1
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Lee J. Development of quantitative structure activity relationships (QSARs) for predicting the aggregation of TiO 2 nanoparticles under favorable conditions. Heliyon 2024; 10:e27966. [PMID: 38571612 PMCID: PMC10987904 DOI: 10.1016/j.heliyon.2024.e27966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 01/08/2024] [Accepted: 03/08/2024] [Indexed: 04/05/2024] Open
Abstract
This study developed multi-linear regression (MLR) quantitative structure-activity relationships (QSARs) to predict n-TiO2 aggregation in the presence of high concentrations of representative emerging organic contaminants (EOCs), which presented favorable conditions to interaction with n-TiO2. The largest diameter change (Δ 517 nm at 0 h and Δ 1164 nm at 12 h) of n-TiO2 was observed by estrone, while the smallest diameter change (Δ -114 nm at 0 h and - 4 nm at 12 h) was observed by lincomycin during experimental periods. In addition, the zeta potential changes of n-TiO2 were observed that the biggest changes were observed by 17β-estradiol (-1.3 mV) and alachlor (-10.02 mV) at 0 h, while 17β-estradiol (-1.31 mV) and pendimethalin (-11.4 mV) showed the biggest changes at 12 h comparing to control. These changes of n-TiO2 diameter and zeta potential may implicate the effects of unique physico-chemical properties of each EOC on the surface modification of n-TiO2. Based on the interaction results, this study investigated the QSARs between n-TiO2 aggregation and physico-chemical descriptors of EOCs with 7 representative descriptors (pKa, Cw, log Kow, M.W., P.S.A., M.V., # of HBD) for predicting n-TiO2 aggregation rate kinetics at 0 h and 12 h by applying MATLAB statistical methods (model 1 - fitlm and model 2 - stepwiselm). In a model 1, QSARs showed the good coefficients of determination (R2 = 0.92) at 0 h and (R2 = 0.87) at 12 h with 7 descriptors. In a model 2, QSARs showed the goodness of fit of a model (R2 = 0.9998) with 8 descriptors (pKa, Cw, log Kow, M.W., P.S.A., M.V., #HBD, pKa⋅#H bond donors) at 0 h, while QSARs showed the coefficients of determination (R2 = 0.68) with 2 descriptors (pKa, M.V.) at 12 h. Particularly, we observed that some descriptors of EOCs such as pKa and # of HBD having polarity have more influenced on the n-TiO2 aggregation rate kinetics. Our developed QSARs demonstrated that the 7 descriptors of EOCs were significantly effective descriptors for predicting n-TiO2 aggregation rate kinetics in favorable conditions, which may implicate the complexity interactions between heterogeneous surfaces of n-TiO2 and physico-chemical properties of EOCs.
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Affiliation(s)
- Jaewoong Lee
- Department of Civil Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
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2
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Singh AV, Shelar A, Rai M, Laux P, Thakur M, Dosnkyi I, Santomauro G, Singh AK, Luch A, Patil R, Bill J. Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:2835-2852. [PMID: 38315814 DOI: 10.1021/acs.jafc.3c06466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
This comprehensive review explores the emerging landscape of Nano-QSAR (quantitative structure-activity relationship) for assessing the risk and potency of nanomaterials in agricultural settings. The paper begins with an introduction to Nano-QSAR, providing background and rationale, and explicitly states the hypotheses guiding the review. The study navigates through various dimensions of nanomaterial applications in agriculture, encompassing their diverse properties, types, and associated challenges. Delving into the principles of QSAR in nanotoxicology, this article elucidates its application in evaluating the safety of nanomaterials, while addressing the unique limitations posed by these materials. The narrative then transitions to the progression of Nano-QSAR in the context of agricultural nanomaterials, exemplified by insightful case studies that highlight both the strengths and the limitations inherent in this methodology. Emerging prospects and hurdles tied to Nano-QSAR in agriculture are rigorously examined, casting light on important pathways forward, existing constraints, and avenues for research enhancement. Culminating in a synthesis of key insights, the review underscores the significance of Nano-QSAR in shaping the future of nanoenabled agriculture. It provides strategic guidance to steer forthcoming research endeavors in this dynamic field.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute of Risk Assessment (BfR), Maxdohrnstrasse 8-10, 10589 Berlin, Germany
