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Ghosh S, Roy K. Quantitative Read-Across Structure-Activity Relationship (q-RASAR): A novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats. Toxicology 2024:153824. [PMID: 38705560 DOI: 10.1016/j.tox.2024.153824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 04/28/2024] [Accepted: 04/29/2024] [Indexed: 05/07/2024]
Abstract
We have developed a quantitative safety prediction model for subchronic repeated doses of diverse organic chemicals on rats using the novel quantitative read-across structure-activity relationship (q-RASAR) approach, which uses similarity-based descriptors for predictive model generation. The experimental -Log (NOAEL) values have been used here as a potential indicator of oral subchronic safety on rats as it determines the maximum dose level for which no observed adverse effects of chemicals are found. A total of 186 data points of diverse organic chemicals have been used for the model generation using structural and physicochemical (0D-2D) descriptors. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0D-2D descriptors. Then, the combined pool of RASAR and the identified 0D-2D descriptors of the training set were employed to develop the final models by using the partial least squares (PLS) algorithm. The developed PLS model was rigorously validated by various internal and external validation metrics as suggested by the Organization for Economic Co-operation and Development (OECD). The final q-RASAR model is proven to be statistically sound, robust and externally predictive (R2=0.85, Q2LOO=0.82 and Q2F1=0.94) that superseding the internal as well as external predictivity of the corresponding quantitative structure-activity relationship (QSAR) model as well as previously reported subchronic repeated dose toxicity model found in the literature. In a nutshell, the q-RASAR is an effective approach that has the potential to be used as a good alternative way to improve external predictivity, interpretability, and transferability for subchronic oral safety prediction as well as ecotoxicity risk identification.
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Affiliation(s)
- Shilpayan Ghosh
- 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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2
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Reddy Ramireddy VS, Kurakula R, Velayudhaperumal Chellam P, James A, van Hullebusch ED. Systematic computational toxicity analysis of the ozonolytic degraded compounds of azo dyes: Quantitative structure-activity relationship (QSAR) and adverse outcome pathway (AOP) based approach. Environ Res 2023; 231:116142. [PMID: 37217122 DOI: 10.1016/j.envres.2023.116142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 04/27/2023] [Accepted: 05/13/2023] [Indexed: 05/24/2023]
Abstract
The present study identifies and analyses the degraded products of three azo dyes (Reactive Orange 16, Reactive Red 120, and Direct Red 80) and proffers their in silico toxicity predictions. In our previously published work, the synthetic dye effluents were degraded using an ozonolysis-based Advanced Oxidation Process. In the present study, the degraded products of the three dyes were analysed using GC-MS at endpoint strategy and further subjected to in silico toxicity analysis using Toxicity Estimation Software Tool (TEST), Prediction Of TOXicity of chemicals (ProTox-II), and Estimation Programs Interface Suite (EPI Suite). Several physiological toxicity endpoints, such as hepatotoxicity, carcinogenicity, mutagenicity, cellular and molecular interactions, were considered to assess the Quantitative Structure-Activity Relationships (QSAR) and adverse outcome pathways. The environmental fate of the by-products in terms of their biodegradability and possible bioaccumulation was also assessed. Results of ProTox-II suggested that the azo dye degradation products are carcinogenic, immunotoxic, and cytotoxic and displayed toxicity towards Androgen Receptor and Mitochondrial Membrane Potential. TEST results predicted LC50 and IGC50 values for three organisms Tetrahymena pyriformis, Daphnia magna, and Pimephales promelas. EPISUITE software via the BCFBAF module surmises that the degradation products' bioaccumulation (BAF) and bioconcentration factors (BCF) are high. The cumulative inference of the results suggests that most degradation by-products are toxic and need further remediation strategies. The study aims to complement existing tests to predict toxicity and prioritise the elimination/reduction of harmful degradation products of primary treatment procedures. The novelty of this study is that it streamlines in silico approaches to predict the nature of toxicity of degradation by-products of toxic industrial affluents like azo dyes. These approaches can assist the first phase of toxicology assessments for any pollutant for regulatory decision-making bodies to chalk out appropriate action plans for their remediation.
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Affiliation(s)
| | - Rakshitha Kurakula
- Department of Biotechnology, National Institute of Technology Andhra Pradesh, India
| | | | - Anina James
- Department of Zoology, Deen Dayal Upadhyaya College, New Delhi, India.
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3
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Smalling KL, Romanok KM, Bradley PM, Morriss MC, Gray JL, Kanagy LK, Gordon SE, Williams BM, Breitmeyer SE, Jones DK, DeCicco LA, Eagles-Smith CA, Wagner T. Per- and polyfluoroalkyl substances (PFAS) in United States tapwater: Comparison of underserved private-well and public-supply exposures and associated health implications. Environ Int 2023; 178:108033. [PMID: 37356308 DOI: 10.1016/j.envint.2023.108033] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 06/27/2023]
Abstract
Drinking-water quality is a rising concern in the United States (US), emphasizing the need to broadly assess exposures and potential health effects at the point-of-use. Drinking-water exposures to per- and poly-fluoroalkyl substances (PFAS) are a national concern, however, there is limited information on PFAS in residential tapwater at the point-of-use, especially from private-wells. We conducted a national reconnaissance to compare human PFAS exposures in unregulated private-well and regulated public-supply tapwater. Tapwater from 716 locations (269 private-wells; 447 public supply) across the US was collected during 2016-2021 including three locations where temporal sampling was conducted. Concentrations of PFAS were assessed by three laboratories and compared with land-use and potential-source metrics to explore drivers of contamination. The number of individual PFAS observed ranged from 1 to 9 (median: 2) with corresponding cumulative concentrations (sum of detected PFAS) ranging from 0.348 to 346 ng/L. Seventeen PFAS were observed at least once with PFBS, PFHxS and PFOA observed most frequently in approximately 15% of the samples. Across the US, PFAS profiles and estimated median cumulative concentrations were similar among private wells and public-supply tapwater. We estimate that at least one PFAS could be detected in about 45% of US drinking-water samples. These detection probabilities varied spatially with limited temporal variation in concentrations/numbers of PFAS detected. Benchmark screening approaches indicated potential human exposure risk was dominated by PFOA and PFOS, when detected. Potential source and land-use information was related to cumulative PFAS concentrations, and the number of PFAS detected; however, corresponding relations with specific PFAS were limited likely due to low detection frequencies and higher detection limits. Information generated supports the need for further assessments of cumulative health risks of PFAS as a class and in combination with other co-occurring contaminants, particularly in unmonitored private-wells where information is limited or not available.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Tyler Wagner
- U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit, The Pennsylvania State University, University Park, PA, USA
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Singh AV, Bansod G, Mahajan M, Dietrich P, Singh SP, Rav K, Thissen A, Bharde AM, Rothenstein D, Kulkarni S, Bill J. Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment. ACS Omega 2023; 8:21377-21390. [PMID: 37360489 PMCID: PMC10286258 DOI: 10.1021/acsomega.3c00596] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 05/09/2023] [Indexed: 06/28/2023]
Abstract
Toxicology is undergoing a digital revolution, with mobile apps, sensors, artificial intelligence (AI), and machine learning enabling better record-keeping, data analysis, and risk assessment. Additionally, computational toxicology and digital risk assessment have led to more accurate predictions of chemical hazards, reducing the burden of laboratory studies. Blockchain technology is emerging as a promising approach to increase transparency, particularly in the management and processing of genomic data related with food safety. Robotics, smart agriculture, and smart food and feedstock offer new opportunities for collecting, analyzing, and evaluating data, while wearable devices can predict toxicity and monitor health-related issues. The review article focuses on the potential of digital technologies to improve risk assessment and public health in the field of toxicology. By examining key topics such as blockchain technology, smoking toxicology, wearable sensors, and food security, this article provides an overview of how digitalization is influencing toxicology. As well as highlighting future directions for research, this article demonstrates how emerging technologies can enhance risk assessment communication and efficiency. The integration of digital technologies has revolutionized toxicology and has great potential for improving risk assessment and promoting public health.
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Affiliation(s)
- Ajay Vikram Singh
- Department
of Chemical and Product Safety, German Federal
Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589 Berlin, Germany
| | - Girija Bansod
- Rajiv
Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth (deemed to be) University, Pune 411045, India
| | - Mihir Mahajan
- Department
of Informatics, Technical University of
Munich, 85758 Garching, Germany
| | - Paul Dietrich
- SPECS
Surface Nano Analysis GmbH, Voltastrasse 5, 13355 Berlin, Germany
| | - Shivam Pratap Singh
- School
of Computer and Mathematical Sciences, University
of Greenwich, London SE10 9LS, U.K.
| | - Kranti Rav
- Delta
Biopharmaceutical, Andhra Pradesh 524126, India
| | - Andreas Thissen
- SPECS
Surface Nano Analysis GmbH, Voltastrasse 5, 13355 Berlin, Germany
| | - Aadya Mandar Bharde
- Guru
Nanak Khalsa College of Arts Science and Commerce, Mumbai 400 037, India
| | - Dirk Rothenstein
- Institute
for Materials Science, Department of Bioinspired Materials, University of Stuttgart, 70569 Stuttgart, Germany
| | - Shilpa Kulkarni
- Seeta
Nursing Home, Shivaji
Nagar, Nashik, Maharashtra 422002, India
| | - Joachim Bill
- Institute
for Materials Science, Department of Bioinspired Materials, University of Stuttgart, 70569 Stuttgart, Germany
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Romano JD, Mei L, Senn J, Moore JH, Mortensen HM. Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science. Comput Toxicol 2023; 25:100261. [PMID: 37829618 PMCID: PMC10569310 DOI: 10.1016/j.comtox.2023.100261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Adverse outcome pathways provide a powerful tool for understanding the biological signaling cascades that lead to disease outcomes following toxicity. The framework outlines downstream responses known as key events, culminating in a clinically significant adverse outcome as a final result of the toxic exposure. Here we use the AOP framework combined with artificial intelligence methods to gain novel insights into genetic mechanisms that underlie toxicity-mediated adverse health outcomes. Specifically, we focus on liver cancer as a case study with diverse underlying mechanisms that are clinically significant. Our approach uses two complementary AI techniques: Generative modeling via automated machine learning and genetic algorithms, and graph machine learning. We used data from the US Environmental Protection Agency's Adverse Outcome Pathway Database (AOP-DB; aopdb.epa.gov) and the UK Biobank's genetic data repository. We use the AOP-DB to extract disease-specific AOPs and build graph neural networks used in our final analyses. We use the UK Biobank to retrieve real-world genotype and phenotype data, where genotypes are based on single nucleotide polymorphism data extracted from the AOP-DB, and phenotypes are case/control cohorts for the disease of interest (liver cancer) corresponding to those adverse outcome pathways. We also use propensity score matching to appropriately sample based on important covariates (demographics, comorbidities, and social deprivation indices) and to balance the case and control populations in our machine language training/testing datasets. Finally, we describe a novel putative risk factor for LC that depends on genetic variation in both the aryl-hydrocarbon receptor (AHR) and ATP binding cassette subfamily B member 11 (ABCB11) genes.
