in Sardinia, Loc. Piscina Manna, Building 1, 09010, Pula (CA), Italy.,Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Giuseppa Raitano
- IRCCS - Istituto di Ricerche Farmacologiche "Mario Negri", Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, Via La Masa 19, 20159, Milan, Italy
| | - Ricardo Medda
- CRS4 - Center for advanced studies, research and development in Sardinia, Loc. Piscina Manna, Building 1, 09010, Pula (CA), Italy
| | - Emilio Benfenati
- IRCCS - Istituto di Ricerche Farmacologiche "Mario Negri", Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, Via La Masa 19, 20159, Milan, Italy
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56
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In silico prediction of the mutagenicity of nitroaromatic compounds using a novel two-QSAR approach. Toxicol In Vitro 2016; 40:102-114. [PMID: 28027902 DOI: 10.1016/j.tiv.2016.12.013] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Revised: 11/13/2016] [Accepted: 12/21/2016] [Indexed: 11/20/2022]
Abstract
Certain drugs are nitroaromatic compounds, which are potentially toxic. As such, it is of practical importance to assess and predict their mutagenic potency in the process of drug discovery. A classical quantitative structure-activity relationship (QSAR) model was developed using the linear partial least square (PLS) scheme to understand the underline mutagenic mechanism and a non-classical QSAR model was derived using the machine learning-based hierarchical support vector regression (HSVR) to predict the mutagenicity of nitroaromatic compounds based on a series of mutagenicity data (TA98-S9). It was observed that HSVR performed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical validations. A mock test designated to mimic real challenges also confirmed the better performance of HSVR. Furthermore, HSVR exhibited superiority in predictivity, generalization capabilities, consistent performance, and robustness when compared with various published predictive models. PLS, conversely, revealed some mechanistically interpretable relationships between descriptors and mutagenicity. Thus, this two-QSAR approach using the predictive HSVR and interpretable PLS models in a synergistic fashion can be adopted to facilitate drug discovery and development by designing safer drug candidates with nitroaromatic moiety.
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57
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Allen TEH, Goodman JM, Gutsell S, Russell PJ. A History of the Molecular Initiating Event. Chem Res Toxicol 2016; 29:2060-2070. [PMID: 27989138 DOI: 10.1021/acs.chemrestox.6b00341] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The adverse outcome pathway (AOP) framework provides an alternative to traditional in vivo experiments for the risk assessment of chemicals. AOPs consist of a number of key events (KEs) linked by key event relationships across a range of biological organization backed by scientific evidence. The first KE in the pathway is the molecular initiating event (MIE)-the initial chemical trigger that starts an AOP. Over the past 3 years the AOP conceptual framework has gained a large amount of momentum in toxicology as an alternative to animal methods, and so the MIE has come into the spotlight. What is an MIE? How can MIEs be measured or predicted? What research is currently contributing to our understanding of MIEs? In this Perspective we outline answers to these key questions.
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Affiliation(s)
- Timothy E H Allen
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Jonathan M Goodman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Steve Gutsell
- Unilever Safety and Environmental Assurance Centre , Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Paul J Russell
- Unilever Safety and Environmental Assurance Centre , Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
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58
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Mutagenic and carcinogenic structural alerts and their mechanisms of action. Arh Hig Rada Toksikol 2016; 67:169-182. [DOI: 10.1515/aiht-2016-67-2801] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Accepted: 09/01/2016] [Indexed: 12/24/2022] Open
Abstract
Abstract
Knowing the mutagenic and carcinogenic properties of chemicals is very important for their hazard (and risk) assessment. One of the crucial events that trigger genotoxic and sometimes carcinogenic effects is the forming of adducts between chemical compounds and nucleic acids and histones. This review takes a look at the mechanisms related to specific functional groups (structural alerts or toxicophores) that may trigger genotoxic or epigenetic effects in the cells. We present up-to-date information about defined structural alerts with their mechanisms and the software based on this knowledge (QSAR models and classification schemes).
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59
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Papamokos G, Silins I. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action. Front Pharmacol 2016; 7:284. [PMID: 27625608 PMCID: PMC5003827 DOI: 10.3389/fphar.2016.00284] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 08/18/2016] [Indexed: 12/28/2022] Open
Abstract
There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.