| | - Amruta Shelar
- Department of Technology, Savitribai Phule Pune University, Pune 411007, India
| | - Mansi Rai
- Department of Microbiology, Central University of Rajasthan NH-8, Bandar Sindri, Dist-Ajmer-305817, Rajasthan, India
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute of Risk Assessment (BfR), Maxdohrnstrasse 8-10, 10589 Berlin, Germany
| | - Manali Thakur
- Uniklinik Köln, Kerpener Strasse 62, 50937 Köln Germany
| | - Ievgen Dosnkyi
- Institute of Chemistry and Biochemistry Department of Organic ChemistryFreie Universität Berlin Takustr. 3 14195 Berlin, Germany
| | - Giulia Santomauro
- Institute for Materials Science, Department of Bioinspired Materials, University of Stuttgart, 70569, Stuttgart, Germany
| | - Alok Kumar Singh
- Department of Plant Molecular Biology & Genetic Engineering, ANDUA&T, Ayodhya 224229, Uttar Pradesh, India
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute of Risk Assessment (BfR), Maxdohrnstrasse 8-10, 10589 Berlin, Germany
| | - Rajendra Patil
- Department of Technology, Savitribai Phule Pune University, Pune 411007, India
| | - Joachim Bill
- Institute for Materials Science, Department of Bioinspired Materials, University of Stuttgart, 70569, Stuttgart, Germany
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3
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Furxhi I, Willighagen E, Evelo C, Costa A, Gardini D, Ammar A. A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability. NANOIMPACT 2023; 31:100475. [PMID: 37423508 DOI: 10.1016/j.impact.2023.100475] [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: 03/28/2023] [Revised: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 07/11/2023]
Abstract
INTRODUCTION The current effort towards the digital transformation across multiple scientific domains requires data that is Findable, Accessible, Interoperable and Reusable (FAIR). In addition to the FAIR data, what is required for the application of computational tools, such as Quantitative Structure Activity Relationships (QSARs), is a sufficient data volume and the ability to merge sources into homogeneous digital assets. In the nanosafety domain there is a lack of FAIR available metadata. METHODOLOGY To address this challenge, we utilized 34 datasets from the nanosafety domain by exploiting the NanoSafety Data Reusability Assessment (NSDRA) framework, which allowed the annotation and assessment of dataset's reusability. From the framework's application results, eight datasets targeting the same endpoint (i.e. numerical cellular viability) were selected, processed and merged to test several hypothesis including universal versus nanogroup-specific QSAR models (metal oxide and nanotubes), and regression versus classification Machine Learning (ML) algorithms. RESULTS Universal regression and classification QSARs reached an 0.86 R2 and 0.92 accuracy, respectively, for the test set. Nanogroup-specific regression models reached 0.88 R2 for nanotubes test set followed by metal oxide (0.78). Nanogroup-specific classification models reached 0.99 accuracy for nanotubes test set, followed by metal oxide (0.91). Feature importance revealed different patterns depending on the dataset with common influential features including core size, exposure conditions and toxicological assay. Even in the case where the available experimental knowledge was merged, the models still failed to correctly predict the outputs of an unseen dataset, revealing the cumbersome conundrum of scientific reproducibility in realistic applications of QSAR for nanosafety. To harness the full potential of computational tools and ensure their long-term applications, embracing FAIR data practices is imperative in driving the development of responsible QSAR models. CONCLUSIONS This study reveals that the digitalization of nanosafety knowledge in a reproducible manner has a long way towards its successful pragmatic implementation. The workflow carried out in the study shows a promising approach to increase the FAIRness across all the elements of computational studies, from dataset's annotation, selection, merging to FAIR modeling reporting. This has significant implications for future research as it provides an example of how to utilize and report different tools available in the nanosafety knowledge system, while increasing the transparency of the results. One of the main benefits of this workflow is that it promotes data sharing and reuse, which is essential for advancing scientific knowledge by making data and metadata FAIR compliant. In addition, the increased transparency and reproducibility of the results can enhance the trustworthiness of the computational findings.