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Affiliation(s)
- Joseph D. Romano
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
- Center of Excellence in Environmental Toxicology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Liang Mei
- Oak Ridge Associated Universities, Oak Ridge, TN, United States
| | - Jonathan Senn
- Oak Ridge Associated Universities, Oak Ridge, TN, United States
| | - Jason H. Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Holly M. Mortensen
- United States Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, Research Triangle Park, NC, United States
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6
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Jeong J, Choi J. Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications. Environ Sci Technol 2022; 56:7532-7543. [PMID: 35666838 DOI: 10.1021/acs.est.1c07413] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computational toxicology techniques that predict toxicity based on the structure of chemical substances are gaining attention, including the quantitative structure-activity relationship. To understand the recent development of these models, we analyzed the databases, molecular descriptors, fingerprints, and algorithms considered in recent studies. Based on a selection of 96 papers published since 2014, we found that AI models have been developed to predict approximately 30 different toxicity end points using more than 20 toxicity databases. For model development, molecular access system and extended-connectivity fingerprints are the most commonly used molecular descriptors. The most used algorithm among the machine learning techniques is the random forest, while the most used algorithm among the deep learning techniques is a deep neural network. The use of AI technology in the development of toxicity prediction models is a new concept that will aid in achieving a scientific accord and meet regulatory applications. The comprehensive overview provided in this study will provide a useful guide for the further development and application of toxicity prediction models.
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Affiliation(s)
- Jaeseong Jeong
- School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, South Korea
| | - Jinhee Choi
- School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, South Korea
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7
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Kim J, Seo M, Choi J, Na M. MRA Toolbox v. 1.0: a web-based toolbox for predicting mixture toxicity of chemical substances in chemical products. Sci Rep 2022; 12:8880. [PMID: 35614210 DOI: 10.1038/s41598-022-13028-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 05/17/2022] [Indexed: 11/13/2022] Open
Abstract
The chemical risk assessment paradigm is shifting from “substance-based” to “product/mixture-based” and from “animal testing” to “alternative testing” under chemical regulations. Organisms and the environment may be exposed to mixtures rather than a single substance. Conducting toxicity tests for all possible combinations is impractical due to the enormous combinatorial complexity. This study highlights the development and application case studies of Mixture Risk Assessment Toolbox, a novel web-based platform that supports mixture risk assessment through the use of different prediction models and public databases. This integrated framework provides new functional values for assessors to easily screen and compare the toxicity of mixture products using different computational techniques and find strategic solutions to reduce the mixture toxicity in the product development process. The toolbox (https://www.mratoolbox.org) includes four additive toxicity models: two conventional (Concentration Addition; and Independent Action) and two advanced (Generalized Concentration Addition; and Quantitative Structure–Activity Relationship-based Two-Stage Prediction) models. We demonstrated the multiple functions of the toolbox using three cases: (i) how it can be used to calculate the mixture toxicity, (ii) those for which safety data sheet (SDS) only indicating representative toxicity values (EC50; and LC50), and (iii) those comprising chemicals with low toxic effects.
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8
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Daston GP, Mahony C, Thomas RS, Vinken M. Assessing Safety without Animal Testing: The Road Ahead. Toxicol Sci 2022; 187:214-218. [PMID: 35357465 DOI: 10.1093/toxsci/kfac039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
| | | | - Russell S Thomas
- US Environmental Protection Agency, Research Triangle Park, NC, USA
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9
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Holmgren SD, Boyles RR, Cronk RD, Duncan CG, Kwok RK, Lunn RM, Osborn KC, Thessen AE, Schmitt CP. Catalyzing Knowledge-Driven Discovery in Environmental Health Sciences through a Community-Driven Harmonized Language. Int J Environ Res Public Health 2021; 18:8985. [PMID: 34501574 PMCID: PMC8430534 DOI: 10.3390/ijerph18178985] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/13/2021] [Accepted: 08/19/2021] [Indexed: 01/10/2023]
Abstract
Harmonized language is critical for helping researchers to find data, collecting scientific data to facilitate comparison, and performing pooled and meta-analyses. Using standard terms to link data to knowledge systems facilitates knowledge-driven analysis, allows for the use of biomedical knowledge bases for scientific interpretation and hypothesis generation, and increasingly supports artificial intelligence (AI) and machine learning. Due to the breadth of environmental health sciences (EHS) research and the continuous evolution in scientific methods, the gaps in standard terminologies, vocabularies, ontologies, and related tools hamper the capabilities to address large-scale, complex EHS research questions that require the integration of disparate data and knowledge sources. The results of prior workshops to advance a harmonized environmental health language demonstrate that future efforts should be sustained and grounded in scientific need. We describe a community initiative whose mission was to advance integrative environmental health sciences research via the development and adoption of a harmonized language. The products, outcomes, and recommendations developed and endorsed by this community are expected to enhance data collection and management efforts for NIEHS and the EHS community, making data more findable and interoperable. This initiative will provide a community of practice space to exchange information and expertise, be a coordination hub for identifying and prioritizing activities, and a collaboration platform for the development and adoption of semantic solutions. We encourage anyone interested in advancing this mission to engage in this community.
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Affiliation(s)
- Stephanie D. Holmgren
- Office of Data Science, National Institute of Environmental Health Sciences (NIEHS), Durham, NC 27709, USA;
| | | | | | - Christopher G. Duncan
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, NIEHS, Durham, NC 27709, USA;
| | - Richard K. Kwok
- Epidemiology Branch, Division of Intramural Research, NIEHS, Durham, NC 27709, USA;
- Office of the Director, NIEHS, Bethesda, MD 20892, USA
| | - Ruth M. Lunn
- Integrative Health Assessment Branch, Division of the National Toxicology Program, NIEHS, Durham, NC 27709, USA;
| | | | - Anne E. Thessen
- Environmental and Molecular Toxicology Department, Oregon State University, Corvallis, OR 97331, USA;
| | - Charles P. Schmitt
- Office of Data Science, National Institute of Environmental Health Sciences (NIEHS), Durham, NC 27709, USA;
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10
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Đukić-Ćosić D, Baralić K, Jorgovanović D, Živančević K, Javorac D, Stojilković N, Radović B, Marić Đ, Ćurčić M, Buha-Đorđević A, Bulat Z, Antonijević-Miljaković E, Antonijević B. 'In silico' toxicology methods in drug safety assessment. Arhiv za farmaciju 2021. [DOI: 10.5937/arhfarm71-32966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
While experimental animal investigation has historically been the most conventional approach conducted to assess drug safety and is currently considered the main method for determining drug toxicity, these studies are constricted by cost, time, and ethical approvals. Over the last 20 years, there have been significant advances in computational sciences and computer data processing, while knowledge of alternative techniques and their application has developed into a valuable skill in toxicology. Thus, the application of in silico methods in drug safety assessment is constantly increasing. They are very complex and are grounded on accumulated knowledge from toxicology, bioinformatics, biochemistry, statistics, mathematics, as well as molecular biology. This review will summarize current state-of-the-art scientific data on the use of in silico methods in toxicity testing, taking into account their shortcomings, and highlighting the strategies that should deliver consistent results, while covering the applications of in silico methods in preclinical trials and drug impurities toxicity testing.
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Abstract
A variety of environmental toxicants such as heavy metals, pesticides, organic
chemicals, etc produce harmful effects in our living systems. In the literature, various reports have
indicated the detrimental effects of toxicants such as immunotoxicity, cardiotoxicity,
nephrotoxicity, etc. Experimental animals are generally used to investigate the safety profile of
environmental chemicals, but research on animals has some limitations. Thus, there is a need for
alternative approaches. Docking study is one of the alternate techniques which predict the binding
affinity of molecules in the active site of a particular receptor without using animals. These
techniques can also be used to check the interactions of environmental toxicants towards biological
targets. Varieties of user-friendly software are available in the market for molecular docking, but
very few toxicologists use these techniques in the field of toxicology. To increase the use of these
techniques in the field of toxicology, understanding of basic concepts of these techniques is
required among toxicological scientists. This article has summarized the fundamental concepts of
docking in the context of its role in toxicology. Furthermore, these promising techniques are also
discussed in this study.