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Affiliation(s)
- George Papamokos
- Department of Physics and School of Engineering and Applied Sciences, Harvard UniversityCambridge, MA, USA; Department of Physics, University of IoanninaIoannina, Greece; Biomedical Research Division, Institute of Molecular Biology and Biotechnology Foundation for Research and TechnologyHeraklion, Greece
| | - Ilona Silins
- Institute of Environmental Medicine, Karolinska Institutet Stockholm, Sweden
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60
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John A, Sivashanmugam M, Umashankar V, Natarajan SK. Virtual screening, molecular dynamics, and binding free energy calculations on human carbonic anhydrase IX catalytic domain for deciphering potential leads. J Biomol Struct Dyn 2016; 35:2155-2168. [DOI: 10.1080/07391102.2016.1207565] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Arun John
- Centre for Bioinformatics, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Vision Research Foundation, Sankara Nethralaya, Chennai 600006, Tamil Nadu, India
- School of Chemical and Biotechnology, SASTRA University, Thanjavur, Tamil Nadu, India
| | - Muthukumaran Sivashanmugam
- Centre for Bioinformatics, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Vision Research Foundation, Sankara Nethralaya, Chennai 600006, Tamil Nadu, India
- School of Chemical and Biotechnology, SASTRA University, Thanjavur, Tamil Nadu, India
| | - Vetrivel Umashankar
- Centre for Bioinformatics, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Vision Research Foundation, Sankara Nethralaya, Chennai 600006, Tamil Nadu, India
| | - Sulochana Konerirajapuram Natarajan
- R.S. Mehta Jain Department of Biochemistry and Cell Biology, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Vision Research Foundation, Sankara Nethralaya, Chennai 600006, Tamil Nadu, India
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61
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Auerbach SS. In vivo Signatures of Genotoxic and Non-genotoxic Chemicals. TOXICOGENOMICS IN PREDICTIVE CARCINOGENICITY 2016. [DOI: 10.1039/9781782624059-00113] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
This chapter reviews the findings from a broad array of in vivo genomic studies with the goal of identifying a general signature of genotoxicity (GSG) that is indicative of exposure to genotoxic agents (i.e. agents that are active in either the bacterial mutagenesis and/or the in vivo micronucleus test). While the GSG has largely emerged from systematic studies of rat and mouse liver, its response is evident across a broad collection of genotoxic treatments that cover a variety of tissues and species. Pathway-based characterization of the GSG indicates that it is enriched with genes that are regulated by p53. In addition to the GSG, another pan-tissue signature related to bone marrow suppression (a common effect of genotoxic agent exposure) is reviewed. Overall, these signatures are quite effective in identifying genotoxic agents; however, there are situations where false positive findings can occur, for example when necrotizing doses of non-genotoxic soft electrophiles (e.g. thioacetamide) are used. For this reason specific suggestions for best practices for generating for use in the creation and application of in vivo genomic signatures are reviewed.
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Affiliation(s)
- Scott S. Auerbach
- Toxicoinformatic Group, Biomolecular Screening Branch, Division of the National Toxicology Program, National Institute of Environmental Health Sciences PO Box 12233 MD K2-17 Research Triangle Park NC 27709 USA
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62
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Mostafa SM, Islam ABMMK. An in silico approach predicted potential therapeutics that can confer protection from maximum pathogenic Hantaviruses. Future Virol 2016. [DOI: 10.2217/fvl-2016-0046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Aim: In silico approach is used to identify most potent epitope and drug against pathogenic Hantavirus against which no approved therapeutics exist. Methods: Nucleocapsid protein sequences were retrieved, aligned and conserved regions were analyzed for the presence of B- and T-cell epitopes, and pockets for potential drugs. Results: T-cell epitope SYLRRTQSM and B-cell epitopes SYLRRTQ and YLRRTQSM appeared to be highly conserved, antigenic, nonallergenic. The T-cell epitope bound to maximum alleles. Thus, SYLRRTQSM would likely elicit both T- and B-cell immunity. High-throughput screening of Traditional Chinese Medicine database by docking technique revealed a potential drug, compound 46547 (1R,11S,15S,18S,20S,21R,22S)-12-oxa-8,17-diazaheptacyclo[15.5.2.0^{1,18}.0^{2,7}.0^{8,22}.0^{11,21}.0^{15,20}]tetracosa-2,4,6-trien-9-one. Conclusion: Our results predict potential therapeutics against multiple strains of pathogenic Hantavirus, but requires validation by in vivo experimentation.