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Affiliation(s)
- Irini Furxhi
- Transgero Limited, Cullinagh, Newcastle West, Co. Limerick, Ireland; Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93, Ireland.
| | - Egon Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, the Netherlands.
| | - Chris Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, the Netherlands.
| | - Anna Costa
- National Research Council, Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy.
| | - Davide Gardini
- National Research Council, Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy.
| | - Ammar Ammar
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, the Netherlands.
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4
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Yan X, Yue T, Winkler DA, Yin Y, Zhu H, Jiang G, Yan B. Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation. Chem Rev 2023. [PMID: 37262026 DOI: 10.1021/acs.chemrev.3c00070] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.
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Affiliation(s)
- Xiliang Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Tongtao Yue
- Key Laboratory of Marine Environment and Ecology, Ministry of Education, Institute of Coastal Environmental Pollution Control, Ocean University of China, Qingdao 266100, China
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham NG7 2QL, U.K
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia
| | - Yongguang Yin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Bing Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
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5
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Gomes SIL, Roca CP, Pokhrel S, Mädler L, Scott-Fordsmand JJ, Amorim MJB. TiO 2 nanoparticles' library toxicity (UV and non-UV exposure) - High-throughput in vivo transcriptomics reveals mechanisms. NANOIMPACT 2023; 30:100458. [PMID: 36858316 DOI: 10.1016/j.impact.2023.100458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/21/2023] [Accepted: 02/24/2023] [Indexed: 06/03/2023]
Abstract
The hazards of nanomaterials/nanoparticles (NMs/NPs) are mostly assessed using individual NMs, and a more systematic approach, using many NMs, is needed to evaluate its risks in the environment. Libraries of NMs, with a range of identified different but related characters/descriptors allow the comparison of effects across many NMs. The effects of a custom designed Fe-doped TiO2 NMs library containing 11 NMs was assessed on the soil model Enchytraeus crypticus (Oligochaeta), both with and without UV (standard fluorescent) radiation. Effects were analyzed at organism (phenotypic, survival and reproduction) and gene expression level (transcriptomics, high-throughput 4x44K microarray) to understand the underlying mechanisms. A total of 48 microarrays (20 test conditions) were done plus controls (UV and non-UV). Unique mechanisms induced by TiO2 NPs exposure included the impairment in RNA processing for TiO2_10nm, or deregulated apoptosis for 2%FeTiO2_10nm. Strikingly apparent was the size dependent effects such as induction of reproductive effects via smaller TiO2 NPs (≤12 nm) - embryo interaction, while larger particles (27 nm) caused reproductive effects through different mechanisms. Also, phagocytosis was affected by 12 and 27 nm NPs, but not by ≤11 nm. The organism level study shows the integrated response, i.e. the result after a cascade of events. While uni-cell models offer key mechanistic information, we here deliver a combined biological system level (phenotype and genotype), seldom available, especially for environmental models.
