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Banerjee A, Roy K. Prediction-Inspired Intelligent Training for the Development of Classification Read-across Structure-Activity Relationship (c-RASAR) Models for Organic Skin Sensitizers: Assessment of Classification Error Rate from Novel Similarity Coefficients. Chem Res Toxicol 2023; 36:1518-1531. [PMID: 37584642 DOI: 10.1021/acs.chemrestox.3c00155] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
The advancements in the field of cheminformatics have led to a reduction in animal testing to estimate the activity, property, and toxicity of query chemicals. Read-across structure-activity relationship (RASAR) is an emerging concept that utilizes various similarity functions derived from chemical information to develop highly predictive models. Unlike quantitative structure-activity relationship (QSAR) models, RASAR descriptors of a query compound are computed from its close congeners instead of the compound itself, thus targeting predictions in the model training phase. The objective of the present study is not to propose new QSAR models for skin sensitization but to demonstrate the enhancement in the quality of predictions of the skin-sensitizing potential of organic compounds by developing classification-based RASAR (c-RASAR) models. A diverse, previously curated data set was collected from the literature for which 2D descriptors were computed. The extracted essential features were then used to develop a classification-based linear discriminant analysis (LDA) QSAR model. Furthermore, from the read-across-based predictions, RASAR descriptors were calculated using the basic settings of the hyperparameters for the Laplacian Kernel-based optimum similarity measure. After feature selection, an LDA c-RASAR model was developed, which superseded the prediction quality of the LDA-QSAR model. Various other combinations of RASAR descriptors were also taken to develop additional c-RASAR models, all showing better prediction quality than the LDA QSAR model while using a lower number of descriptors. Various other machine learning c-RASAR models were also developed for comparison purposes. In this work, we have proposed and analyzed three new similarity metrics: gm_class, sm1, and sm2. The first one is an indicator variable used to generate a simple univariate c-RASAR model with good prediction ability, while the remaining two are similarity indices used to analyze possible activity cliffs in the training and test sets and are believed to play an important role in the modelability analysis of data sets.
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Affiliation(s)
- Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India
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Ayipo YO, Ahmad I, Najib YS, Sheu SK, Patel H, Mordi MN. Molecular modelling and structure-activity relationship of a natural derivative of o-hydroxybenzoate as a potent inhibitor of dual NSP3 and NSP12 of SARS-CoV-2: in silico study. J Biomol Struct Dyn 2023; 41:1959-1977. [PMID: 35037841 DOI: 10.1080/07391102.2022.2026818] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
The nsp3 macrodomain and nsp12 (RdRp) enzymes are strongly implicated in the virulent regulation of the host immune response and viral replication of SARS-CoV-2, making them plausible therapeutic targets for mitigating infectivity. Remdesivir remains the only FDA-approved small-molecule inhibitor of the nsp12 in clinical conditions while none has been approved yet for the nsp3 macrodomain. In this study, 69,067 natural compounds from the IBScreen database were screened for efficacious potentials with mechanistic multitarget-directed inhibitory pharmacology against the dual targets using in silico approaches. Standard and extra precision (SP and XP) Maestro glide docking analyses were employed to evaluate their inhibitory interactions against the enzymes. Four compounds, STOCK1N-45901, 03804, 83408, 08377 consistently showed high XP scores against the respective targets and interacted strongly with pharmacologically essential amino acid and RNA residues, in better terms than the standard, co-crystallized inhibitors, GS-441524 and remdesivir. Further assessments through the predictions of ADMET and mutagenicity distinguished STOCK1N-45901, a natural derivative of o-hydroxybenzoate as the most promising candidate. The ligand maintained a good conformational and thermodynamic stability in complex with the enzymes throughout the trajectories of 100 ns molecular dynamics, indicated by RMSD, RMSF and radius of gyration plots. Its binding free energy, MM-GBSA was recorded as -54.24 and -31.77 kcal/mol against the respective enzyme, while its structure-activity relationships confer high probabilities as active antiviral, anti-inflammatory, antiinfection, antitussive and peroxidase inhibitor. The IBScreen database natural product, STOCK1N-45901 (2,3,4,5,6-pentahydroxyhexyl o-hydroxybenzoate) is thus recommended as a potent inhibitor of dual nsp3 and nsp12 of SARS-CoV-2 for further study. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Yusuf Oloruntoyin Ayipo
- Centre for Drug Research, Universiti Sains Malaysia, USM, Pulau Pinang, Malaysia
- Department of Chemistry and Industrial Chemistry, Kwara State University, Malete, Ilorin, Nigeria
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Maharashtra, India
| | - Yahaya Sani Najib
- Centre for Drug Research, Universiti Sains Malaysia, USM, Pulau Pinang, Malaysia
- Department of Pharmaceutical and Medicinal Chemistry, Bayero University, Kano, Nigeria
| | - Sikirat Kehinde Sheu
- Department of Chemistry and Industrial Chemistry, Kwara State University, Malete, Ilorin, Nigeria
| | - Harun Patel
- Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Maharashtra, India
| | - Mohd Nizam Mordi
- Centre for Drug Research, Universiti Sains Malaysia, USM, Pulau Pinang, Malaysia
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Lee SH, Kim J, Kim J, Park J, Park S, Kim KB, Lee BM, Kwon S. Current trends in read-across applications for chemical risk assessments and chemical registrations in the Republic of Korea. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART B, CRITICAL REVIEWS 2022; 25:393-404. [PMID: 36250612 DOI: 10.1080/10937404.2022.2133033] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Read-across, an alternative approach for hazard assessment, has been widely adopted when in vivo data are unavailable for chemicals of interest. Read-across is enabled via in silico tools such as quantitative structure activity relationship (QSAR) modeling. In this study, the current status of structure activity relationship (SAR)-based read-across applications in the Republic of Korea (ROK) was examined considering both chemical risk assessments and chemical registrations from different sectors, including regulatory agencies, industry, and academia. From the regulatory perspective, the Ministry of Environment (MOE) established the Act on Registration and Evaluation of Chemicals (AREC) in 2019 to enable registrants to submit alternative data such as information from read-across instead of in vivo data to support hazard assessment and determine chemical-specific risks. Further, the Ministry of Food and Drug Safety (MFDS) began to consider read-across approaches for establishing acceptable intake (AI) limits of impurities occurring during pharmaceutical manufacturing processes under the ICH M7 guideline. Although read-across has its advantages, this approach also has limitations including (1) lack of standardized criteria for regulatory acceptance, (2) inconsistencies in the robustness of scientific evidence, and (3) deficiencies in the objective reliability of read-across data. The application and acceptance rate of read-across may vary among regulatory agencies. Therefore, sufficient data need to be prepared to verify the hypothesis that structural similarities might lead to similarities in properties of substances (between source and target chemicals) prior to adopting a read-across approach. In some cases, additional tests may be required during the registration process to clarify long-term effects on human health or the environment for certain substances that are data deficient. To improve the quality of read-across data for regulatory acceptance, cooperative efforts from regulatory agencies, academia, and industry are needed to minimize limitations of read-across applications.