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Affiliation(s)
- Salwa Mohd Mostafa
- Department of Genetic Engineering & Biotechnology, University of Dhaka, Science Complex Building, Dhaka 1000, Bangladesh
| | - Abul BMMK Islam
- Department of Genetic Engineering & Biotechnology, University of Dhaka, Science Complex Building, Dhaka 1000, Bangladesh
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63
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Luanpitpong S, Wang L, Davidson DC, Riedel H, Rojanasakul Y. Carcinogenic Potential of High Aspect Ratio Carbon Nanomaterials. ENVIRONMENTAL SCIENCE. NANO 2016; 3:483-493. [PMID: 27570625 PMCID: PMC4996468 DOI: 10.1039/c5en00238a] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Engineered nanomaterials, including high aspect ratio carbon nanomaterials, are often commercialized without a complete human risk assessment and safety evaluation. A health concern has been raised that high aspect ratio nanomaterials such as carbon nanotubes may cause unintended health consequences, such as asbestos-like lung cancer and mesothelioma, when chronically inhaled. Considering the widespread industrial and clinical applications and the increasing incidence of human exposure to nanomaterials, it is important to address the issue of nanomaterial carcinogenicity in a timely manner. This review summarizes recent advances in nanomaterial genotoxicity and carcinogenicity with a focus on high aspect ratio carbon nanotubes, and discusses current knowledge gaps and future research directions.
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Affiliation(s)
- Sudjit Luanpitpong
- Siriraj Center of Excellence for Stem Cell Research, Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Department of Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506, USA
| | - Liying Wang
- Allergy and Clinical Immunology Branch, National Institute for Occupational Safety and Health, Morgantown, WV 26505, USA
| | - Donna C. Davidson
- Allergy and Clinical Immunology Branch, National Institute for Occupational Safety and Health, Morgantown, WV 26505, USA
| | - Heimo Riedel
- Department of Biochemistry, West Virginia University, Morgantown, WV 26506, USA
- Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506, USA
| | - Yon Rojanasakul
- Department of Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506, USA
- Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506, USA
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64
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Petkov PI, Schultz TW, Donner EM, Honma M, Morita T, Hamada S, Wakata A, Mishima M, Maniwa J, Todorov M, Kaloyanova E, Kotov S, Mekenyan OG. Integrated approach to testing and assessment for predicting rodent genotoxic carcinogenicity. J Appl Toxicol 2016; 36:1536-1550. [DOI: 10.1002/jat.3338] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Revised: 03/18/2016] [Accepted: 03/23/2016] [Indexed: 12/27/2022]
Affiliation(s)
- Petko I. Petkov
- Laboratory of Mathematical Chemistry (LMC); As. Zlatarov University; Bourgas Bulgaria
| | - Terry W. Schultz
- College of Veterinary Medicine; The University of Tennessee; Knoxville TN 37996-4500 USA
| | - E. Maria Donner
- DuPont Haskell Global Centers for Health and Environmental Sciences, Newark; DE USA
| | - Masamitsu Honma
- Division of Genetics and Mutagenesis; National Institute of Health Sciences; Tokyo Japan
| | - Takeshi Morita
- Division of Risk Assessment; National Institute of Health Sciences; Tokyo Japan
| | | | | | - Masayuki Mishima
- Chugai Pharmaceutical Co., Ltd., Fuji Gotemba Research Labs; Shizuoka Japan
| | - Jiro Maniwa
- Clinical Science Division, Research & Development AstraZeneca KK; Osaka Japan
| | - Milen Todorov
- Laboratory of Mathematical Chemistry (LMC); As. Zlatarov University; Bourgas Bulgaria
| | - Elena Kaloyanova
- Laboratory of Mathematical Chemistry (LMC); As. Zlatarov University; Bourgas Bulgaria
| | - Stefan Kotov
- Laboratory of Mathematical Chemistry (LMC); As. Zlatarov University; Bourgas Bulgaria
| | - Ovanes G. Mekenyan
- Laboratory of Mathematical Chemistry (LMC); As. Zlatarov University; Bourgas Bulgaria
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65
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Luijten M, Olthof ED, Hakkert BC, Rorije E, van der Laan JW, Woutersen RA, van Benthem J. An integrative test strategy for cancer hazard identification. Crit Rev Toxicol 2016; 46:615-39. [PMID: 27142259 DOI: 10.3109/10408444.2016.1171294] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Assessment of genotoxic and carcinogenic potential is considered one of the basic requirements when evaluating possible human health risks associated with exposure to chemicals. Test strategies currently in place focus primarily on identifying genotoxic potential due to the strong association between the accumulation of genetic damage and cancer. Using genotoxicity assays to predict carcinogenic potential has the significant drawback that risks from non-genotoxic carcinogens remain largely undetected unless carcinogenicity studies are performed. Furthermore, test systems already developed to reduce animal use are not easily accepted and implemented by either industries or regulators. This manuscript reviews the test methods for cancer hazard identification that have been adopted by the regulatory authorities, and discusses the most promising alternative methods that have been developed to date. Based on these findings, a generally applicable tiered test strategy is proposed that can be considered capable of detecting both genotoxic as well as non-genotoxic carcinogens and will improve understanding of the underlying mode of action. Finally, strengths and weaknesses of this new integrative test strategy for cancer hazard identification are presented.