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Affiliation(s)
- Susana I L Gomes
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Carlos P Roca
- Department of Ecoscience, Aarhus University, C.F. Møllers Alle 4, DK-8000, Aarhus, Denmark
| | - Suman Pokhrel
- Department of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany; Leibniz Institute for Materials Engineering IWT, Badgasteiner Str. 3, 28359 Bremen, Germany
| | - Lutz Mädler
- Department of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany; Leibniz Institute for Materials Engineering IWT, Badgasteiner Str. 3, 28359 Bremen, Germany
| | | | - Mónica J B Amorim
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
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6
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Furxhi I, Bengalli R, Motta G, Mantecca P, Kose O, Carriere M, Haq EU, O’Mahony C, Blosi M, Gardini D, Costa A. Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case Study of Silver Nanoforms. ACS APPLIED NANO MATERIALS 2023; 6:3948-3962. [PMID: 36938492 PMCID: PMC10012170 DOI: 10.1021/acsanm.3c00173] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
The current European (EU) policies, that is, the Green Deal, envisage safe and sustainable practices for chemicals, which include nanoforms (NFs), at the earliest stages of innovation. A theoretically safe and sustainable by design (SSbD) framework has been established from EU collaborative efforts toward the definition of quantitative criteria in each SSbD dimension, namely, the human and environmental safety dimension and the environmental, social, and economic sustainability dimensions. In this study, we target the safety dimension, and we demonstrate the journey toward quantitative intrinsic hazard criteria derived from findable, accessible, interoperable, and reusable data. Data were curated and merged for the development of new approach methodologies, that is, quantitative structure-activity relationship models based on regression and classification machine learning algorithms, with the intent to predict a hazard class. The models utilize system (i.e., hydrodynamic size and polydispersity index) and non-system (i.e., elemental composition and core size)-dependent nanoscale features in combination with biological in vitro attributes and experimental conditions for various silver NFs, functional antimicrobial textiles, and cosmetics applications. In a second step, interpretable rules (criteria) followed by a certainty factor were obtained by exploiting a Bayesian network structure crafted by expert reasoning. The probabilistic model shows a predictive capability of ≈78% (average accuracy across all hazard classes). In this work, we show how we shifted from the conceptualization of the SSbD framework toward the realistic implementation with pragmatic instances. This study reveals (i) quantitative intrinsic hazard criteria to be considered in the safety aspects during synthesis stage, (ii) the challenges within, and (iii) the future directions for the generation and distillation of such criteria that can feed SSbD paradigms. Specifically, the criteria can guide material engineers to synthesize NFs that are inherently safer from alternative nanoformulations, at the earliest stages of innovation, while the models enable a fast and cost-efficient in silico toxicological screening of previously synthesized and hypothetical scenarios of yet-to-be synthesized NFs.
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Affiliation(s)
- Irini Furxhi
- Transgero
Ltd, Limerick V42V384, Ireland
- Department
of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick V94T9PX, Ireland
| | - Rossella Bengalli
- Department
of Earth and Environmental Sciences, University
of Milano-Bicocca, Piazza
della Scienza 1, Milano 20126, Italy
| | - Giulia Motta
- Department
of Earth and Environmental Sciences, University
of Milano-Bicocca, Piazza
della Scienza 1, Milano 20126, Italy
| | - Paride Mantecca
- Department
of Earth and Environmental Sciences, University
of Milano-Bicocca, Piazza
della Scienza 1, Milano 20126, Italy
| | - Ozge Kose
- Univ.
Grenoble Alpes, CEA, CNRS, Grenoble INP, IRIG, SYMMES, Grenoble 38000, France
| | - Marie Carriere
- Univ.
Grenoble Alpes, CEA, CNRS, Grenoble INP, IRIG, SYMMES, Grenoble 38000, France
| | - Ehtsham Ul Haq
- Department
of Physics, and Bernal Institute, University
of Limerick, Limerick V94TC9PX, Ireland
| | - Charlie O’Mahony
- Department
of Physics, and Bernal Institute, University
of Limerick, Limerick V94TC9PX, Ireland
| | - Magda Blosi
- Istituto
di Scienza e Tecnologia dei Materiali Ceramici (CNR-ISTEC), Via Granarolo, 64, Faenza 48018, Ravenna, Italy
| | - Davide Gardini
- Istituto
di Scienza e Tecnologia dei Materiali Ceramici (CNR-ISTEC), Via Granarolo, 64, Faenza 48018, Ravenna, Italy
| | - Anna Costa
- Istituto
di Scienza e Tecnologia dei Materiali Ceramici (CNR-ISTEC), Via Granarolo, 64, Faenza 48018, Ravenna, Italy
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7
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Tan P, Chen X, Zhang H, Wei Q, Luo K. Artificial intelligence aids in development of nanomedicines for cancer management. Semin Cancer Biol 2023; 89:61-75. [PMID: 36682438 DOI: 10.1016/j.semcancer.2023.01.005] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/28/2022] [Accepted: 01/18/2023] [Indexed: 01/21/2023]
Abstract
Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.