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Affiliation(s)
- Sang Hee Lee
- Chemicals Registration & Evaluation Team, Risk Assessment Research Division, National Institute of Environmental Research, Ministry of Environment, Inchon, Republic of Korea
| | - Jongwoon Kim
- Chemical Safety Research Center, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea
| | - Jinyong Kim
- Environment, Safety and Health DepartmentChemical Products and Biocides Safety Center, Korea Environmental Industry and Technology Institute (KEITI), Inchon, Republic of Korea
| | - Jaehyun Park
- Pharmaceutical Standardization Division, Drug Evaluation Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Osong Health Technology Administration Complex, Cheongju, Chungcheongbuk-do, Republic of Korea
| | | | - Kyu-Bong Kim
- College of Pharmacy, Dankook University, Chungnam 31116, Republic of Korea
| | - Byung-Mu Lee
- Division of Toxicology, College of Pharmacy, Sungkyunkwan University, Seobu-ro 2066, Suwon, Republic of Korea
| | - Seok Kwon
- Global Product Stewardship, Research & Development, Singapore Innovation Center, Procter & Gamble (P&G) International Operationsr, Singapore
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Virtual Extensive Read-Across: A New Open-Access Software for Chemical Read-Across and Its Application to the Carcinogenicity Assessment of Botanicals. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27196605. [PMID: 36235142 PMCID: PMC9570968 DOI: 10.3390/molecules27196605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/23/2022] [Accepted: 09/25/2022] [Indexed: 11/16/2022]
Abstract
Read-across applies the principle of similarity to identify the most similar substances to represent a given target substance in data-poor situations. However, differences between the target and the source substances exist. The present study aims to screen and assess the effect of the key components in a molecule which may escape the evaluation for read-across based only on the most similar substance(s) using a new open-access software: Virtual Extensive Read-Across (VERA). VERA provides a means to assess similarity between chemicals using structural alerts specific to the property, pre-defined molecular groups and structural similarity. The software finds the most similar compounds with a certain feature, e.g., structural alerts and molecular groups, and provides clusters of similar substances while comparing these similar substances within different clusters. Carcinogenicity is a complex endpoint with several mechanisms, requiring resource intensive experimental bioassays and a large number of animals; as such, the use of read-across as part of new approach methodologies would support carcinogenicity assessment. To test the VERA software, carcinogenicity was selected as the endpoint of interest for a range of botanicals. VERA correctly labelled 70% of the botanicals, indicating the most similar substances and the main features associated with carcinogenicity.
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Ayipo YO, Alananzeh WA, Ahmad I, Patel H, Mordi MN. Structural modelling and in silico pharmacology of β-carboline alkaloids as potent 5-HT1A receptor antagonists and reuptake inhibitors. J Biomol Struct Dyn 2022:1-17. [PMID: 35881145 DOI: 10.1080/07391102.2022.2104376] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Serotonin (5-HT) antagonists and reuptake inhibitors (SARIs) are atypical antidepressants for managing major depressive disorder. They are oftentimes applied as adjuvants for ameliorating aftereffects of SSRI antidepressants including insomnia and sexual dysfunction. The few available candidates of this class including lorpiprazole and trazodone also display some daunting side effects, making a continuous search for improved alternatives essential. Natural β-carboline alkaloids (NβCs) are interestingly renowned with broad pharmacological spectrum against several neuropsychiatric disorders including depression. However, their potentials as SARIs remain underexplored. In this study, 982 NβCs retrieved from the Ambinter-Greenpharma (Amb) database were virtually screened for potent SARI alternatives using computational and biocheminformatics approaches: homology modelling of 5-HT1A receptor, Glide HTVS, SP and XP molecular docking, molecular dynamics (MD) simulation, ADMET and mutagenicity predictions. The homology receptor was validated as a good representative of human 5HT1A receptor using the RCSB structure validation and quality protocols. From the virtual screening against the 5-HT1A receptor, Amb ligands, Amb18709727 and Amb37857532 showed higher binding affinities by XP scores of -8.725 and -7.976 kcal/mol, and MMGBSA of -87.972 and -107.585 kcal/mol respectively compared to lorpiprazole, a reference SARI with XP score and MMGBSA of -6.512 and -62.788 kcal/mol respectively. They maintained ideal contacts with pharmacologically essential amino acid residues implicated in SARI mechanisms and expressed higher stability and compactness than lorpiprazole throughout the trajectories of 100 ns MD simulation. They also displayed interesting ADME, druggability, low toxicity and mutagenicity profiles, ideal for CNS drug prospects, thus, recommended as putative SARI candidates for further study.
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Affiliation(s)
- Yusuf Oloruntoyin Ayipo
- Centre for Drug Research, Universiti Sains Malaysia, USM, Pulau Pinang, Malaysia.,Department of Chemistry and Industrial Chemistry, Kwara State University, Malete, Ilorin, Nigeria
| | - Waleed A Alananzeh
- Centre for Drug Research, Universiti Sains Malaysia, USM, Pulau Pinang, Malaysia
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Maharashtra, India
| | - Harun Patel
- Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Maharashtra, India
| | - Mohd Nizam Mordi
- Centre for Drug Research, Universiti Sains Malaysia, USM, Pulau Pinang, Malaysia
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Roncaglioni A, Lombardo A, Benfenati E. The VEGAHUB Platform: The Philosophy and the Tools. Altern Lab Anim 2022; 50:121-135. [PMID: 35382564 DOI: 10.1177/02611929221090530] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
VEGAHUB (www.vegahub.eu) is a repository of freely available, downloadable tools based on computational toxicology methodologies. The main software tool available in VEGAHUB is VEGA QSAR software encoding more than 90 quantitative structure-activity relationship (QSAR) models for tens of endpoints for human toxicology, ecotoxicology, environmental, physico-chemical and toxicokinetic properties. However, beyond VEGA QSAR, VEGAHUB offers several other tools. Here, we present these resources, the possibilities to fully exploit them and the ways in which to integrate results provided by different VEGAHUB tools. Read-across and weight-of-evidence represent a major advantage of VEGAHUB. Integration between hazard and exposure is provided within innovative tools, which are specific for well-defined scenarios, such as those for cosmetic products. Prioritisation can be achieved by integrating results from 48 models. Finally, we highlight how some tools may not only fit predefined endpoints but also could be applied to general problems and research applications in the QSAR field. A couple of examples are provided, in which a critical assessment of the predictions and the documentation associated with the prediction are considered, in order to properly assess the quality of the results. These results may be associated with different levels of uncertainty or even be conflicting.