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Affiliation(s)
- Mirjam Luijten
- a Centre for Health Protection, National Institute for Public Health and the Environment (RIVM) , Bilthoven , the Netherlands
| | - Evelyn D Olthof
- a Centre for Health Protection, National Institute for Public Health and the Environment (RIVM) , Bilthoven , the Netherlands
| | - Betty C Hakkert
- b Centre for Safety of Substances and Products, National Institute for Public Health and the Environment (RIVM) , Bilthoven , the Netherlands
| | - Emiel Rorije
- b Centre for Safety of Substances and Products, National Institute for Public Health and the Environment (RIVM) , Bilthoven , the Netherlands
| | | | - Ruud A Woutersen
- d Netherlands Organization for Applied Scientific Research (TNO) , Zeist , the Netherlands
| | - Jan van Benthem
- a Centre for Health Protection, National Institute for Public Health and the Environment (RIVM) , Bilthoven , the Netherlands
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66
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Mangiatordi GF, Alberga D, Altomare CD, Carotti A, Catto M, Cellamare S, Gadaleta D, Lattanzi G, Leonetti F, Pisani L, Stefanachi A, Trisciuzzi D, Nicolotti O. Mind the Gap! A Journey towards Computational Toxicology. Mol Inform 2016; 35:294-308. [PMID: 27546034 DOI: 10.1002/minf.201501017] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Accepted: 03/23/2016] [Indexed: 11/11/2022]
Abstract
Computational methods have advanced toxicology towards the development of target-specific models based on a clear cause-effect rationale. However, the predictive potential of these models presents strengths and weaknesses. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. On the other, the unconscious use of in silico methods can mislead end-users with elusive results. The focus of this review is on the basic scientific and regulatory recommendations in the derivation and application of computational models. Attention is paid to examine the interplay between computational toxicology and drug discovery and development. Avoiding the easy temptation of an overoptimistic future, we report our view on what can, or cannot, realistically be done. Indeed, studies of safety/toxicity represent a key element of chemical prioritization programs carried out by chemical industries, and primarily by pharmaceutical companies.
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Affiliation(s)
- Giuseppe Felice Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Domenico Alberga
- Dipartimento Interateneo di Fisica 'M.Merlin', Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Angelo Carotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Marco Catto
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Saverio Cellamare
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Domenico Gadaleta
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Gianluca Lattanzi
- Dipartimento Interateneo di Fisica 'M.Merlin', Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Francesco Leonetti
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Leonardo Pisani
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Angela Stefanachi
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università di Bari 'AldoMoro', Via Orabona, 4, 70126, Bari, Italy.
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Golbamaki A, Benfenati E, Golbamaki N, Manganaro A, Merdivan E, Roncaglioni A, Gini G. New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS 2016; 34:97-113. [PMID: 26986491 DOI: 10.1080/10590501.2016.1166879] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In this study, new molecular fragments associated with genotoxic and nongenotoxic carcinogens are introduced to estimate the carcinogenic potential of compounds. Two rule-based carcinogenesis models were developed with the aid of SARpy: model R (from rodents' experimental data) and model E (from human carcinogenicity data). Structural alert extraction method of SARpy uses a completely automated and unbiased manner with statistical significance. The carcinogenicity models developed in this study are collections of carcinogenic potential fragments that were extracted from two carcinogenicity databases: the ANTARES carcinogenicity dataset with information from bioassay on rats and the combination of ISSCAN and CGX datasets, which take into accounts human-based assessment. The performance of these two models was evaluated in terms of cross-validation and external validation using a 258 compound case study dataset. Combining R and H predictions and scoring a positive or negative result when both models are concordant on a prediction, increased accuracy to 72% and specificity to 79% on the external test set. The carcinogenic fragments present in the two models were compared and analyzed from the point of view of chemical class. The results of this study show that the developed rule sets will be a useful tool to identify some new structural alerts of carcinogenicity and provide effective information on the molecular structures of carcinogenic chemicals.