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Affiliation(s)
- Meenakshi Gupta
- Department of Pharmacology, Indo-Soviet Friendship Pharmacy College (ISFCP), Moga, Punjab, India
| | - Ruchika Sharma
- Department of Biotechnology, Indo-Soviet Friendship College of Professional Studies (ISFCPS), Moga, Punjab, India
| | - Anoop Kumar
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER)-Raebareli, Lucknow (UP), India
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Patel CN, Kumar SP, Rawal RM, Thaker MB, Pandya HA. Development of cardiotoxicity model using ligand-centric and receptor-centric descriptors. Toxicology Research and Application 2020. [DOI: 10.1177/2397847320971259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Background: Bioinformatics and statistical analysis have been employed to develop a classification model to distinguish toxic and non-toxic molecules. Aims: The primary objective of this study is to enumerate the cut-off values of various physico-chemical (ligand-centric) and target interaction (receptor-centric) descriptors which forms the basis for classifying cardiotoxic and non-toxic molecules. We also sought correlation of molecular docking, absorption, distribution, metabolism, excretion, and toxicology (ADMET) parameters, Lipinski rules, physico-chemical parameters, etc. of human cardiotoxicity drugs. Methods: A training and test set of 91 compounds were applied to linear discriminant analysis (LDA) using 2D and 3D descriptors as discriminating variables representing various molecular modeling parameters to identify which function of descriptor type is responsible for cardiotoxicity. Internal validation was performed using the leave-one-out cross-validation methodology ensuing in good results, assuring the stability of the discriminant function (DF). Results: The values of the statistical parameters Fisher Discriminant Analysis (FDA) and Wilk’s λ for the DF showed reliable statistical significance, as long as the success rate in the prediction for both the training and the test set attained more than 93% accuracy, 87.50% sensitivity and 94.74% specificity. Conclusion: The predictive model was built using a hybrid approach using organ-specific targets for docking and ADMET properties for the FDA (Food and Drug Administration) approved and withdrawn drugs. Classifiers were developed by linear discriminant analysis and the cut-off was enumerated by receiver operating characteristic curve (ROC) analysis to achieve reliable specificity and sensitivity.
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Affiliation(s)
- Chirag N Patel
- Department of Botany, Bioinformatics and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, Gujarat, India
| | - Sivakumar Prasanth Kumar
- Department of Botany, Bioinformatics and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, Gujarat, India
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | - Rakesh M Rawal
- Department of Life Sciences, University School of Sciences, Gujarat University, Ahmedabad, Gujarat, India
| | - Manishkumar B Thaker
- Department of Statistics, M.G. Science Institute, Gujarat University, Ahmedabad, Gujarat, India
| | - Himanshu A Pandya
- Department of Botany, Bioinformatics and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, Gujarat, India
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Jahani R, Mojab F, Mahboubi A, Nasiri A, Tahamtani A, Faizi M. An In-Vivo Study on Anticonvulsant, Anxiolytic, and Sedative-Hypnotic Effects of the Polyphenol-Rich Thymus Kotschyanus Extract; Evidence for the Involvement of GABA A Receptors. Iran J Pharm Res 2020; 18:1456-1465. [PMID: 32641954 PMCID: PMC6934950 DOI: 10.22037/ijpr.2019.15579.13194] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Antidepressant-like activity of T. kotschyanus has been recently reported by scientists but insufficient attention has been so far devoted to T. kotschyanus, and there is a lack of information on the other neurobehavioral effects and side effects of this species. In the current study, the anticonvulsant, anxiolytic, and sedative-hypnotic, effects of Thymus kotschyanus extract on male NMRI mice were evaluated using pentylenetetrazole, maximal electroshock, elevated plus maze, and pentobarbital-induced sleeping tests. Since phenolic compounds and flavonoids have main roles in pharmacological effects of most plant extracts, the phenolic and flavonoid contents of the extract were measured with Folin-Ciocalteu and AlCl3 reagents. Acute toxicity, passive avoidance, and open field tests were carried out to assess the toxicity of the extract. To find out the possible mechanism of action, flumazenil as the specific GABAA receptor antagonist was used. Anticonvulsant and hypnotic effects of the extract were observed at 400 and 600 mg/kg. The extract at the dose of 200 mg/kg revealed significant anxiolytic effects, but it did not show any adverse effects on learning and memory at all the tested doses. Results of this study indicate that Thymus kotschyanus extract has anticonvulsant, anxiolytic, and hypnotic effects, which are likely related to the ability of some phenolic compounds to activate α1-containing GABAA receptors but more experiments still need to be carried out in order to find the exact mechanism, active component, and the toxicity of the Thymus kotschyanus extract.
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Affiliation(s)
- Reza Jahani
- Student Research Committee, Department of Pharmacology and Toxicology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Faraz Mojab
- Department of Pharmacognosy, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Arash Mahboubi
- Food Safty Research Center Department of Pharmaceutics, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azadeh Nasiri
- Department of Pharmacology and Toxicology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Armin Tahamtani
- Department of Pharmacology and Toxicology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrdad Faizi
- Department of Pharmacology and Toxicology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Pharmaceutical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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14
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Morger A, Mathea M, Achenbach JH, Wolf A, Buesen R, Schleifer KJ, Landsiedel R, Volkamer A. KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development. J Cheminform 2020; 12:24. [PMID: 33431007 PMCID: PMC7157991 DOI: 10.1186/s13321-020-00422-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 03/09/2020] [Indexed: 02/07/2023] Open
Abstract
Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of predictions for new chemicals are of utmost importance. Therefore, first, the conformal prediction technique was deployed, comprising an additional calibration step and per definition creating internally valid predictors at a given significance level. Second, to further improve validity and information efficiency, two adaptations are suggested, exemplified at the androgen receptor antagonism endpoint. An absolute increase in validity of 23% on the in-house dataset of 534 compounds could be achieved by introducing KNNRegressor normalisation. This increase in validity comes at the cost of efficiency, which could again be improved by 20% for the initial ToxCast model by balancing the dataset during model training. Finally, the value of the developed pipeline for risk assessment is discussed using two in-house triazole molecules. Compared to a single toxicity prediction method, complementing the outputs of different approaches can have a higher impact on guiding toxicity testing and de-selecting most likely harmful development-candidate compounds early in the development process.
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Affiliation(s)
- Andrea Morger
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany
| | | | | | | | | | | | | | - Andrea Volkamer
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany.
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15
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Karri K, Waxman DJ. Widespread Dysregulation of Long Noncoding Genes Associated With Fatty Acid Metabolism, Cell Division, and Immune Response Gene Networks in Xenobiotic-exposed Rat Liver. Toxicol Sci 2020; 174:291-310. [PMID: 31926019 PMCID: PMC7098378 DOI: 10.1093/toxsci/kfaa001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Xenobiotic exposure dysregulates hundreds of protein-coding genes in mammalian liver, impacting many physiological processes and inducing diverse toxicological responses. Little is known about xenobiotic effects on long noncoding RNAs (lncRNAs), many of which have important regulatory functions. Here, we present a computational framework to discover liver-expressed, xenobiotic-responsive lncRNAs (xeno-lncs) with strong functional, gene regulatory potential and elucidate the impact of xenobiotic exposure on their gene regulatory networks. We assembled the long noncoding transcriptome of xenobiotic-exposed rat liver using RNA-seq datasets from male rats treated with 27 individual chemicals, representing 7 mechanisms of action (MOAs). Ortholog analysis was combined with coexpression data and causal inference methods to infer lncRNA function and deduce gene regulatory networks, including causal effects of lncRNAs on protein-coding gene expression and biological pathways. We discovered > 1400 liver-expressed xeno-lncs, many with human and/or mouse orthologs. Xenobiotics representing different MOAs often regulated common xeno-lnc targets: 123 xeno-lncs were dysregulated by ≥ 10 chemicals, and 5 xeno-lncs responded to ≥ 20 of the 27 chemicals investigated; 81 other xeno-lncs served as MOA-selective markers of xenobiotic exposure. Xeno-lnc-protein-coding gene coexpression regulatory network analysis identified xeno-lncs closely associated with exposure-induced perturbations of hepatic fatty acid metabolism, cell division, or immune response pathways, and with apoptosis or cirrhosis. We also identified hub and bottleneck lncRNAs, which are expected to be key regulators of gene expression. This work elucidates extensive networks of xeno-lnc-protein-coding gene interactions and provides a framework for understanding the widespread transcriptome-altering actions of foreign chemicals in a key-responsive mammalian tissue.
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Affiliation(s)
- Kritika Karri
- Department of Biology and Bioinformatics Program, Boston University, Boston, Massachusetts
| | - David J Waxman
- Department of Biology and Bioinformatics Program, Boston University, Boston, Massachusetts
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16
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Patel CN, Kumar SP, Rawal RM, Patel DP, Gonzalez FJ, Pandya HA. A multiparametric organ toxicity predictor for drug discovery. Toxicol Mech Methods 2020; 30:159-166. [PMID: 31618094 PMCID: PMC7383222 DOI: 10.1080/15376516.2019.1681044] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 10/06/2019] [Accepted: 10/12/2019] [Indexed: 12/31/2022]
Abstract
The assessment of major organ toxicities through in silico predictive models plays a crucial role in drug discovery. Computational tools can predict chemical toxicities using the knowledge gained from experimental studies which drastically reduces the attrition rate of compounds during drug discovery and developmental stages. The purpose of in silico predictions for drug leads and anticipating toxicological endpoints of absorption, distribution, metabolism, excretion and toxicity, clinical adverse impacts and metabolism of pharmaceutically active substances has gained widespread acceptance in academia and pharmaceutical industries. With unrestricted accessibility to powerful biomarkers, researchers have an opportunity to contemplate the most accurate predictive scores to evaluate drug's adverse impact on various organs.A multiparametric model involving physico-chemical properties, quantitative structure-activity relationship predictions and docking score was found to be a more reliable predictor for estimating chemical toxicities with potential to reflect atomic-level insights. These in silico models provide informed decisions to carry out in vitro and in vivo studies and subsequently confirms the molecules clues deciphering the cytotoxicity, pharmacokinetics, and pharmacodynamics and organ toxicity properties of compounds. Even though the drugs withdrawn by USFDA at later phases of drug discovery which should have passed all the state-of-the-art experimental approaches and currently acceptable toxicity filters, there is a dire need to interconnect all these molecular key properties to enhance our knowledge and guide in the identification of leads to drug optimization phases. Current computational tools can predict ADMET and organ toxicities based on pharmacophore fingerprint, toxicophores and advanced machine-learning techniques.