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Affiliation(s)
- Ping Tan
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xiaoting Chen
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hu Zhang
- Amgen Bioprocessing Centre, Keck Graduate Institute, Claremont, CA 91711, USA
| | - Qiang Wei
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Kui Luo
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.
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8
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Zhang N, Xiong G, Liu Z. Toxicity of metal-based nanoparticles: Challenges in the nano era. Front Bioeng Biotechnol 2022; 10:1001572. [PMID: 36619393 PMCID: PMC9822575 DOI: 10.3389/fbioe.2022.1001572] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 10/25/2022] [Indexed: 11/12/2022] Open
Abstract
With the rapid progress of nanotechnology, various nanoparticles (NPs) have been applicated in our daily life. In the field of nanotechnology, metal-based NPs are an important component of engineered NPs, including metal and metal oxide NPs, with a variety of biomedical applications. However, the unique physicochemical properties of metal-based NPs confer not only promising biological effects but also pose unexpected toxic threats to human body at the same time. For safer application of metal-based NPs in humans, we should have a comprehensive understanding of NP toxicity. In this review, we summarize our current knowledge about metal-based NPs, including the physicochemical properties affecting their toxicity, mechanisms of their toxicity, their toxicological assessment, the potential strategies to mitigate their toxicity and current status of regulatory movement on their toxicity. Hopefully, in the near future, through the convergence of related disciplines, the development of nanotoxicity research will be significantly promoted, thereby making the application of metal-based NPs in humans much safer.
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Affiliation(s)
- Naiding Zhang
- Department of Vascular Surgery, 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Guiya Xiong
- Department of Science and Research, 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhenjie Liu
- Department of Vascular Surgery, 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China,*Correspondence: Zhenjie Liu,
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9
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Li J, Wang C, Yue L, Chen F, Cao X, Wang Z. Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 243:113955. [PMID: 35961199 DOI: 10.1016/j.ecoenv.2022.113955] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/11/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Given the rapid development of nanotechnology, it is crucial to understand the effects of nanoparticles on living organisms. However, it is laborious to perform toxicological tests on a case-by-case basis. Quantitative structure-activity relationship (QSAR) is an effective computational technique because it saves time, costs, and animal sacrifice. Therefore, this review presents general procedures for the construction and application of nano-QSAR models of metal-based and metal-oxide nanoparticles (MBNPs and MONPs). We also provide an overview of available databases and common algorithms. The molecular descriptors and their roles in the toxicological interpretation of MBNPs and MONPs are systematically reviewed and the future of nano-QSAR is discussed. Finally, we address the growing demand for novel nano-specific descriptors, new computational strategies to address the data shortage, in situ data for regulatory concerns, a better understanding of the physicochemical properties of NPs with bioactivity, and, most importantly, the design of nano-QSAR for real-life environmental predictions rather than laboratory simulations.
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Affiliation(s)
- Jing Li
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Chuanxi Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Le Yue
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Feiran Chen
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Xuesong Cao
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Zhenyu Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China.