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Affiliation(s)
| | - Anna Lombardo
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, 9361Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.,This article is part of the Virtual Special Collection on In Silico Tools
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, 9361Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
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Manganelli S, Gamba A, Colombo E, Benfenati E. Using VEGAHUB Within a Weight-of-Evidence Strategy. Methods Mol Biol 2022; 2425:479-495. [PMID: 35188643 DOI: 10.1007/978-1-0716-1960-5_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Industrial needs and regulatory requirements have played a significant role in accelerating the use of nontesting methods including in silico tools as alternatives to animal testing. The main interest is not solely on the use of in silico tools, or in read-across, but on better toxicological safety assessment of substances, and for this purpose more advanced, integrated strategies have to be implemented. VEGAHUB wants to promote this broader view, not necessarily focused on a specific approach. Applying multiple tools and complementary approaches instead of one technique may provide more elements for a more robust evaluation, but at the same time it is important to have a conceptual scheme to integrate multiple, heterogeneous lines of evidence. We will show how the user can benefit from the diversity of tools available within the platform VEGAHUB for assessing the biological properties of chemical substances on an example of (non)mutagenicity.
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Affiliation(s)
| | - Alessio Gamba
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Erika Colombo
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
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Gini G. QSAR Methods. Methods Mol Biol 2022; 2425:1-26. [PMID: 35188626 DOI: 10.1007/978-1-0716-1960-5_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This chapter introduces the basis of computational chemistry and discusses how computational methods have been extended from physical to biological properties, and toxicology in particular, modeling. Since about three decades, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Animal and wet experiments, aimed at providing a standardized result about a biological property, can be mimicked by modeling methods, globally called in silico methods, all characterized by deducing properties starting from the chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (quantitative structure-activity relationships), and models that check relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. Virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.
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Gadaleta D, Benfenati E. A descriptor-based analysis to highlight the mechanistic rationale of mutagenicity. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, TOXICOLOGY AND CARCINOGENESIS 2021; 39:269-292. [PMID: 33955817 DOI: 10.1080/26896583.2021.1883964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Cancer is a main concern for human health and there is a need of alternative methodologies to rapidly screen large quantitative of compounds that may represent a toxicological risk. Here a statistical analyses is performed on a benchmark database of experimental Ames data to identify chemical descriptors discriminating mutagens and non-mutagens. A total of 53 activating and deactivating modulators are identified, that flagged respectively a percentage of mutagen and non-mutagen up to 87%. Modulators are further combined to form synergistic cross-terms, accounting for the effect that combined properties may have on the final toxicity. Exclusion rules are defined as exception to the modulators. Synergistic cross-terms and exclusion rules improve the enrichment of mutagens/non-mutagens with respect of the original abundance in the dataset to values higher than 95%. The external predictivity of modulators and cross-terms reach balanced accuracy up to 0.775 that is analogous to other mutagenicity models from the literature, confirming the suitability of the rules to real-life screening of chemicals. Modulators are discussed for their mechanistic link to mutagenicity. This analysis confirms the key role of some properties (polarizability, shape, mass, presence of reactive functional groups or unsaturated planar systems) as driving elements for the initiation of the mutagenicity.
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Affiliation(s)
- Domenico Gadaleta
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
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Charehsaz M, Tugcu G, Aydin A. Filling data gap for nicotinic acid, nicotinate esters and nicotinamide for the determination of permitted daily exposure by a category approach. Toxicol Res 2020; 37:337-344. [PMID: 34295797 DOI: 10.1007/s43188-020-00069-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 09/02/2020] [Accepted: 09/23/2020] [Indexed: 11/28/2022] Open
Abstract
This study aimed to obtain necessary toxicological data using experimental and computational methods for the calculation of a common permitted daily exposure (PDE) which can be relevant for nicotinic acid and its esters and nicotinamide according to European Medicines Agency Guideline on setting health-based exposure limits. PDE calculation is mainly based on critical toxicological endpoints. During this procedure, critical toxicological endpoints data of an active pharmaceutical ingredient (API) may not be able to find satisfactorily. Hence, using toxicological data for another API that has a similar chemical structure can be a useful way. In this study, toxicological endpoints of nicotinic acid and its esters and nicotinamide were evaluated. Then, the data gaps in the toxicological endpoints were filledusing the read-across approach. Based on the current existing data, nicotinic acid and its esters and also nicotinamide are not genotoxic and do not have skin sensitization potential. These compounds do not present a concern for carcinogenicity and developmental/reproductive toxicity. Based on these critical endpoints and available experimental data, the final PDE of 10 mg/day was calculated for all category members. Our study showed the utility of the read-across for PDE calculation of APIs with experimental toxicological data gap.