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Affiliation(s)
- Chirag N. Patel
- Department of Botany, Bioinformatics and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Sivakumar Prasanth Kumar
- Division of Biological Sciences, Molecular Biophysics Unit, Indian Institute of Science (IISc), Bangalore, India
| | - Rakesh M. Rawal
- Department of Life Sciences, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Daxesh P. Patel
- Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Frank J. Gonzalez
- Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Himanshu A. Pandya
- Department of Botany, Bioinformatics and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
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17
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Affiliation(s)
- Guilherme J M Garcia
- Department of Biomedical Engineering, Marquette University & The Medical College of Wisconsin, Milwaukee, Wisconsin.,Department of Otolaryngology and Communication Sciences, Medical College of Wisconsin, Milwaukee, Wisconsin
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18
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Karthikeyan BS, Ravichandran J, Mohanraj K, Vivek-Ananth RP, Samal A. A curated knowledgebase on endocrine disrupting chemicals and their biological systems-level perturbations. Sci Total Environ 2019; 692:281-296. [PMID: 31349169 DOI: 10.1016/j.scitotenv.2019.07.225] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/28/2019] [Accepted: 07/14/2019] [Indexed: 05/22/2023]
Abstract
Human well-being can be affected by exposure to several chemicals in the environment. One such group is endocrine disrupting chemicals (EDCs) that can perturb the hormonal homeostasis leading to adverse health effects. In this work, we have developed a detailed workflow to identify EDCs with supporting evidence of endocrine disruption in published experiments in humans or rodents. Thereafter, this workflow was used to manually evaluate more than 16,000 published research articles and identify 686 potential EDCs with published evidence in humans or rodents. Importantly, we have compiled the observed adverse effects or endocrine-specific perturbations along with the dosage information for the potential EDCs from their supporting published experiments. Subsequently, the potential EDCs were classified based on the type of supporting evidence, their environmental source and their chemical properties. Additional compiled information for potential EDCs include their chemical structure, physicochemical properties, predicted ADMET properties and target genes. In order to enable future research based on this compiled information on potential EDCs, we have built an online knowledgebase, Database of Endocrine Disrupting Chemicals and their Toxicity profiles (DEDuCT), accessible at: https://cb.imsc.res.in/deduct/. After building this comprehensive resource, we have performed a network-centric analysis of the chemical space and the associated biological space of target genes of EDCs. Specifically, we have constructed two networks of EDCs using our resource based on similarity of chemical structures or target genes. Ensuing analysis revealed a lack of correlation between chemical structure and target genes of EDCs. Though our detailed results highlight potential challenges in developing predictive models for EDCs, the compiled information in our resource will undoubtedly enable future research in the field, especially, those focussed towards mechanistic understanding of the systems-level perturbations caused by EDCs.
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Affiliation(s)
| | - Janani Ravichandran
- The Institute of Mathematical Sciences (IMSc), Homi Bhabha National Institute (HBNI), Chennai 600113, India.
| | - Karthikeyan Mohanraj
- The Institute of Mathematical Sciences (IMSc), Homi Bhabha National Institute (HBNI), Chennai 600113, India
| | - R P Vivek-Ananth
- The Institute of Mathematical Sciences (IMSc), Homi Bhabha National Institute (HBNI), Chennai 600113, India
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Homi Bhabha National Institute (HBNI), Chennai 600113, India.
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19
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Noyes PD, Friedman KP, Browne P, Haselman JT, Gilbert ME, Hornung MW, Barone S, Crofton KM, Laws SC, Stoker TE, Simmons SO, Tietge JE, Degitz SJ. Evaluating Chemicals for Thyroid Disruption: Opportunities and Challenges with in Vitro Testing and Adverse Outcome Pathway Approaches. Environ Health Perspect 2019; 127:95001. [PMID: 31487205 PMCID: PMC6791490 DOI: 10.1289/ehp5297] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 07/01/2019] [Accepted: 08/13/2019] [Indexed: 05/19/2023]
Abstract
BACKGROUND Extensive clinical and experimental research documents the potential for chemical disruption of thyroid hormone (TH) signaling through multiple molecular targets. Perturbation of TH signaling can lead to abnormal brain development, cognitive impairments, and other adverse outcomes in humans and wildlife. To increase chemical safety screening efficiency and reduce vertebrate animal testing, in vitro assays that identify chemical interactions with molecular targets of the thyroid system have been developed and implemented. OBJECTIVES We present an adverse outcome pathway (AOP) network to link data derived from in vitro assays that measure chemical interactions with thyroid molecular targets to downstream events and adverse outcomes traditionally derived from in vivo testing. We examine the role of new in vitro technologies, in the context of the AOP network, in facilitating consideration of several important regulatory and biological challenges in characterizing chemicals that exert effects through a thyroid mechanism. DISCUSSION There is a substantial body of knowledge describing chemical effects on molecular and physiological regulation of TH signaling and associated adverse outcomes. Until recently, few alternative nonanimal assays were available to interrogate chemical effects on TH signaling. With the development of these new tools, screening large libraries of chemicals for interactions with molecular targets of the thyroid is now possible. Measuring early chemical interactions with targets in the thyroid pathway provides a means of linking adverse outcomes, which may be influenced by many biological processes, to a thyroid mechanism. However, the use of in vitro assays beyond chemical screening is complicated by continuing limits in our knowledge of TH signaling in important life stages and tissues, such as during fetal brain development. Nonetheless, the thyroid AOP network provides an ideal tool for defining causal linkages of a chemical exerting thyroid-dependent effects and identifying research needs to quantify these effects in support of regulatory decision making. https://doi.org/10.1289/EHP5297.
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Affiliation(s)
- Pamela D Noyes
- National Center for Environmental Assessment, Office of Research and Development (ORD), U.S. Environmental Protection Agency (EPA), Washington, DC, USA
| | - Katie Paul Friedman
- National Center for Computational Toxicology, ORD, U.S. EPA, Research Triangle Park, North Carolina, USA
| | - Patience Browne
- Environment Health and Safety Division, Environment Directorate, Organisation for Economic Co-operation and Development (OECD), Paris, France
| | - Jonathan T Haselman
- Mid-Continent Ecology Division, National Health and Environmental Effects Research Laboratory (NHEERL), ORD, U.S. EPA, Duluth, Minnesota, USA
| | - Mary E Gilbert
- Toxicity Assessment Division, NHEERL, ORD, U.S. EPA, Research Triangle Park, North Carolina, USA
| | - Michael W Hornung
- Mid-Continent Ecology Division, National Health and Environmental Effects Research Laboratory (NHEERL), ORD, U.S. EPA, Duluth, Minnesota, USA
| | - Stan Barone
- Office of Pollution Prevention and Toxics, Office of Chemical Safety and Pollution Prevention, U.S. EPA, Washington, DC, USA
| | - Kevin M Crofton
- National Center for Computational Toxicology, ORD, U.S. EPA, Research Triangle Park, North Carolina, USA
| | - Susan C Laws
- Toxicity Assessment Division, NHEERL, ORD, U.S. EPA, Research Triangle Park, North Carolina, USA
| | - Tammy E Stoker
- Toxicity Assessment Division, NHEERL, ORD, U.S. EPA, Research Triangle Park, North Carolina, USA
| | - Steven O Simmons
- National Center for Computational Toxicology, ORD, U.S. EPA, Research Triangle Park, North Carolina, USA
| | - Joseph E Tietge
- Mid-Continent Ecology Division, National Health and Environmental Effects Research Laboratory (NHEERL), ORD, U.S. EPA, Duluth, Minnesota, USA
| | - Sigmund J Degitz
- Mid-Continent Ecology Division, National Health and Environmental Effects Research Laboratory (NHEERL), ORD, U.S. EPA, Duluth, Minnesota, USA
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20
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Sakkiah S, Kusko R, Tong W, Hong H. Applications of Molecular Dynamics Simulations in Computational Toxicology. In: Hong H, editor. Advances in Computational Toxicology. Cham: Springer International Publishing; 2019. pp. 181-212. [DOI: 10.1007/978-3-030-16443-0_10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
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21
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Tang W, Chen J, Wang Z, Xie H, Hong H. Deep learning for predicting toxicity of chemicals: a mini review. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 2019; 36:252-271. [PMID: 30821199 DOI: 10.1080/10590501.2018.1537563] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Humans and wildlife inhabit a world with panoply of natural and synthetic chemicals. Alarmingly, only a limited number of chemicals have undergone comprehensive toxicological evaluation due to limitations of traditional toxicity testing. High-throughput screening assays provide a higher-speed alternative for conventional toxicity testing. Advancement of high-throughput bioassay technology has greatly increased chemical toxicity data volumes in the past decade, pushing toxicology research into a "big data" era. However, traditional data analysis methods fail to effectively process large data volumes, presenting both a challenge and an opportunity for toxicologists. Deep learning, a machine learning method leveraging deep neural networks (DNNs), is a proven useful tool for building quantitative structure-activity relationship (QSAR) models for toxicity prediction utilizing these new large datasets. In this mini review, a brief technical background on DNNs is provided, and the current state of chemical toxicity prediction models built with DNNs is reviewed. In addition, relevant toxicity data sources are summarized, possible limitations are discussed, and perspectives on DNN utilization in chemical toxicity prediction are given.