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Affiliation(s)
- Azadi Golbamaki
- a Laboratory of Environmental Chemistry and Toxicology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - Emilio Benfenati
- a Laboratory of Environmental Chemistry and Toxicology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - Nazanin Golbamaki
- b DRC/VIVA/METO Unit, Institut National de l.Environnement Industriel et des Risques (INERIS), Parc Technologique Alata , Verneuil en Halatte , France
| | - Alberto Manganaro
- a Laboratory of Environmental Chemistry and Toxicology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - Erinc Merdivan
- c Faculty of Engineering and Natural Sciences, Sabancı University , Tuzla/Istanbul , Turkey
| | - Alessandra Roncaglioni
- a Laboratory of Environmental Chemistry and Toxicology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
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Raies AB, Bajic VB. In silico toxicology: computational methods for the prediction of chemical toxicity. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2016; 6:147-172. [PMID: 27066112 PMCID: PMC4785608 DOI: 10.1002/wcms.1240] [Citation(s) in RCA: 339] [Impact Index Per Article: 42.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Revised: 10/27/2015] [Accepted: 11/10/2015] [Indexed: 01/08/2023]
Abstract
Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models. WIREs Comput Mol Sci 2016, 6:147-172. doi: 10.1002/wcms.1240 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Arwa B Raies
- King Abdullah University of Science and Technology (KAUST) Computational Bioscience Research Centre (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE) Thuwal Saudi Arabia
| | - Vladimir B Bajic
- King Abdullah University of Science and Technology (KAUST) Computational Bioscience Research Centre (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE) Thuwal Saudi Arabia
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Hsu KH, Su BH, Tu YS, Lin OA, Tseng YJ. Mutagenicity in a Molecule: Identification of Core Structural Features of Mutagenicity Using a Scaffold Analysis. PLoS One 2016; 11:e0148900. [PMID: 26863515 PMCID: PMC4749256 DOI: 10.1371/journal.pone.0148900] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Accepted: 01/24/2016] [Indexed: 11/24/2022] Open
Abstract
With advances in the development and application of Ames mutagenicity in silico prediction tools, the International Conference on Harmonisation (ICH) has amended its M7 guideline to reflect the use of such prediction models for the detection of mutagenic activity in early drug safety evaluation processes. Since current Ames mutagenicity prediction tools only focus on functional group alerts or side chain modifications of an analog series, these tools are unable to identify mutagenicity derived from core structures or specific scaffolds of a compound. In this study, a large collection of 6512 compounds are used to perform scaffold tree analysis. By relating different scaffolds on constructed scaffold trees with Ames mutagenicity, four major and one minor novel mutagenic groups of scaffold are identified. The recognized mutagenic groups of scaffold can serve as a guide for medicinal chemists to prevent the development of potentially mutagenic therapeutic agents in early drug design or development phases, by modifying the core structures of mutagenic compounds to form non-mutagenic compounds. In addition, five series of substructures are provided as recommendations, for direct modification of potentially mutagenic scaffolds to decrease associated mutagenic activities.
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Affiliation(s)
- Kuo-Hsiang Hsu
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Bo-Han Su
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Yi-Shu Tu
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Olivia A. Lin
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Yufeng J. Tseng
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
- * E-mail:
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Kehrer JP, Klotz LO. Free radicals and related reactive species as mediators of tissue injury and disease: implications for Health. Crit Rev Toxicol 2015; 45:765-98. [DOI: 10.3109/10408444.2015.1074159] [Citation(s) in RCA: 180] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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71
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The Syrian hamster embryo cells transformation assay identifies efficiently nongenotoxic carcinogens, and can contribute to alternative, integrated testing strategies. MUTATION RESEARCH-GENETIC TOXICOLOGY AND ENVIRONMENTAL MUTAGENESIS 2015; 779:35-8. [DOI: 10.1016/j.mrgentox.2015.02.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2015] [Revised: 02/05/2015] [Accepted: 02/07/2015] [Indexed: 01/10/2023]