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Affiliation(s)
- Mohammad Charehsaz
- Department of Toxicology, Faculty of Pharmacy, Yeditepe University, Inonu Mah. Kayisdagi Cad., Atasehir, 34755 Istanbul, Turkey
| | - Gulcin Tugcu
- Department of Toxicology, Faculty of Pharmacy, Yeditepe University, Inonu Mah. Kayisdagi Cad., Atasehir, 34755 Istanbul, Turkey
| | - Ahmet Aydin
- Department of Toxicology, Faculty of Pharmacy, Yeditepe University, Inonu Mah. Kayisdagi Cad., Atasehir, 34755 Istanbul, Turkey
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Antifouling Napyradiomycins from Marine-Derived Actinomycetes Streptomyces aculeolatus. Mar Drugs 2020; 18:md18010063. [PMID: 31963732 PMCID: PMC7024211 DOI: 10.3390/md18010063] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 01/14/2020] [Accepted: 01/16/2020] [Indexed: 01/20/2023] Open
Abstract
The undesired attachment of micro and macroorganisms on water-immersed surfaces, known as marine biofouling, results in severe prevention and maintenance costs (billions €/year) for aquaculture, shipping and other industries that rely on coastal and off-shore infrastructures. To date, there are no sustainable, cost-effective and environmentally safe solutions to address this challenging phenomenon. Therefore, we investigated the antifouling activity of napyradiomycin derivatives that were isolated from actinomycetes from ocean sediments collected off the Madeira Archipelago. Our results revealed that napyradiomycins inhibited ≥80% of the marine biofilm-forming bacteria assayed, as well as the settlement of Mytilus galloprovincialis larvae (EC50 < 5 µg/ml and LC50/EC50 >15), without viability impairment. In silico prediction of toxicity end points are of the same order of magnitude of standard approved drugs and biocides. Altogether, napyradiomycins disclosed bioactivity against marine micro and macrofouling organisms, and non-toxic effects towards the studied species, displaying potential to be used in the development of antifouling products.
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Gini G, Zanoli F. Machine Learning and Deep Learning Methods in Ecotoxicological QSAR Modeling. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2020. [DOI: 10.1007/978-1-0716-0150-1_6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Abstract
Abstract
The prediction of toxicological endpoints has gained broad acceptance; it is widely applied in early stages of drug discovery as well as for impurities obtained in the production of generic or equivalent products. In this work, we describe methodologies for the prediction of toxicological endpoints compounds, with a particular focus on secondary metabolites. Case studies include toxicity prediction of natural compound databases with anti-diabetic, anti-malaria and anti-HIV properties.
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Benfenati E, Chaudhry Q, Gini G, Dorne JL. Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy. ENVIRONMENT INTERNATIONAL 2019; 131:105060. [PMID: 31377600 DOI: 10.1016/j.envint.2019.105060] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Revised: 06/26/2019] [Accepted: 07/25/2019] [Indexed: 06/10/2023]
Abstract
In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence of experimental toxicity data. Many in silico models are available and can be used individually or in an integrated fashion. Whilst such models offer major benefits to toxicologists, risk assessors and the global scientific community, the lack of a consistent framework for the integration of in silico results can lead to uncertainty and even contradictions across models and users, even for the same chemicals. In this context, a range of methods for integrating in silico results have been proposed on a statistical or case-specific basis. Read-across constitutes another strategy for deriving reference points or points of departure for hazard characterisation of untested chemicals, from the available experimental data for structurally-similar compounds, mostly using expert judgment. Recently a number of software systems have been developed to support experts in this task providing a formalised and structured procedure. Such a procedure could also facilitate further integration of the results generated from in silico models and read-across. This article discusses a framework on weight of evidence published by EFSA to identify the stepwise approach for systematic integration of results or values obtained from these "non-testing methods". Key criteria and best practices for selecting and evaluating individual in silico models are also described, together with the means to combining the results, taking into account any limitations, and identifying strategies that are likely to provide consistent results.
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Affiliation(s)
- Emilio Benfenati
- Department of Environmental and Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, Milano, Italy.
| | - Qasim Chaudhry
- University of Chester, Parkgate Road, Chester CH1 4BJ, United Kingdom
| | | | - Jean Lou Dorne
- Scientific Committee and Emerging Risks Unit, European Food Safety Authority, Via Carlo Magno 1A, Parma, Italy
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Tugcu G, Charehsaz M, Aydın A. Toxicological evaluation of ergocalciferol, cholecalciferol, and their metabolites by a category approach. Drug Chem Toxicol 2019; 44:661-667. [PMID: 31412708 DOI: 10.1080/01480545.2019.1650061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Predictive toxicology plays an integral role in determining the toxicological profiles of chemicals for safety assessment. Vitamin D is an essential vitamin for the regulation of calcium absorption and homeostasis, as well as the treatment and prevention of several diseases such as rickets and osteomalacia. According to European Medicines Agency (EMA) Guideline on setting health-based exposure limits for use in risk identification in the manufacturing of different medicinal products in shared facilities, permitted daily exposure (PDE) calculation for active pharmaceutical ingredients (APIs) should be done by the medicinal product producers. PDE calculation is mainly based on critical toxicological endpoints such as repeated dose toxicity, genotoxicity, carcinogenicity, developmental and reproductive toxicity, and hypersensitivity potential. During this procedure, critical toxicological endpoints data of an API can be used to predict the PDE of another API that has a similar chemical structure. In the present paper, human toxicological endpoints of vitamin D2, D3, and their metabolites were evaluated and afterwards the data gaps in the toxicological endpoints were filled by forming a category. The read-across was justified by the structural and metabolic similarities. Molecular similarity and mechanistic relevance were found to be substantial, resulting in low uncertainty. The untested vitamin D analogs within the category can be read across with confidence to complete the data gaps related to the human health endpoints.
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Affiliation(s)
- Gulcin Tugcu
- Department of Toxicology, Faculty of Pharmacy, Yeditepe University, Istanbul, Turkey
| | - Mohammad Charehsaz
- Department of Toxicology, Faculty of Pharmacy, Yeditepe University, Istanbul, Turkey
| | - Ahmet Aydın
- Department of Toxicology, Faculty of Pharmacy, Yeditepe University, Istanbul, Turkey
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Madden JC, Pawar G, Cronin MT, Webb S, Tan YM, Paini A. In silico resources to assist in the development and evaluation of physiologically-based kinetic models. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.comtox.2019.03.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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17
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Patlewicz G, Lizarraga LE, Rua D, Allen DG, Daniel AB, Fitzpatrick SC, Garcia-Reyero N, Gordon J, Hakkinen P, Howard AS, Karmaus A, Matheson J, Mumtaz M, Richarz AN, Ruiz P, Scarano L, Yamada T, Kleinstreuer N. Exploring current read-across applications and needs among selected U.S. Federal Agencies. Regul Toxicol Pharmacol 2019; 106:197-209. [PMID: 31078681 PMCID: PMC6814248 DOI: 10.1016/j.yrtph.2019.05.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 04/27/2019] [Accepted: 05/08/2019] [Indexed: 10/26/2022]
Abstract
Read-across is a well-established data gap-filling technique applied for regulatory purposes. In US Environmental Protection Agency's New Chemicals Program under TSCA, read-across has been used extensively for decades, however the extent of application and acceptance of read-across among U.S. federal agencies is less clear. In an effort to build read-across capacity, raise awareness of the state of the science, and work towards a harmonization of read-across approaches across U.S. agencies, a new read-across workgroup was established under the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM). This is one of several ad hoc groups ICCVAM has convened to implement the ICCVAM Strategic Roadmap. In this article, we outline the charge and scope of the workgroup and summarize the current applications, tools used, and needs of the agencies represented on the workgroup for read-across. Of the agencies surveyed, the Environmental Protection Agency had the greatest experience in using read-across whereas other agencies indicated that they would benefit from gaining a perspective of the landscape of the tools and available guidance. Two practical case studies are also described to illustrate how the read-across approaches applied by two agencies vary on account of decision context.