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Affiliation(s)
- Weihao Tang
- a Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology , Dalian University of Technology , Dalian , China
| | - Jingwen Chen
- a Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology , Dalian University of Technology , Dalian , China
| | - Zhongyu Wang
- a Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology , Dalian University of Technology , Dalian , China
| | - Hongbin Xie
- a Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology , Dalian University of Technology , Dalian , China
| | - Huixiao Hong
- b National Center for Toxicological Research , U.S. Food and Drug Administration , Jefferson , Arkansas , USA
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22
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Idakwo G, Luttrell J, Chen M, Hong H, Zhou Z, Gong P, Zhang C. A review on machine learning methods for in silico toxicity prediction. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 2019; 36:169-191. [PMID: 30628866 DOI: 10.1080/10590501.2018.1537118] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In silico toxicity prediction plays an important role in the regulatory decision making and selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time, budget, and other resources. Many computational methods have been employed in predicting the toxicity profile of chemicals. This review provides a detailed end-to-end overview of the application of machine learning algorithms to Structure-Activity Relationship (SAR)-based predictive toxicology. From raw data to model validation, the importance of data quality is stressed as it greatly affects the predictive power of derived models. Commonly overlooked challenges such as data imbalance, activity cliff, model evaluation, and definition of applicability domain are highlighted, and plausible solutions for alleviating these challenges are discussed.
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Affiliation(s)
- Gabriel Idakwo
- a School of Computing Sciences and Computer Engineering , University of Southern Mississippi , Hattiesburg , Mississippi , USA
| | - Joseph Luttrell
- a School of Computing Sciences and Computer Engineering , University of Southern Mississippi , Hattiesburg , Mississippi , USA
| | - Minjun Chen
- b Division of Bioinformatics and Biostatistics, National Center for Toxicological Science , US Food and Drug Administration , Jefferson , Arkansas , USA
| | - Huixiao Hong
- b Division of Bioinformatics and Biostatistics, National Center for Toxicological Science , US Food and Drug Administration , Jefferson , Arkansas , USA
| | - Zhaoxian Zhou
- a School of Computing Sciences and Computer Engineering , University of Southern Mississippi , Hattiesburg , Mississippi , USA
| | - Ping Gong
- c Environmental Laboratory , US Army Engineer Research and Development Center , Vicksburg , Mississippi , USA
| | - Chaoyang Zhang
- a School of Computing Sciences and Computer Engineering , University of Southern Mississippi , Hattiesburg , Mississippi , USA
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23
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Gupta M, Sharma R, Kumar A. Docking techniques in pharmacology: How much promising? Comput Biol Chem 2018; 76:210-217. [PMID: 30067954 DOI: 10.1016/j.compbiolchem.2018.06.005] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Revised: 02/21/2018] [Accepted: 06/30/2018] [Indexed: 01/01/2023]
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24
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25
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Choudhuri S, Patton GW, Chanderbhan RF, Mattia A, Klaassen CD. From Classical Toxicology to Tox21: Some Critical Conceptual and Technological Advances in the Molecular Understanding of the Toxic Response Beginning From the Last Quarter of the 20th Century. Toxicol Sci 2018; 161:5-22. [PMID: 28973688 PMCID: PMC5837539 DOI: 10.1093/toxsci/kfx186] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Toxicology has made steady advances over the last 60+ years in understanding the mechanisms of toxicity at an increasingly finer level of cellular organization. Traditionally, toxicological studies have used animal models. However, the general adoption of the principles of 3R (Replace, Reduce, Refine) provided the impetus for the development of in vitro models in toxicity testing. The present commentary is an attempt to briefly discuss the transformation in toxicology that began around 1980. Many genes important in cellular protection and metabolism of toxicants were cloned and characterized in the 80s, and gene expression studies became feasible, too. The development of transgenic and knockout mice provided valuable animal models to investigate the role of specific genes in producing toxic effects of chemicals or protecting the organism from the toxic effects of chemicals. Further developments in toxicology came from the incorporation of the tools of "omics" (genomics, proteomics, metabolomics, interactomics), epigenetics, systems biology, computational biology, and in vitro biology. Collectively, the advances in toxicology made during the last 30-40 years are expected to provide more innovative and efficient approaches to risk assessment. A goal of experimental toxicology going forward is to reduce animal use and yet be able to conduct appropriate risk assessments and make sound regulatory decisions using alternative methods of toxicity testing. In that respect, Tox21 has provided a big picture framework for the future. Currently, regulatory decisions involving drugs, biologics, food additives, and similar compounds still utilize data from animal testing and human clinical trials. In contrast, the prioritization of environmental chemicals for further study can be made using in vitro screening and computational tools.
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Affiliation(s)
- Supratim Choudhuri
- Office of Food Additive Safety, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, Maryland
| | - Geoffrey W Patton
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington
| | - Ronald F Chanderbhan
- Office of Food Additive Safety, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, Maryland
| | - Antonia Mattia
- Office of Food Additive Safety, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, Maryland
| | - Curtis D Klaassen
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington
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Abstract
Various methods of machine learning, supervised and unsupervised, linear and nonlinear, classification and regression, in combination with various types of molecular descriptors, both "handcrafted" and "data-driven," are considered in the context of their use in computational toxicology. The use of multiple linear regression, variants of naïve Bayes classifier, k-nearest neighbors, support vector machine, decision trees, ensemble learning, random forest, several types of neural networks, and deep learning is the focus of attention of this review. The role of fragment descriptors, graph mining, and graph kernels is highlighted. The application of unsupervised methods, such as Kohonen's self-organizing maps and related approaches, which allow for combining predictions with data analysis and visualization, is also considered. The necessity of applying a wide range of machine learning methods in computational toxicology is underlined.
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Affiliation(s)
- Igor I Baskin
- Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow, Russian Federation.
- Butlerov Institute of Chemistry, Kazan Federal University, Kazan, Russian Federation.
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27
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Selvaraj C, Sakkiah S, Tong W, Hong H. Molecular dynamics simulations and applications in computational toxicology and nanotoxicology. Food Chem Toxicol 2017; 112:495-506. [PMID: 28843597 DOI: 10.1016/j.fct.2017.08.028] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 08/08/2017] [Accepted: 08/22/2017] [Indexed: 12/13/2022]
Abstract
Nanotoxicology studies toxicity of nanomaterials and has been widely applied in biomedical researches to explore toxicity of various biological systems. Investigating biological systems through in vivo and in vitro methods is expensive and time taking. Therefore, computational toxicology, a multi-discipline field that utilizes computational power and algorithms to examine toxicology of biological systems, has gained attractions to scientists. Molecular dynamics (MD) simulations of biomolecules such as proteins and DNA are popular for understanding of interactions between biological systems and chemicals in computational toxicology. In this paper, we review MD simulation methods, protocol for running MD simulations and their applications in studies of toxicity and nanotechnology. We also briefly summarize some popular software tools for execution of MD simulations.
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Affiliation(s)
- Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
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28
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Chen G, Peijnenburg W, Xiao Y, Vijver MG. Current Knowledge on the Use of Computational Toxicology in Hazard Assessment of Metallic Engineered Nanomaterials. Int J Mol Sci 2017; 18:ijms18071504. [PMID: 28704975 PMCID: PMC5535994 DOI: 10.3390/ijms18071504] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Revised: 07/07/2017] [Accepted: 07/10/2017] [Indexed: 11/16/2022] Open
Abstract
As listed by the European Chemicals Agency, the three elements in evaluating the hazards of engineered nanomaterials (ENMs) include the integration and evaluation of toxicity data, categorization and labeling of ENMs, and derivation of hazard threshold levels for human health and the environment. Assessing the hazards of ENMs solely based on laboratory tests is time-consuming, resource intensive, and constrained by ethical considerations. The adoption of computational toxicology into this task has recently become a priority. Alternative approaches such as (quantitative) structure-activity relationships ((Q)SAR) and read-across are of significant help in predicting nanotoxicity and filling data gaps, and in classifying the hazards of ENMs to individual species. Thereupon, the species sensitivity distribution (SSD) approach is able to serve the establishment of ENM hazard thresholds sufficiently protecting the ecosystem. This article critically reviews the current knowledge on the development of in silico models in predicting and classifying the hazard of metallic ENMs, and the development of SSDs for metallic ENMs. Further discussion includes the significance of well-curated experimental datasets and the interpretation of toxicity mechanisms of metallic ENMs based on reported models. An outlook is also given on future directions of research in this frontier.
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Affiliation(s)
- Guangchao Chen
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
| | - Willie Peijnenburg
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
- Centre for Safety of Substances and Products, National Institute of Public Health and the Environment (RIVM), Bilthoven, 3720 BA Bilthoven, The Netherlands.
| | - Yinlong Xiao
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
| | - Martina G Vijver
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
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Burgoon LD, Druwe IL, Painter K, Yost EE. Using In Vitro High-Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards. Risk Anal 2017; 37:280-290. [PMID: 27088631 DOI: 10.1111/risa.12613] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Revised: 01/07/2016] [Accepted: 02/10/2016] [Indexed: 05/24/2023]
Abstract
Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values, and risk screening values. We aim to use computational toxicology and quantitative high-throughput screening (qHTS) technologies to fill these data gaps, and begin to prioritize these chemicals for additional assessment. In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene may induce DNA damage and steatosis using qHTS data and two separate adverse outcome pathways (AOPs). We also demonstrate how bootstrap natural spline-based meta-regression can be used to integrate data across multiple assay replicates to generate a concentration-response curve. We used this analysis to calculate an in vitro point of departure of 0.751 μM and risk-specific in vitro concentrations of 0.29 μM and 0.28 μM for 1:1,000 and 1:10,000 risk, respectively, for DNA damage. Based on the available evidence, and considering that only a single HSD17B4 assay is available, we have low overall confidence in the steatosis hazard identification. This case study suggests that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap meta-regression, may allow risk assessors to identify points of departure and risk-specific internal/in vitro concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we will need to estimate external doses for risk screening purposes, such as through margin of exposure methods.