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Kossler N, Matheis KA, Ostenfeldt N, Bach Toft D, Dhalluin S, Deschl U, Kalkuhl A. Identification of specific mRNA signatures as fingerprints for carcinogenesis in mice induced by genotoxic and nongenotoxic hepatocarcinogens. Toxicol Sci 2014; 143:277-95. [PMID: 25410580 DOI: 10.1093/toxsci/kfu248] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Long-term rodent carcinogenicity studies for evaluation of chemicals and pharmaceuticals concerning their carcinogenic potential to humans are currently receiving critical revision. Additional data from mechanistic studies can support cancer risk assessment by clarifying the underlying mode of action. In the course of the IMI MARCAR project, a European consortium of EFPIA partners and academics, which aims to identify biomarkers for nongenotoxic carcinogenesis, a toxicogenomic mouse liver database was generated. CD-1 mice were orally treated for 3 and 14 days with 3 known genotoxic hepatocarcinogens: C.I. Direct Black 38, Dimethylnitrosamine and 4,4'-Methylenedianiline; 3 nongenotoxic hepatocarcinogens: 1,4-Dichlorobenzene, Phenobarbital sodium and Piperonyl butoxide; 4 nonhepatocarcinogens: Cefuroxime sodium, Nifedipine, Prazosin hydrochloride and Propranolol hydrochloride; and 3 compounds that show ambiguous results in genotoxicity testing: Cyproterone acetate, Thioacetamide and Wy-14643. By liver mRNA expression analysis using individual animal data, we identified 64 specific biomarker candidates for genotoxic carcinogens and 69 for nongenotoxic carcinogens for male mice at day 15. The majority of genotoxic carcinogen biomarker candidates possess functions in DNA damage response (eg, apoptosis, cell cycle progression, DNA repair). Most of the identified nongenotoxic carcinogen biomarker candidates are involved in regulation of cell cycle progression and apoptosis. The derived biomarker lists were characterized with respect to their dependency on study duration and gender and were successfully used to characterize carcinogens with ambiguous genotoxicity test results, such as Wy-14643. The identified biomarker candidates improve the mechanistic understanding of drug-induced effects on the mouse liver that result in hepatocellular adenomas and/or carcinomas in 2-year mouse carcinogenicity studies.
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Affiliation(s)
- Nadine Kossler
- *Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach an der Riss, Germany, H. Lundbeck A/S, 2500 Valby, Denmark and UCB Pharma S.A., 1070 Brussels, Belgium
| | - Katja A Matheis
- *Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach an der Riss, Germany, H. Lundbeck A/S, 2500 Valby, Denmark and UCB Pharma S.A., 1070 Brussels, Belgium
| | - Nina Ostenfeldt
- *Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach an der Riss, Germany, H. Lundbeck A/S, 2500 Valby, Denmark and UCB Pharma S.A., 1070 Brussels, Belgium
| | - Dorthe Bach Toft
- *Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach an der Riss, Germany, H. Lundbeck A/S, 2500 Valby, Denmark and UCB Pharma S.A., 1070 Brussels, Belgium
| | - Stéphane Dhalluin
- *Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach an der Riss, Germany, H. Lundbeck A/S, 2500 Valby, Denmark and UCB Pharma S.A., 1070 Brussels, Belgium
| | - Ulrich Deschl
- *Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach an der Riss, Germany, H. Lundbeck A/S, 2500 Valby, Denmark and UCB Pharma S.A., 1070 Brussels, Belgium
| | - Arno Kalkuhl
- *Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach an der Riss, Germany, H. Lundbeck A/S, 2500 Valby, Denmark and UCB Pharma S.A., 1070 Brussels, Belgium
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Nicolotti O, Benfenati E, Carotti A, Gadaleta D, Gissi A, Mangiatordi GF, Novellino E. REACH and in silico methods: an attractive opportunity for medicinal chemists. Drug Discov Today 2014; 19:1757-1768. [PMID: 24998783 DOI: 10.1016/j.drudis.2014.06.027] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2014] [Revised: 05/06/2014] [Accepted: 06/26/2014] [Indexed: 11/30/2022]
Abstract
REACH, the most ambitious chemical legislation in the world, provides unprecedented opportunities for medicinal chemists. Companies must report (eco)toxicological information about their chemicals, disseminated to the public domain by the European Chemicals Agency after their registration. The availability of this wealth of new toxicological data, together with the promotion of REACH of in silico methods, appoints medicinal chemists to a leading role in the regulatory hazard assessment process. In fact, Quantitative Structure-Activity Relation ship (QSAR) models and predictive toxicology have been applied in drug design and development for decades. Here, we discuss toxicological endpoints and areas where further development is needed to provide an enlightened appraisal of this attractive new opportunity.