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Affiliation(s)
- Grace Patlewicz
- (a)National Center for Computational Toxicology, U.S. Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC, 27709, USA.
| | - Lucina E Lizarraga
- (b)National Center for Environmental Assessment, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, OH, 45268, USA
| | - Diego Rua
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
| | - David G Allen
- ILS, P.O. Box 13501, Research Triangle Park, NC, 27709, USA
| | - Amber B Daniel
- ILS, P.O. Box 13501, Research Triangle Park, NC, 27709, USA
| | - Suzanne C Fitzpatrick
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, 5100 Paint Branch Parkway, College Park, MD, 20740, USA
| | - Natàlia Garcia-Reyero
- Environmental Laboratory, U.S. Army Engineer Research and Developmental Center, 3909 Halls Ferry Rd., Vicksburg, MS, 39180, USA
| | - John Gordon
- U.S. Consumer Product Safety Commission, 5 Research Place, Rockville, MD, 20850, USA
| | - Pertti Hakkinen
- National Library of Medicine, 6707 Democracy Blvd., Bethesda, MD, 20892, USA
| | | | - Agnes Karmaus
- ILS, P.O. Box 13501, Research Triangle Park, NC, 27709, USA
| | - Joanna Matheson
- U.S. Consumer Product Safety Commission, 5 Research Place, Rockville, MD, 20850, USA
| | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, 1600 Clifton Rd., Chamblee, GA, 30341, USA
| | | | - Patricia Ruiz
- Agency for Toxic Substances and Disease Registry, 1600 Clifton Rd., Chamblee, GA, 30341, USA
| | - Louis Scarano
- Office of Pollution Prevention and Toxics, U.S. Environmental Protection Agency, 1200 Pennsylvania Ave. NW, Washington, DC, 20460, USA
| | - Takashi Yamada
- Division of Risk Assessment, Biological Safety Research Center, National Institute of Health Sciences, 3-25-26, Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa, 210-9501, Japan
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC, 27709, USA
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18
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Mellor C, Marchese Robinson R, Benigni R, Ebbrell D, Enoch S, Firman J, Madden J, Pawar G, Yang C, Cronin M. Molecular fingerprint-derived similarity measures for toxicological read-across: Recommendations for optimal use. Regul Toxicol Pharmacol 2019; 101:121-134. [DOI: 10.1016/j.yrtph.2018.11.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 10/09/2018] [Accepted: 11/12/2018] [Indexed: 12/20/2022]
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Chushak YG, Shows HW, Gearhart JM, Pangburn HA. In silico identification of protein targets for chemical neurotoxins using ToxCast in vitro data and read-across within the QSAR toolbox. Toxicol Res (Camb) 2018; 7:423-431. [PMID: 30090592 PMCID: PMC6061186 DOI: 10.1039/c7tx00268h] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 01/10/2018] [Indexed: 12/12/2022] Open
Abstract
This study evaluates the application of QSAR Toolbox and ToxCast screening data to identify neurological targets for pyrethroids.
There are many mechanisms of neurotoxicity that are initiated by the interaction of chemicals with different neurological targets. Under the U.S. Environmental Protection Agency's ToxCast program, the biological activity of thousands of chemicals was screened in biochemical and cell-based assays in a high-throughput manner. Two hundred sixteen assays in the ToxCast screening database were identified as targeting a total of 123 proteins having neurological functions according to the Gene Ontology database. Data from these assays were imported into the Organization for Economic Co-operation and Development QSAR Toolbox and used to predict neurological targets for chemical neurotoxins. Two sets of data were generated: one set was used to classify compounds as active or inactive and another set, composed of AC50s for only active compounds, was used to predict AC50 values for unknown chemicals. Chemical grouping and read-across within the QSAR Toolbox were used to identify neurologic targets and predict interactions for pyrethroids, a class of compounds known to elicit neurotoxic effects in humans. The classification prediction results showed 79% accuracy while AC50 predictions demonstrated mixed accuracy compared with the ToxCast screening data.
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Affiliation(s)
- Y G Chushak
- Henry M Jackson Foundation for the Advancement of Military Medicine , Wright-Patterson AFB , Ohio 45433 , USA .
| | - H W Shows
- Biological Sciences Department , Wright State University , Dayton , Ohio 45435 , USA
| | - J M Gearhart
- Henry M Jackson Foundation for the Advancement of Military Medicine , Wright-Patterson AFB , Ohio 45433 , USA .
| | - H A Pangburn
- United States Air Force School of Aerospace Medicine , Aeromedical Research Department , Force Health Protection , Wright-Patterson AFB , Ohio 45433 , USA
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Abstract
QSAR (quantitative structure-activity relationship) is a method for predicting the physical and biological properties of small molecules; it is today in large use in companies and public services. However, as any scientific method, it is nowadays challenged by more and more requests, especially considering its possible role in assessing the safety of new chemicals. Posing the question whether QSAR is a way not only to exploit available knowledge but also to build new knowledge, we shortly review QSAR history, thus searching for a QSAR epistemology. We consider the three pillars on which QSAR stands: biological data, chemical knowledge, and modeling algorithms. Most of the time we assume that biological data is a true picture of the world (as they result from good experimental practice), that chemical knowledge is scientifically true; so if a QSAR is not working, blame modeling. This opens the way to look at the role of modeling in developing scientific theories, and in producing knowledge. QSAR is a mature technology; however, debate is still active in many topics, in particular about the acceptability of the models and how they are explained. After an excursus in inductive reasoning, we relate the QSAR methodology to open debates in the philosophy of science.