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Affiliation(s)
- Lyle D Burgoon
- U.S. Army Engineer Research and Development Center, Research Triangle Park, NC, USA
| | - Ingrid L Druwe
- Oak Ridge Institute for Science and Education assigned to the U.S. Environmental Protection Agency, National Center for Environmental Assessment, Research Triangle Park, NC, USA
| | - Kyle Painter
- Oak Ridge Institute for Science and Education assigned to the U.S. Environmental Protection Agency, National Center for Environmental Assessment, Research Triangle Park, NC, USA
| | - Erin E Yost
- Oak Ridge Institute for Science and Education assigned to the U.S. Environmental Protection Agency, National Center for Environmental Assessment, Research Triangle Park, NC, USA
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30
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Ingle BL, Veber BC, Nichols JW, Tornero-Velez R. Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability. J Chem Inf Model 2016; 56:2243-2252. [DOI: 10.1021/acs.jcim.6b00291] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Brandall L. Ingle
- U.S.
Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Research Triangle Park, North Carolina 27709, United States
| | - Brandon C. Veber
- U.S.
Environmental Protection Agency, Office of Research and Development, National Health Exposure Effects Research Laboratory, Duluth, Minnesota 55804, United States
- Oak Ridge Institutes for Science and Education, Oak Ridge, Tennessee 37830, United States
| | - John W. Nichols
- U.S.
Environmental Protection Agency, Office of Research and Development, National Health Exposure Effects Research Laboratory, Duluth, Minnesota 55804, United States
| | - Rogelio Tornero-Velez
- U.S.
Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Research Triangle Park, North Carolina 27709, United States
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31
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Zhang S, Chen J, Zhao Q, Xie Q, Wei X. Unveiling self-sensitized photodegradation pathways by DFT calculations: A case of sunscreen p-aminobenzoic acid. Chemosphere 2016; 163:227-233. [PMID: 27529387 DOI: 10.1016/j.chemosphere.2016.08.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2016] [Revised: 07/04/2016] [Accepted: 08/04/2016] [Indexed: 06/06/2023]
Abstract
Self-sensitized photodegradation has been observed for diverse aquatic organic pollutants. However, photodegradation pathways have not been clarified in previous experimental studies. Here, we attempted to probe self-sensitized photodegradation pathways of organic pollutants employing both photolytic experiments and density functional theory calculations. By performing photolytic experiments, we found that singlet state oxygen ((1)O2) play an essential role in photodegradation of a sunscreen p-aminobenzoic acid (PABA). PABA can photogenerate (1)O2 and react fast with (1)O2. We hypothesized that PABA underwent (1)O2 induced self-sensitized photodegradation. By calculating transition states, intermediates and reaction barriers, we found that (1)O2 can oxidize PABA through electrophilic attacks on the benzene ring to abstract one H atom of the amino group following a 1,3-addition mechanism or to induce decarboxylation. Either pathway produces a hydroperoxide. O-O bond cleavage of the hydroperoxides occurring at ground states or the lowest triplet excited states can produce phenoxyl radical precursors of 4-amino-3-hydroxybenzoic acid and 4-aminophenol, which are photodegradation products detected in experiments. Thus, a viable (1)O2 self-sensitized photodegradation mechanism was unveiled for PABA.
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Affiliation(s)
- Siyu Zhang
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Science, Shenyang 110016, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Linggong Road 2, Dalian 116024, China.
| | - Qing Zhao
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Science, Shenyang 110016, China
| | - Qing Xie
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Linggong Road 2, Dalian 116024, China
| | - Xiaoxuan Wei
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Linggong Road 2, Dalian 116024, China
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32
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Csiszar SA, Meyer DE, Dionisio KL, Egeghy P, Isaacs KK, Price PS, Scanlon KA, Tan YM, Thomas K, Vallero D, Bare JC. Conceptual Framework To Extend Life Cycle Assessment Using Near-Field Human Exposure Modeling and High-Throughput Tools for Chemicals. Environ Sci Technol 2016; 50:11922-11934. [PMID: 27668689 PMCID: PMC7388028 DOI: 10.1021/acs.est.6b02277] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Life Cycle Assessment (LCA) is a decision-making tool that accounts for multiple impacts across the life cycle of a product or service. This paper presents a conceptual framework to integrate human health impact assessment with risk screening approaches to extend LCA to include near-field chemical sources (e.g., those originating from consumer products and building materials) that have traditionally been excluded from LCA. A new generation of rapid human exposure modeling and high-throughput toxicity testing is transforming chemical risk prioritization and provides an opportunity for integration of screening-level risk assessment (RA) with LCA. The combined LCA and RA approach considers environmental impacts of products alongside risks to human health, which is consistent with regulatory frameworks addressing RA within a sustainability mindset. A case study is presented to juxtapose LCA and risk screening approaches for a chemical used in a consumer product. The case study demonstrates how these new risk screening tools can be used to inform toxicity impact estimates in LCA and highlights needs for future research. The framework provides a basis for developing tools and methods to support decision making on the use of chemicals in products.
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Affiliation(s)
- Susan A Csiszar
- Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, hosted at U.S. Environmental Protection Agency , Cincinnati, Ohio 45268, United States
| | - David E Meyer
- Office of Research and Development, National Risk Management Research Laboratory, U.S. Environmental Protection Agency , Cincinnati, Ohio 45268, United States
| | - Kathie L Dionisio
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Peter Egeghy
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Kristin K Isaacs
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Paul S Price
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Kelly A Scanlon
- AAAS Science & Technology Policy Fellow hosted by the U.S. Environmental Protection Agency, Office of Air and Radiation, Office of Radiation and Indoor Air, Washington, DC 20460, United States
| | - Yu-Mei Tan
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Kent Thomas
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Daniel Vallero
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Jane C Bare
- Office of Research and Development, National Risk Management Research Laboratory, U.S. Environmental Protection Agency , Cincinnati, Ohio 45268, United States
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Zhang Y, Wong YS, Deng J, Anton C, Gabos S, Zhang W, Huang DY, Jin C. Machine learning algorithms for mode-of-action classification in toxicity assessment. BioData Min 2016; 9:19. [PMID: 27182283 PMCID: PMC4866020 DOI: 10.1186/s13040-016-0098-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Accepted: 04/30/2016] [Indexed: 12/29/2022] Open
Abstract
Background Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probing the mode of action (MOA) of the testing substances. Results In this paper, we present machine learning approaches for MOA assessment. Computational tools based on artificial neural network (ANN) and support vector machine (SVM) are developed to analyze the time-concentration response curves (TCRCs) of human cell lines responding to tested chemicals. The techniques are capable of learning data from given TCRCs with known MOA information and then making MOA classification for the unknown toxicity. A novel data processing step based on wavelet transform is introduced to extract important features from the original TCRC data. From the dose response curves, time interval leading to higher classification success rate can be selected as input to enhance the performance of the machine learning algorithm. This is particularly helpful when handling cases with limited and imbalanced data. The validation of the proposed method is demonstrated by the supervised learning algorithm applied to the exposure data of HepG2 cell line to 63 chemicals with 11 concentrations in each test case. Classification success rate in the range of 85 to 95 % are obtained using SVM for MOA classification with two clusters to cases up to four clusters. Conclusions Wavelet transform is capable of capturing important features of TCRCs for MOA classification. The proposed SVM scheme incorporated with wavelet transform has a great potential for large scale MOA classification and high-through output chemical screening. Electronic supplementary material The online version of this article (doi:10.1186/s13040-016-0098-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yile Zhang
- Department of Mathematical and Statistical Science, University of Alberta, T6G 2G1, Edmonton, Canada
| | - Yau Shu Wong
- Department of Mathematical and Statistical Science, University of Alberta, T6G 2G1, Edmonton, Canada
| | - Jian Deng
- Department of Mathematical and Statistical Science, University of Alberta, T6G 2G1, Edmonton, Canada
| | - Cristina Anton
- Department of Mathematics and Statistics, Grant MacEwan University, T5P 2P7, Edmonton, Canada
| | - Stephan Gabos
- Department of Laboratory Medicine and Pathology, University of Alberta, T6G 2B7, Edmonton, Canada
| | | | - Dorothy Yu Huang
- Alberta Centre for Toxicology, University of Calgary, T2N 4N1, Calgary, Canada
| | - Can Jin
- AACEA Biosciences Inc, San Diego, 92121 USA
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Hu Z, Wahl J, Hamburger M, Vedani A. Molecular mechanisms of endocrine and metabolic disruption: An in silico study on antitrypanosomal natural products and some derivatives. Toxicol Lett 2016; 252:29-41. [PMID: 27091077 DOI: 10.1016/j.toxlet.2016.04.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2016] [Revised: 04/12/2016] [Accepted: 04/14/2016] [Indexed: 11/24/2022]
Abstract
The VirtualToxLab is an in silico technology for estimating the toxic potential - endocrine and metabolic disruption, as well as aspects of carcinogenicity and cardiotoxicity - of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects. The simulations are conducted at the atomic level and explicitly allow for a mechanistic interpretation of the results (in real-time 3D/4D), thereby complying with the Setubal principles put forward in 2002 for computational approaches to toxicology. Moreover, the underlying "ab-initio" protocol is independent from any training data and makes the approach universal with respect to the applicability domain. The VirtualToxLab runs in client-server mode and is freely available to academic and non-profit organizations. As the underlying technology yields a thermodynamic estimate of the binding affinity, the associated ligand-protein complexes have been challenged by molecular-dynamics simulations to probe their kinetic stability. Human African trypanosomiasis is a neglected tropical disease caused by two subspecies of Trypanosoma brucei. The control of this parasitic infection relies on a few chemotherapeutic agents, most of which were discovered decades ago and pose many challenges including adverse side effects, poor efficacy, and the occurrence of drug resistances. Natural products, on the other hand, offer a high potential for the discovery of new drug leads due to their chemical diversity. In this in silico study, we analyze a series of 89 natural products and derivatives displaying anti-trypanosomal activity for their potential to trigger adverse effects. Our results indicate a moderate potential for a number of those compounds to bind to nuclear receptors and thereby ease the development of endocrine disregulation. A few others would seem to inhibit enzymes of the cytochrome P450 family and, hence, sustain drug-drug interactions.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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37
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Chen J, Pan T, Chen S, Zou X. Pattern recognition for cytotoxicity mode of action (MOA) of chemicals by using a high-throughput real-time cell analyzer. RSC Adv 2016. [DOI: 10.1039/c6ra18515k] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Slope and entropy are extracted from the relative normalized cell index collected from RTCA. Then the median value is selected to denote the main mode of actin (MoA) of chemical. Hierarchical cluster is used for pattern recognition of MoA.