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Affiliation(s)
- Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari 'Aldo Moro', Via E. Orabona 4, 70125 Bari, Italy.
| | - Emilio Benfenati
- IRCCS-Istituto di Ricerche Farmacologiche 'Mario Negri', Via La Masa 19, 20156 Milano, Italy
| | - Angelo Carotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari 'Aldo Moro', Via E. Orabona 4, 70125 Bari, Italy
| | - Domenico Gadaleta
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari 'Aldo Moro', Via E. Orabona 4, 70125 Bari, Italy
| | - Andrea Gissi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari 'Aldo Moro', Via E. Orabona 4, 70125 Bari, Italy
| | - Giuseppe Felice Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari 'Aldo Moro', Via E. Orabona 4, 70125 Bari, Italy
| | - Ettore Novellino
- Dipartimento di Farmacia - Università degli Studi di Napoli 'Federico II', Via D. Montesano 49, 80131 Napoli, Italy
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74
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Benigni R. Predicting the carcinogenicity of chemicals with alternative approaches: recent advances. Expert Opin Drug Metab Toxicol 2014; 10:1199-208. [PMID: 24972624 DOI: 10.1517/17425255.2014.934670] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
INTRODUCTION Alternative approaches to the rodent bioassay are necessary for early identification of problematic drugs and biocides during the development process, and are the only practicable tool for assessing environmental chemicals with no or adequate safety documentation. AREAS COVERED This review informs on: i) the traditional prescreening through genotoxicity testing; ii) an integrative approach that assesses DNA-reactivity and ability to disorganize tissues; iii) new applications of omics technologies (ToxCast/Tox21 project); iv) a pragmatic approach aimed at filling data gaps by intrapolating/extrapolating from similar chemicals (read-across, category formation). The review also approaches the issue of the concerns about false-positive and false-negative results that prevents a wider acceptance and use of alternatives. EXPERT OPINION The review addresses strengths and limitations of various proposals, and concludes on the need of differential approaches to the issue of false negatives and false positives. False negatives can be eliminated or reduced below the variability of the animal assay with conservative quantitative structure-activity relationships or in vitro tests; false positives can be cleared with ad hoc mechanistically based follow-ups. This framework can permit a reduction of animal testing and a better protection of human health.
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Affiliation(s)
- Romualdo Benigni
- Istituto Superiore di Sanita', Environment and Health Department , Viale Regina Elena 299, Rome 00161 , Italy +39 06 49902579 ; +39 06 49902999 ;
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75
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Römer M, Eichner J, Metzger U, Templin MF, Plummer S, Ellinger-Ziegelbauer H, Zell A. Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat. PLoS One 2014; 9:e97640. [PMID: 24830643 PMCID: PMC4022579 DOI: 10.1371/journal.pone.0097640] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2014] [Accepted: 04/10/2014] [Indexed: 02/07/2023] Open
Abstract
In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens.
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Affiliation(s)
- Michael Römer
- Center of Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Johannes Eichner
- Center of Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Ute Metzger
- Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany
| | - Markus F. Templin
- Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany
| | - Simon Plummer
- CXR Biosciences, James Lindsay Place, Dundee Technopole, Dundee, Scotland, United Kingdom
| | | | - Andreas Zell
- Center of Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
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76
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Fjodorova N, Novič M. Comparison of criteria used to access carcinogenicity in CPANN QSAR models versus the knowledge-based expert system Toxtree. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2014; 25:423-441. [PMID: 24716754 DOI: 10.1080/1062936x.2014.898687] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The primary goal of this study was to describe and compare the criteria used to assess carcinogenic activity. The statistically-based predictive quantitative structure-activity relationship (QSAR) models based on the counter propagation artificial neural network (CPANN) algorithm, and knowledge-based expert systems based on a decision tree structural alert (SA) approach (Toxtree application), were considered. The integration of the QSAR (CPANN models) and SAR (Toxtree SA application) approach contributed to the mechanistic understanding of the QSAR model considered. The mapping technique inherent to CPANN Kohonen enables us to relate the similarities or dissimilarities within a congeneric set of chemicals with particular SAs for carcinogenicity. The focus of our investigations was the similarities and dissimilarities of the features used in the QSAR and SAR methods. Due to the complexity of the carcinogenic endpoint, the integration of different approaches allows the models to be improved and provides a valuable technique for evaluating the safety of chemicals.