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21
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Olejnik A, Sliwowska A, Nowak I. A conjugate of jasmonic acid and tetrapeptide Tyr-Tyr-Lys-Ser-NH 2 as a potential ingredient for skin care products – synthesis and characterization. NEW J CHEM 2018. [DOI: 10.1039/c8nj01843j] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A conjugate of jasmonic acid and a tetrapeptide was synthesized and it might be used as a promising active ingredient in topical applications.
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Affiliation(s)
- Anna Olejnik
- Faculty of Chemistry
- Adam Mickiewicz University in Poznań, ul. Umultowska 89b
- 61-614 Poznań
- Poland
| | - Alicja Sliwowska
- Faculty of Chemistry
- Adam Mickiewicz University in Poznań, ul. Umultowska 89b
- 61-614 Poznań
- Poland
| | - Izabela Nowak
- Faculty of Chemistry
- Adam Mickiewicz University in Poznań, ul. Umultowska 89b
- 61-614 Poznań
- Poland
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Abstract
In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.
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Abstract
Read-across has become popular since the introduction of regulations, such as the European REACH regulation. This chapter provides instructions on how to use ToxRead, new freely available software for read-across analysis, and on how to interpret its output predictions for mutagenicity assessments.This tool offers two seminal sources: a set of rules/structural alerts, which may explain the toxicity, and a similarity tool, associated with a large database of chemicals with their properties.
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Vračko M, Drgan V. Grouping of CoMPARA data with respect to compounds from the carcinogenic potency database. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2017; 28:801-813. [PMID: 29156996 DOI: 10.1080/1062936x.2017.1398184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 10/25/2017] [Indexed: 06/07/2023]
Abstract
Methods for clustering and measures of similarity of chemical structures have become an important supporting tool in chemoinformatics. They represent the basis for categorization of chemicals and read-across, where a molecular property is estimated from 'similar molecules'. This study proposes a clustering scheme within the given dataset with respect to a reference dataset. The scheme was applied on two datasets ToxCast_AR_Agonist and ToxCast_AR_Antagonists with 1654 and 1522 compounds, respectively. The compounds are tested to androgen receptor activity (AR) in 11 high throughput screening assays. The carcinogenic dataset was used as the reference set.
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Affiliation(s)
- M Vračko
- a National Institute of Chemistry , Kemijski Inštitut , Ljubljana , Slovenia
| | - V Drgan
- a National Institute of Chemistry , Kemijski Inštitut , Ljubljana , Slovenia
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Grace P, George H, Prachi P, Imran S. Navigating through the minefield of read-across tools: A review of in silico tools for grouping. ACTA ACUST UNITED AC 2017; 3:1-18. [PMID: 30221211 DOI: 10.1016/j.comtox.2017.05.003] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Read-across is a popular data gap filling technique used within analogue and category approaches for regulatory purposes. In recent years there have been many efforts focused on the challenges involved in read-across development, its scientific justification and documentation. Tools have also been developed to facilitate read-across development and application. Here, we describe a number of publicly available read-across tools in the context of the category/analogue workflow and review their respective capabilities, strengths and weaknesses. No single tool addresses all aspects of the workflow. We highlight how the different tools complement each other and some of the opportunities for their further development to address the continued evolution of read-across.
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Affiliation(s)
- Patlewicz Grace
- National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park (RTP), NC 27711, USA
| | - Helman George
- National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park (RTP), NC 27711, USA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA
| | - Pradeep Prachi
- National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park (RTP), NC 27711, USA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA
| | - Shah Imran
- National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park (RTP), NC 27711, USA
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26
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Drgan V, Župerl Š, Vračko M, Cappelli CI, Novič M. CPANNatNIC software for counter-propagation neural network to assist in read-across. J Cheminform 2017; 9:30. [PMID: 29086050 PMCID: PMC5440416 DOI: 10.1186/s13321-017-0218-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Accepted: 05/15/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND CPANNatNIC is software for development of counter-propagation artificial neural network models. Besides the interface for training of a new neural network it also provides an interface for visualisation of the results which was developed to aid in interpretation of the results and to use the program as a tool for read-across. RESULTS The work presents the details of the program's interface. Parts of the interface are presented and how they can be used. The examples provided show how the user can build a new model and view the results of predictions using the interface. Examples are given to show how the software may be used in read-across. CONCLUSIONS CPANNatNIC provides a simple user interface for model development and visualisation. The interface implements options which may simplify read-across procedure. Statistical results show better prediction accuracy of read-across predictions than model predictions where similar compounds could be identified, which indicates the importance of using read-across and usefulness of the program.
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Affiliation(s)
- Viktor Drgan
- Department of Cheminformatics, National Institute of Chemistry, Hajdrihova 19, 1001, Ljubljana, Slovenia.
| | - Špela Župerl
- Department of Cheminformatics, National Institute of Chemistry, Hajdrihova 19, 1001, Ljubljana, Slovenia
| | - Marjan Vračko
- Department of Cheminformatics, National Institute of Chemistry, Hajdrihova 19, 1001, Ljubljana, Slovenia
| | - Claudia Ileana Cappelli
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milan, Italy
| | - Marjana Novič
- Department of Cheminformatics, National Institute of Chemistry, Hajdrihova 19, 1001, Ljubljana, Slovenia
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27
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Shah I, Liu J, Judson RS, Thomas RS, Patlewicz G. Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information. Regul Toxicol Pharmacol 2016; 79:12-24. [DOI: 10.1016/j.yrtph.2016.05.008] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Revised: 04/20/2016] [Accepted: 05/03/2016] [Indexed: 02/03/2023]
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Benfenati E, Belli M, Borges T, Casimiro E, Cester J, Fernandez A, Gini G, Honma M, Kinzl M, Knauf R, Manganaro A, Mombelli E, Petoumenou MI, Paparella M, Paris P, Raitano G. Results of a round-robin exercise on read-across. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:371-384. [PMID: 27167159 DOI: 10.1080/1062936x.2016.1178171] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Accepted: 04/11/2016] [Indexed: 06/05/2023]
Abstract
A round-robin exercise was conducted within the CALEIDOS LIFE project. The participants were invited to assess the hazard posed by a substance, applying in silico methods and read-across approaches. The exercise was based on three endpoints: mutagenicity, bioconcentration factor and fish acute toxicity. Nine chemicals were assigned for each endpoint and the participants were invited to complete a specific questionnaire communicating their conclusions. The interesting aspect of this exercise is the justification behind the answers more than the final prediction in itself. Which tools were used? How did the approach selected affect the final answer?