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Affiliation(s)
- Jiao Chen
- School of Electrical & Information Engineering
- Jiangsu University
- Zhenjiang
- China
- School of Electronics and Electrical Engineering
| | - Tianhong Pan
- School of Electrical & Information Engineering
- Jiangsu University
- Zhenjiang
- China
| | - Shan Chen
- School of Electrical & Information Engineering
- Jiangsu University
- Zhenjiang
- China
| | - Xiaobo Zou
- School of Food and Biological Engineering
- Jiangsu University
- Zhenjiang
- China
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38
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Wallace MAG, Kormos TM, Pleil JD. Blood-borne biomarkers and bioindicators for linking exposure to health effects in environmental health science. J Toxicol Environ Health B Crit Rev 2016; 19:380-409. [PMID: 27759495 PMCID: PMC6147038 DOI: 10.1080/10937404.2016.1215772] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Environmental health science aims to link environmental pollution sources to adverse health outcomes to develop effective exposure intervention strategies that reduce long-term disease risks. Over the past few decades, the public health community recognized that health risk is driven by interaction between the human genome and external environment. Now that the human genetic code has been sequenced, establishing this "G × E" (gene-environment) interaction requires a similar effort to decode the human exposome, which is the accumulation of an individual's environmental exposures and metabolic responses throughout the person's lifetime. The exposome is composed of endogenous and exogenous chemicals, many of which are measurable as biomarkers in blood, breath, and urine. Exposure to pollutants is assessed by analyzing biofluids for the pollutant itself or its metabolic products. New methods are being developed to use a subset of biomarkers, termed bioindicators, to demonstrate biological changes indicative of future adverse health effects. Typically, environmental biomarkers are assessed using noninvasive (excreted) media, such as breath and urine. Blood is often avoided for biomonitoring due to practical reasons such as medical personnel, infectious waste, or clinical setting, despite the fact that blood represents the central compartment that interacts with every living cell and is the most relevant biofluid for certain applications and analyses. The aims of this study were to (1) review the current use of blood samples in environmental health research, (2) briefly contrast blood with other biological media, and (3) propose additional applications for blood analysis in human exposure research.
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Affiliation(s)
- M Ariel Geer Wallace
- a Exposure Methods and Measurement Division, National Exposure Research Laboratory, Office of Research and Development , U.S. Environmental Protection Agency , Research Triangle Park , North Carolina , USA
| | | | - Joachim D Pleil
- a Exposure Methods and Measurement Division, National Exposure Research Laboratory, Office of Research and Development , U.S. Environmental Protection Agency , Research Triangle Park , North Carolina , USA
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Chen B, Zhang T, Bond T, Gan Y. Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources. J Hazard Mater 2015; 299:260-79. [PMID: 26142156 DOI: 10.1016/j.jhazmat.2015.06.054] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 06/17/2015] [Accepted: 06/21/2015] [Indexed: 05/19/2023]
Abstract
Quantitative structure-activity relationship (QSAR) models are tools for linking chemical activities with molecular structures and compositions. Due to the concern about the proliferating number of disinfection byproducts (DBPs) in water and the associated financial and technical burden, researchers have recently begun to develop QSAR models to investigate the toxicity, formation, property, and removal of DBPs. However, there are no standard procedures or best practices regarding how to develop QSAR models, which potentially limit their wide acceptance. In order to facilitate more frequent use of QSAR models in future DBP research, this article reviews the processes required for QSAR model development, summarizes recent trends in QSAR-DBP studies, and shares some important resources for QSAR development (e.g., free databases and QSAR programs). The paper follows the four steps of QSAR model development, i.e., data collection, descriptor filtration, algorithm selection, and model validation; and finishes by highlighting several research needs. Because QSAR models may have an important role in progressing our understanding of DBP issues, it is hoped that this paper will encourage their future use for this application.
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Affiliation(s)
- Baiyang Chen
- Harbin Institute of Technology Shenzhen Graduate School, Shenzhen Key Laboratory of Water Resource Utilization and Environmental Pollution Control, Shenzhen 518055, China.
| | - Tian Zhang
- Harbin Institute of Technology Shenzhen Graduate School, Shenzhen Key Laboratory of Water Resource Utilization and Environmental Pollution Control, Shenzhen 518055, China
| | - Tom Bond
- Department of Civil and Environmental Engineering, Imperial College, London SW7 2AZ, United Kingdom
| | - Yiqun Gan
- Harbin Institute of Technology Shenzhen Graduate School, Shenzhen Key Laboratory of Water Resource Utilization and Environmental Pollution Control, Shenzhen 518055, China
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Quantin P, Thélu A, Catoire S, Ficheux H. Perspectives and strategies of alternative methods used in the risk assessment of personal care products. Annales Pharmaceutiques Françaises 2015; 73:422-35. [DOI: 10.1016/j.pharma.2015.06.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Revised: 06/02/2015] [Accepted: 06/11/2015] [Indexed: 10/23/2022]
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Métivier JP, Lepailleur A, Buzmakov A, Poezevara G, Crémilleux B, Kuznetsov SO, Goff JL, Napoli A, Bureau R, Cuissart B. Discovering Structural Alerts for Mutagenicity Using Stable Emerging Molecular Patterns. J Chem Inf Model 2015; 55:925-40. [DOI: 10.1021/ci500611v] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
| | - Alban Lepailleur
- Normandie Université, Caen, France
- UNICAEN, CERMN,
UPRES EA 4258, FR CNRS 3038, F-14032 Caen, France
| | - Aleksey Buzmakov
- Laboratoire
Lorrain de Recherche en Informatique et ses Applications (LORIA), University of Lorraine, Nancy, France
- National Research University Higher School of Economics (HSE), Moscow, Russia
| | - Guillaume Poezevara
- Normandie Université, Caen, France
- UNICAEN, GREYC,
UMR CNRS 6072, F-14032 Caen, France
- UNICAEN, CERMN,
UPRES EA 4258, FR CNRS 3038, F-14032 Caen, France
| | - Bruno Crémilleux
- Normandie Université, Caen, France
- UNICAEN, GREYC,
UMR CNRS 6072, F-14032 Caen, France
| | - Sergei O. Kuznetsov
- National Research University Higher School of Economics (HSE), Moscow, Russia
| | | | - Amedeo Napoli
- Laboratoire
Lorrain de Recherche en Informatique et ses Applications (LORIA), University of Lorraine, Nancy, France
| | - Ronan Bureau
- Normandie Université, Caen, France
- UNICAEN, CERMN,
UPRES EA 4258, FR CNRS 3038, F-14032 Caen, France
| | - Bertrand Cuissart
- Normandie Université, Caen, France
- UNICAEN, GREYC,
UMR CNRS 6072, F-14032 Caen, France
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Hala D, Petersen LH, Martinović D, Huggett DB. In Silico analysis of perturbed steroidogenesis and gonad growth in fathead minnows (P. promelas) exposed to 17α-ethynylestradiol. Syst Biol Reprod Med 2015; 61:122-38. [PMID: 25910217 DOI: 10.3109/19396368.2015.1035817] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
The multi-factorial nature of adverse reproductive effects mediated by endocrine disrupting compounds (or EDCs) makes understanding the mechanistic basis of reproductive dysfunction a highly pertinent area of research. As a consequence, a main motivator for continued research is to integrate 'multi-leveled' complexity (i.e., from genes to phenotype) using mathematical methods capable of encapsulating properties of physiological relevance. In this study, an in silico stoichiometric model of piscine steroidogenesis was augmented with a 'biomass' reaction associating the underlying stoichiometry of steroidogenesis with a reaction representative of gonad growth. The ability of the in silico model to predict perturbed steroidogenesis and subsequent effects on gonad growth was tested by exposing reproductively active male and female fathead minnows (Pimephales promelas) to 88 ng/L of the synthetic estrogen, 17α-ethynylestradiol (EE2). The in silico model was parameterized (or constrained) with experimentally quantified concentrations of selected steroid hormones (using mass spectrometry) and fold changes in gene expression (using RT-qPCR) for selected steroidogenic enzyme genes, in gonads of male and female fish. Once constrained, the optimization framework of flux balance analysis (FBA) was used to calculate an optimal flux through the biomass reaction (analogous to gonad growth) and associated steroidogenic flux distributions required to generate biomass. FBA successfully predicted effects of EE2 exposure on fathead minnow gonad growth (%gonadosomatic index or %GSI) and perturbed production of steroid hormones. Specifically, FBA accurately predicted no effects of exposure on male %GSI and a significant reduction for female %GSI. Furthermore, in silico simulations accurately identified disrupted reaction fluxes catalyzing productions of androgens (in male fish) and progestogens (in female fish), an observation which agreed with in vivo experimentation. The analyses presented is the first-ever to successfully associate underlying flux properties of the steroidogenic network with gonad growth in fish, an approach which can incorporate in silico predictions with toxicological risk assessments.
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Affiliation(s)
- David Hala
- Department of Biology, University of North Texas , Denton, TX , USA
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Abstract
The understanding of the risk to humans from exposure to pathogens has been firmly put into a risk assessment framework. A key element of applying this approach is the understanding of the relationship between dose and response for particular pathogens. This understanding has progressed from early use of threshold concepts ("minimal infectious dose") thru multiple generations of models. Generation 1 models describe probability of response to exposed dose. Generation 2 models incorporate host factors (e.g., age) and/or pathogen factors (e.g., particle size of inhaled agents). Generation 3 models describe the rate at which effects develop, i.e. the epidemic curve. These (generation 1 through three models) have been developed and used in multiple contexts. Beyond Generation 3 lies an opportunity for the deep incorporation of in vivo physiological responses and the coupling of the individual host dynamics to the dynamics of spread of contagious diseases in the population. This would enable more direct extrapolation from controlled dosing studies to estimate population level effects. There remain also needs to understand broader categories of infectious agents, including pathogenic amoebae and fungi. More advanced models need to be validated against well-characterized human outbreak data.