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Affiliation(s)
- N Fjodorova
- a National Institute of Chemistry , Hajdrihova, Ljubljana , Slovenia
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Benigni R, Bossa C, Battistelli CL, Tcheremenskaia O. IARC classes 1 and 2 carcinogens are successfully identified by an alternative strategy that detects DNA-reactivity and cell transformation ability of chemicals. MUTATION RESEARCH. GENETIC TOXICOLOGY AND ENVIRONMENTAL MUTAGENESIS 2013; 758:56-61. [PMID: 24076401 DOI: 10.1016/j.mrgentox.2013.09.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2013] [Revised: 09/09/2013] [Accepted: 09/19/2013] [Indexed: 06/02/2023]
Abstract
For decades, traditional toxicology has been the ultimate source of information on the carcinogenic potential of chemicals; however with increasing demand on regulation of chemicals and decreasing resources for testing, opportunities to accept "alternative" approaches have dramatically expanded. The need for tools able to identify carcinogens in shorter times and at a lower cost in terms of animal lives and money is still an open issue, and the present strategies and regulations for carcinogenicity pre-screening do not adequately protect human health. In previous papers, we have proposed an integrated in vitro/in silico strategy that detects DNA-reactivity and tissue disorganization/disruption by chemicals, and we have shown that the combination of Salmonella and Structural Alerts for the DNA-reactive carcinogens, and in vitro cell transformation assays for nongenotoxic carcinogens permits the identification of a very large proportion (up to 95%) of rodent carcinogens, while having a considerable specificity with the rodent noncarcinogens. In the present paper we expand the previous investigation and show that this alternative strategy identifies correctly IARC Classes 1 and 2 carcinogens. If implemented, this alternative strategy can contribute to improve the protection of human health while decreasing the use of animals.
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Affiliation(s)
- Romualdo Benigni
- Istituto Superiore di Sanita', Environment and Health Department, Viale Regina Elena 299, 00161 Rome, Italy.
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Patlewicz G, Ball N, Booth ED, Hulzebos E, Zvinavashe E, Hennes C. Use of category approaches, read-across and (Q)SAR: General considerations. Regul Toxicol Pharmacol 2013; 67:1-12. [DOI: 10.1016/j.yrtph.2013.06.002] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2013] [Revised: 05/25/2013] [Accepted: 06/03/2013] [Indexed: 10/26/2022]
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A toxicogenomic approach for the prediction of murine hepatocarcinogenesis using ensemble feature selection. PLoS One 2013; 8:e73938. [PMID: 24040119 PMCID: PMC3769381 DOI: 10.1371/journal.pone.0073938] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2013] [Accepted: 07/24/2013] [Indexed: 01/19/2023] Open
Abstract
The current strategy for identifying the carcinogenicity of drugs involves the 2-year bioassay in male and female rats and mice. As this assay is cost-intensive and time-consuming there is a high interest in developing approaches for the screening and prioritization of drug candidates in preclinical safety evaluations. Predictive models based on toxicogenomics investigations after short-term exposure have shown their potential for assessing the carcinogenic risk. In this study, we investigated a novel method for the evaluation of toxicogenomics data based on ensemble feature selection in conjunction with bootstrapping for the purpose to derive reproducible and characteristic multi-gene signatures. This method was evaluated on a microarray dataset containing global gene expression data from liver samples of both male and female mice. The dataset was generated by the IMI MARCAR consortium and included gene expression profiles of genotoxic and nongenotoxic hepatocarcinogens obtained after treatment of CD-1 mice for 3 or 14 days. We developed predictive models based on gene expression data of both sexes and the models were employed for predicting the carcinogenic class of diverse compounds. Comparing the predictivity of our multi-gene signatures against signatures from literature, we demonstrated that by incorporating our gene sets as features slightly higher accuracy is on average achieved by a representative set of state-of-the art supervised learning methods. The constructed models were also used for the classification of Cyproterone acetate (CPA), Wy-14643 (WY) and Thioacetamid (TAA), whose primary mechanism of carcinogenicity is controversially discussed. Based on the extracted mouse liver gene expression patterns, CPA would be predicted as a nongenotoxic compound. In contrast, both WY and TAA would be classified as genotoxic mouse hepatocarcinogens.
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80
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Benigni R. Evaluation of the toxicity forecasting capability of EPA's ToxCast Phase I data: can ToxCast in vitro assays predict carcinogenicity? JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS 2013; 31:201-212. [PMID: 24024519 DOI: 10.1080/10590501.2013.824188] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Long-term rodent bioassays have played a central role in protecting human health from carcinogens; for ethical and practical reasons their use is decreasing whereas genotoxicity testing has taken a pivotal role. However, this strategy--as presently implemented--is not sensitive enough to detect all genotoxic carcinogens, and cannot detect nongenotoxic carcinogens. Among the alternative approaches under study there is the ToxCast/Tox21 project. Following a previous study from our laboratory, here we present a new, more extensive analysis of ToxCast Phase I results, indicating that at the present state-of-art this approach is not able to predict the carcinogenicity of chemicals. Possible reasons for this mediocre performance are discussed, and opinions on ways to tune up the project in the next phases are presented.
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Affiliation(s)
- Romualdo Benigni
- a Istituto Superiore di Sanita' , Environment and Health Department , Rome , Italy
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