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Affiliation(s)
- E Benfenati
- a IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milano , Italy
| | - M Belli
- a IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milano , Italy
| | - T Borges
- b Direcção-Geral da Saúde , Lisboa , Portugal
| | - E Casimiro
- c INFOTOX, Consultores de Riscos Ambientais e Tecnológicos, Lda , Lisboa , Portugal
| | - J Cester
- d Universitat Rovira i Virgili , Tarragona , Spain
| | - A Fernandez
- d Universitat Rovira i Virgili , Tarragona , Spain
| | - G Gini
- e Politecnico di Milano, Dipartimento di Elettronica e Informazione , Milan , Italy
| | - M Honma
- f Division of Genetics and Mutagenesis , National Institute of Health Sciences , Tokyo , Japan
| | - M Kinzl
- g Umweltbundesamt GmbH , Vienna , Austria
| | - R Knauf
- h Centro REACH S.r.l. , Milan , Italy
| | | | - E Mombelli
- j Institut National de l'Environnement Industriel et des Risques , Verneuil-en-Halatte , France
| | - M I Petoumenou
- a IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milano , Italy
| | | | - P Paris
- k Istituto Superiore per la Protezione e la Ricerca Ambientale , Rome , Italy
| | - G Raitano
- a IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milano , Italy
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Golbamaki A, Benfenati E, Golbamaki N, Manganaro A, Merdivan E, Roncaglioni A, Gini G. New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS 2016; 34:97-113. [PMID: 26986491 DOI: 10.1080/10590501.2016.1166879] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In this study, new molecular fragments associated with genotoxic and nongenotoxic carcinogens are introduced to estimate the carcinogenic potential of compounds. Two rule-based carcinogenesis models were developed with the aid of SARpy: model R (from rodents' experimental data) and model E (from human carcinogenicity data). Structural alert extraction method of SARpy uses a completely automated and unbiased manner with statistical significance. The carcinogenicity models developed in this study are collections of carcinogenic potential fragments that were extracted from two carcinogenicity databases: the ANTARES carcinogenicity dataset with information from bioassay on rats and the combination of ISSCAN and CGX datasets, which take into accounts human-based assessment. The performance of these two models was evaluated in terms of cross-validation and external validation using a 258 compound case study dataset. Combining R and H predictions and scoring a positive or negative result when both models are concordant on a prediction, increased accuracy to 72% and specificity to 79% on the external test set. The carcinogenic fragments present in the two models were compared and analyzed from the point of view of chemical class. The results of this study show that the developed rule sets will be a useful tool to identify some new structural alerts of carcinogenicity and provide effective information on the molecular structures of carcinogenic chemicals.
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Affiliation(s)
- Azadi Golbamaki
- a Laboratory of Environmental Chemistry and Toxicology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - Emilio Benfenati
- a Laboratory of Environmental Chemistry and Toxicology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - Nazanin Golbamaki
- b DRC/VIVA/METO Unit, Institut National de l.Environnement Industriel et des Risques (INERIS), Parc Technologique Alata , Verneuil en Halatte , France
| | - Alberto Manganaro
- a Laboratory of Environmental Chemistry and Toxicology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - Erinc Merdivan
- c Faculty of Engineering and Natural Sciences, Sabancı University , Tuzla/Istanbul , Turkey
| | - Alessandra Roncaglioni
- a Laboratory of Environmental Chemistry and Toxicology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
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30
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Patlewicz G, Fitzpatrick JM. Current and Future Perspectives on the Development, Evaluation, and Application of in Silico Approaches for Predicting Toxicity. Chem Res Toxicol 2016; 29:438-51. [PMID: 26686752 DOI: 10.1021/acs.chemrestox.5b00388] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Exploiting non-testing approaches to predict toxicity early in the drug discovery development cycle is a helpful component in minimizing expensive drug failures due to toxicity being identified in late development or even during clinical trials. Changes in regulations in the industrial chemicals and cosmetics sectors in recent years have prompted a significant number of advances in the development, application, and assessment of non-testing approaches, such as (Q)SARs. Many efforts have also been undertaken to establish guiding principles for performing read-across within category and analogue approaches. This review offers a perspective, as taken from these sectors, of the current status of non-testing approaches, their evolution in light of the advances in high-throughput approaches and constructs such as adverse outcome pathways, and their potential relevance for drug discovery. It also proposes a workflow for how non-testing approaches could be practically integrated within testing and assessment strategies.
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Affiliation(s)
- Grace Patlewicz
- National Center for Computational Toxicology (NCCT), U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Jeremy M Fitzpatrick
- National Center for Computational Toxicology (NCCT), U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
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31
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Kamath P, Raitano G, Fernández A, Rallo R, Benfenati E. In silico exploratory study using structure-activity relationship models and metabolic information for prediction of mutagenicity based on the Ames test and rodent micronucleus assay. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2015; 26:1017-1031. [PMID: 26565432 DOI: 10.1080/1062936x.2015.1108932] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The mutagenic potential of chemicals is a cause of growing concern, due to the possible impact on human health. In this paper we have developed a knowledge-based approach, combining information from structure-activity relationship (SAR) and metabolic triggers generated from the metabolic fate of chemicals in biological systems for prediction of mutagenicity in vitro based on the Ames test and in vivo based on the rodent micronucleus assay. In the first part of the work, a model was developed, which comprises newly generated SAR rules and a set of metabolic triggers. These SAR rules and metabolic triggers were further externally validated to predict mutagenicity in vitro, with metabolic triggers being used only to predict mutagenicity of chemicals, which were predicted unknown, by SARpy. Hence, this model has a higher accuracy than the SAR model, with an accuracy of 89% for the training set and 75% for the external validation set. Subsequently, the results of the second part of this work enlist a set of metabolic triggers for prediction of mutagenicity in vivo, based on the rodent micronucleus assay. Finally, the results of the third part enlist a list of metabolic triggers to find similarities and differences in the mutagenic response of chemicals in vitro and in vivo.