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Affiliation(s)
- Charles N Haas
- Department of Civil, Architectural & Environmental Engineering Drexel University Philadelphia, Pennsylvania 19104, United States
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Wang X, Chen J, Wang Y, Xie H, Fu Z. Transformation pathways of MeO-PBDEs catalyzed by active center of P450 enzymes: a DFT investigation employing 6-MeO-BDE-47 as a case. Chemosphere 2015; 120:631-636. [PMID: 25462307 DOI: 10.1016/j.chemosphere.2014.09.105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Revised: 09/27/2014] [Accepted: 09/29/2014] [Indexed: 06/04/2023]
Abstract
Recent in vivo and in vitro experiments indicated that methoxylated polybrominated diphenyl ethers (MeO-PBDEs) can be biotransformed into hydroxylated PBDEs (HO-PBDEs) that are more toxic than PBDEs and MeO-PBDEs. Nevertheless, the enzymatic transformation mechanism is not clear. We hypothesized that cytochrome P450 enzymes (CYPs) play a key role in the transformation and employed the density functional theory calculations to unveil the mechanism. The transformation of a model compound, 6-MeO-BDE-47, catalyzed by the active center of CYPs (Compound I), was computed. For the first time, our results show that the energy barriers for the addition of Compound I to the C atoms on the phenyl of 6-MeO-BDE-47 are much higher than that for hydroxylation of the methoxyl, indicating that O-demethylation is a dominating metabolic pathway. This is in line with experimental observations performed by others. The pathways for the transformation of 6-MeO-BDE-47 catalyzed by Compound I were clarified. A C-H bond of the methoxyl is activated by Compound I, followed by radical rebound to form carbinol intermediates, then the carbinols decompose to form 6-HO-BDE-47 with the assistance of water molecules. The computational method can be potentially employed to develop models that predict biotransformation of xenobiotics catalyzed by CYPs.
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Affiliation(s)
- Xingbao Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China; State Key Laboratory of Fine Chemicals, School of Pharmaceutical Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.
| | - Yong Wang
- State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Hongbin Xie
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Zhiqiang Fu
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
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DeBord DG, Burgoon L, Edwards SW, Haber LT, Kanitz MH, Kuempel E, Thomas RS, Yucesoy B. Systems Biology and Biomarkers of Early Effects for Occupational Exposure Limit Setting. J Occup Environ Hyg 2015; 12 Suppl 1:S41-54. [PMID: 26132979 PMCID: PMC4654673 DOI: 10.1080/15459624.2015.1060324] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
In a recent National Research Council document, new strategies for risk assessment were described to enable more accurate and quicker assessments. This report suggested that evaluating individual responses through increased use of bio-monitoring could improve dose-response estimations. Identification of specific biomarkers may be useful for diagnostics or risk prediction as they have the potential to improve exposure assessments. This paper discusses systems biology, biomarkers of effect, and computational toxicology approaches and their relevance to the occupational exposure limit setting process. The systems biology approach evaluates the integration of biological processes and how disruption of these processes by chemicals or other hazards affects disease outcomes. This type of approach could provide information used in delineating the mode of action of the response or toxicity, and may be useful to define the low adverse and no adverse effect levels. Biomarkers of effect are changes measured in biological systems and are considered to be preclinical in nature. Advances in computational methods and experimental -omics methods that allow the simultaneous measurement of families of macromolecules such as DNA, RNA, and proteins in a single analysis have made these systems approaches feasible for broad application. The utility of the information for risk assessments from -omics approaches has shown promise and can provide information on mode of action and dose-response relationships. As these techniques evolve, estimation of internal dose and response biomarkers will be a critical test of these new technologies for application in risk assessment strategies. While proof of concept studies have been conducted that provide evidence of their value, challenges with standardization and harmonization still need to be overcome before these methods are used routinely.
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Affiliation(s)
- D. Gayle DeBord
- National Institute for Occupational Safety and Health, Division of Applied Research and Technology, Cincinnati, Ohio
| | - Lyle Burgoon
- U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, North Carolina
| | - Stephen W. Edwards
- U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, North Carolina
| | - Lynne T. Haber
- Toxicology Excellence for Risk Assessment (TERA), Cincinnati, Ohio
| | - M. Helen Kanitz
- National Institute for Occupational Safety and Health, Division of Applied Research and Technology, Cincinnati, Ohio
| | - Eileen Kuempel
- National Institute for Occupational Safety and Health, Education and Information Division, Cincinnati, Ohio
| | - Russell S. Thomas
- U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, North Carolina
- The Hamner Institute for Health Sciences, Research Triangle Park, North Carolina
| | - Berran Yucesoy
- National Institute for Occupational Safety and Health, Heath Effects Laboratory Division, Morgantown, West Virginia
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Vedani A, Dobler M, Hu Z, Smieško M. OpenVirtualToxLab--a platform for generating and exchanging in silico toxicity data. Toxicol Lett 2014; 232:519-32. [PMID: 25240273 DOI: 10.1016/j.toxlet.2014.09.004] [Citation(s) in RCA: 74] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Accepted: 09/03/2014] [Indexed: 11/30/2022]
Abstract
The VirtualToxLab is an in silico technology for estimating the toxic potential--endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity--of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The toxic potential of a compound--its ability to trigger adverse effects--is derived from its computed binding affinities toward these very proteins: the computationally demanding simulations are executed in client-server model on a Linux cluster of the University of Basel. The graphical-user interface supports all computer platforms, allows building and uploading molecular structures, inspecting and downloading the results and, most important, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. Access to the VirtualToxLab is available free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.
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Affiliation(s)
- Angelo Vedani
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland; Foundation Biographics Laboratory 3R, Klingelbergstrasse 50, 4056 Basel, Switzerland.
| | - Max Dobler
- Foundation Biographics Laboratory 3R, Klingelbergstrasse 50, 4056 Basel, Switzerland
| | - Zhenquan Hu
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
| | - Martin Smieško
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
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Liao M, Zhao Y, Ning P, Cao H, Wen H, Zhang Y. Optimal Design of Solvent Blend and Its Application in Coking Wastewater Treatment Process. Ind Eng Chem Res 2014. [DOI: 10.1021/ie5010898] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Mingsen Liao
- Institute
of Process Engineering, Chinese Academy of Science, Beijing 100190, China
- School
of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- China National Engineering Research Center for Distillation Technology-NERCDT, Tianjin 300072, China
| | - Yuehong Zhao
- Institute
of Process Engineering, Chinese Academy of Science, Beijing 100190, China
| | - Pengge Ning
- Institute
of Process Engineering, Chinese Academy of Science, Beijing 100190, China
| | - Hongbin Cao
- Institute
of Process Engineering, Chinese Academy of Science, Beijing 100190, China
| | - Hao Wen
- Institute
of Process Engineering, Chinese Academy of Science, Beijing 100190, China
| | - Yi Zhang
- Institute
of Process Engineering, Chinese Academy of Science, Beijing 100190, China
- School
of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
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Chen G, Cairelli MJ, Kilicoglu H, Shin D, Rindflesch TC. Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference. PLoS Comput Biol 2014; 10:e1003666. [PMID: 24921649 PMCID: PMC4055569 DOI: 10.1371/journal.pcbi.1003666] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2013] [Accepted: 04/29/2014] [Indexed: 12/28/2022] Open
Abstract
Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.
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Affiliation(s)
- Guocai Chen
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States of America
| | - Michael J. Cairelli
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States of America
| | - Halil Kilicoglu
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States of America
| | - Dongwook Shin
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States of America
| | - Thomas C. Rindflesch
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States of America
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49
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Yang L, Wang J, Wang H, Lv Y, Zuo Y, Jiang W. Analysis and identification of toxin targets by topological properties in protein–protein interaction network. J Theor Biol 2014; 349:82-91. [DOI: 10.1016/j.jtbi.2014.02.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2013] [Revised: 01/20/2014] [Accepted: 02/01/2014] [Indexed: 10/25/2022]
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50
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Kongsbak K, Hadrup N, Audouze K, Vinggaard AM. Applicability of computational systems biology in toxicology. Basic Clin Pharmacol Toxicol 2014; 115:45-9. [PMID: 24528503 DOI: 10.1111/bcpt.12216] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2013] [Accepted: 02/05/2014] [Indexed: 12/31/2022]
Abstract
Systems biology as a research field has emerged within the last few decades. Systems biology, often defined as the antithesis of the reductionist approach, integrates information about individual components of a biological system. In integrative systems biology, large data sets from various sources and databases are used to model and predict effects of chemicals on, for instance, human health. In toxicology, computational systems biology enables identification of important pathways and molecules from large data sets; tasks that can be extremely laborious when performed by a classical literature search. However, computational systems biology offers more advantages than providing a high-throughput literature search; it may form the basis for establishment of hypotheses on potential links between environmental chemicals and human diseases, which would be very difficult to establish experimentally. This is possible due to the existence of comprehensive databases containing information on networks of human protein-protein interactions and protein-disease associations. Experimentally determined targets of the specific chemical of interest can be fed into these networks to obtain additional information that can be used to establish hypotheses on links between the chemical and human diseases. Such information can also be applied for designing more intelligent animal/cell experiments that can test the established hypotheses. Here, we describe how and why to apply an integrative systems biology method in the hypothesis-generating phase of toxicological research.
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Affiliation(s)
- Kristine Kongsbak
- Division of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark, Søborg, Denmark; Department for Systems Biology, Centre for Biological Sequence Analysis, Technical University of Denmark, Kgs. Lyngby, Denmark
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