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Affiliation(s)
- P Kamath
- a Departament d'Enginyeria Quimica , Universitat Rovira i Virgili , Tarragona , Spain
| | - G Raitano
- b Laboratory of Environmental Chemistry and Toxicology, Department Environmental Health Sciences , Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - A Fernández
- a Departament d'Enginyeria Quimica , Universitat Rovira i Virgili , Tarragona , Spain
| | - R Rallo
- c Departament d'Enginyeria Informatica i Matematiques , Universitat Rovira i Virgili , Tarragona , Spain
| | - E Benfenati
- b Laboratory of Environmental Chemistry and Toxicology, Department Environmental Health Sciences , Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
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32
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Pizzo F, Gadaleta D, Lombardo A, Nicolotti O, Benfenati E. Identification of structural alerts for liver and kidney toxicity using repeated dose toxicity data. Chem Cent J 2015; 9:62. [PMID: 26550029 PMCID: PMC4635184 DOI: 10.1186/s13065-015-0139-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Accepted: 10/27/2015] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND The potential for a compound to cause hepatotoxicity and nephrotoxicity is a matter of extreme interest for human health risk assessment. To assess liver and kidney toxicity, repeated-dose toxicity (RDT) studies are conducted mainly on rodents. However, these tests are expensive, time-consuming and require large numbers of animals. For early toxicity screening, in silico models can be applied, reducing the costs, time and animals used. Among in silico approaches, structure-activity relationship (SAR) methods, based on the identification of chemical substructures (structural alerts, SAs) related to a particular activity (toxicity), are widely employed. RESULTS We identified and evaluated some SAs related to liver and kidney toxicity, using RDT data on rats taken from the hazard evaluation support system (HESS) database. We considered only SAs that gave the best percentages of true positives (TP). CONCLUSIONS It was not possible to assign an unambiguous mode of action for all the SAs, but a mechanistic explanation is provided for some of them. Such achievements may help in the early identification of liver and renal toxicity of substances.
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Affiliation(s)
- Fabiola Pizzo
- />Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche “Mario Negri”, Via La Masa 19, 20159 Milan, Italy
| | - Domenico Gadaleta
- />Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche “Mario Negri”, Via La Masa 19, 20159 Milan, Italy
- />Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Anna Lombardo
- />Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche “Mario Negri”, Via La Masa 19, 20159 Milan, Italy
| | - Orazio Nicolotti
- />Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Emilio Benfenati
- />Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche “Mario Negri”, Via La Masa 19, 20159 Milan, Italy
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Benfenati E, Roncaglioni A, Petoumenou MI, Cappelli CI, Gini G. Integrating QSAR and read-across for environmental assessment. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2015; 26:605-618. [PMID: 26535447 DOI: 10.1080/1062936x.2015.1078408] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Accepted: 07/28/2015] [Indexed: 06/05/2023]
Abstract
Read-across and QSAR have different traditions and drawbacks. We address here two main questions: (1) How do we solve the issue of the subjectivity in the evaluation of data and results, which may be particularly critical for read-across, but may have a role also for the QSAR assessment? (2) How do we take advantage of the results of both approaches to support each other? The QSAR model starts from the training set. The presence of similar chemicals with property values close to that predicted can support the result. The approach in read-across is the opposite. The assessment is focused on the few substances similar to the target. The data quality of the similar chemicals is fundamental. A risk is poor standardization in the definition of 'similarity', because different approaches may be applied. Inspired by the principles of high transparency and reproducibility, a new program for read-across, called ToxRead, has been developed and made freely available ( www.toxgate.eu ). The output of ToxRead can be compared and integrated with the output of QSAR, within a weight-of-evidence strategy. We discuss the evaluation and integration of ToxRead and QSAR with examples of the assessment of bioconcentration factors of chemicals.
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Affiliation(s)
- E Benfenati
- a IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milano , Italy
| | - A Roncaglioni
- a IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milano , Italy
| | - M I Petoumenou
- a IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milano , Italy
| | - C I Cappelli
- a IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milano , Italy
| | - G Gini
- b Dipartimento di Elettronica, Informazione e Bioingegneria , Politecnico di Milano , Milano , Italy
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Vračko M, Bobst S. Prediction of mutagenicity and carcinogenicity using in silico modelling: A case study of polychlorinated biphenyls. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2015; 26:667-682. [PMID: 26329919 DOI: 10.1080/1062936x.2015.1080185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2015] [Accepted: 08/03/2015] [Indexed: 06/05/2023]
Abstract
In silico modelling is an important alternative method for the evaluation of properties of chemical compounds. Basically, two concepts are used in its applications: QSAR modelling for endpoint predictions, and grouping (categorization) of large groups of chemicals. In the presented report we address both of these concepts. As a case study we present the results on a set of polychlorinated biphenyls (PCBs) and some of their metabolites. Their mutagenicity and carcinogenic potency were evaluated with CAESAR and T.E.S.T. models, which are freely available over the internet. We discuss the value and reliability of the predictions, the applicability domain of models and the ability to create prioritized groupings of PCBs as a category of chemicals.
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Affiliation(s)
- M Vračko
- a Kemijski Inštitut/National Institute of Chemistry , Ljubljana , Slovenia
| | - S Bobst
- b Nexeo Solutions LLC , Texas , USA
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Benfenati E, Manganelli S, Giordano S, Raitano G, Manganaro A. Hierarchical Rules for Read-Across and In Silico Models of Mutagenicity. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS 2015; 33:385-403. [PMID: 26403277 DOI: 10.1080/10590501.2015.1096881] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
A broad set of rules has been implemented within the ToxRead software for read-across of chemicals for bacterial mutagenicity. These rules were obtained by manually analyzing more than 6000 chemicals and the associated chemical classes. A hierarchy of rules was established to identify those most specifically relating to the target compounds, linked in sequence to the other, more generic ones, which may match with the target compound. Rules related to both mutagenicity and lack of mutagenicity were found. Some of the latter are exceptions to the mutagenicity rules, while others are modulators of activity. These rules can also be used to predict mutagenicity, offering good performance.
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Affiliation(s)
- Emilio Benfenati
- a IRCCS-Istituto di Ricerche Farmacologiche 'Mario Negri,' Department of Environmental Health Sciences , Milan , Italy
| | - Serena Manganelli
- a IRCCS-Istituto di Ricerche Farmacologiche 'Mario Negri,' Department of Environmental Health Sciences , Milan , Italy
| | - Sabrina Giordano
- a IRCCS-Istituto di Ricerche Farmacologiche 'Mario Negri,' Department of Environmental Health Sciences , Milan , Italy
| | - Giuseppa Raitano
- a IRCCS-Istituto di Ricerche Farmacologiche 'Mario Negri,' Department of Environmental Health Sciences , Milan , Italy
| | - Alberto Manganaro
- a IRCCS-Istituto di Ricerche Farmacologiche 'Mario Negri,' Department of Environmental Health Sciences , Milan , Italy
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