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10
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Proposing an Adaptive Neuro-Fuzzy System-Based Swarm Concept Method for Predicting the Physical Properties of Nanofluids. INTERNATIONAL JOURNAL OF CHEMICAL ENGINEERING 2022. [DOI: 10.1155/2022/3345368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This paper employs dispersed nanoparticles (NPs) to build an adaptive neuro-fuzzy system (ANFIS) for predicting their thermal conductivity (TC) and viscosity according to the most important input data including concentration, size, the thickness of the interfacial layer, and intensive properties of NPs. In this regard, we gather an extensive and comprehensive data set from different sources. Here, the ANFIS model factors are optimized by using the particle swarm optimization (PSO) technique. Afterward, the obtained results are compared with previously published models which did a better job in predicting target values. In the following, to investigate the validity of our proposed model, statistical and graphical techniques are employed and it was proved that this model is efficient to evaluate the output values. Amounts of results obtained from the PSO-ANFIS model evaluation are 0.988 and 0.985 for the R2 and 0.0156 and 0.0876 for root mean squared error (RMSE) of TC ratio and viscosity ratio values, respectively, letting out a valid forecast of targets. Finally, by performing various statistical analyzes, it can be said that this model shows a high ability to predict target values and can be considered a good alternative to previous models.
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11
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Roy J, Roy K. Modeling and mechanistic understanding of cytotoxicity of metal oxide nanoparticles (MeOxNPs) to Escherichia coli: categorization and data gap filling for untested metal oxides. Nanotoxicology 2022; 16:152-164. [PMID: 35166631 DOI: 10.1080/17435390.2022.2038299] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Metal oxide nanoparticles (MeOxNPs) production is expected to increase every year exponentially, and their potential to cause adverse effect to the environment and human health will also expand rapidly. Hence, risk assessment of nanoparticles (NPs) is necessary to design ecosafe products. However, experimental ecotoxicological assessments are time-consuming requiring a lot of resources. Therefore, researchers rely on alternative in silico approaches to predict the behavior of NPs in the biological system. Quantitative structure - toxicity relationship (QSTR) has been adopted as a potential method to predict the cytotoxicity of untested NPs. Hence, in the present study, multiple linear regression (MLR) models were developed using 17 MeOxNPs on Escherichia coli (E. coli) bacteria cells under both light and dark conditions. The models were developed applying Small Dataset Modeler software, version 1.0.0 (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) which generates models with a limited number of data points. Periodic table-based descriptors (both 1st and 2nd generation) were used for the modeling purpose. Two statistically significant MLR models based on photo-induced toxicity (Q(LOO)2= 0.612, R2 = 0.726) and dark-based toxicity (Q(LOO)2= 0.627, R2 = 0.770) were developed. From the developed models, we interpreted that increase in valency and oxidation state of the metal will decrease the cytotoxicity whereas the atomic radius of the metal and electronegativity of MeOxNPs influence the toxicity toward E. coli cells. The MLR models were validated using different internal validation metrics. Additionally, we have collected 42 MeOxNPs as an external set to observe the predictive power of the two developed MLR models and categorize them into toxic and non-toxic classes. The chemical features selected in the developed models are important for understanding the mechanisms of nanotoxicity. Thus, the developed models can be a scientific basis for designing safer NPs.
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Affiliation(s)
- Joyita Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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12
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Kamaly N, Farokhzad OC, Corbo C. Nanoparticle protein corona evolution: from biological impact to biomarker discovery. NANOSCALE 2022; 14:1606-1620. [PMID: 35076049 DOI: 10.1039/d1nr06580g] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Nanoparticles exposed to biological fluids such as blood, quickly interact with their surrounding milieu resulting in a biological coating that results in large part as a function of the physicochemical properties of the nanomaterial. The large nanoparticle surface area-to-volume ratio further augments binding of biological molecules and the resulting biomolecular or protein corona, once thought of as problematic biofouling, is now viewed as a rich source of biological information that can guide the development of nanomedicines. This review gives an overview of the utility of the protein corona in proteomic profiling and discusses how a better understanding of nano-bio interactions can accelerate the clinical translation of nanomedicines and facilitate the identification of disease-specific biomarkers. With the FDA requirement of the protein corona analysis of nanoparticles in place, it is envisaged that analyzing the protein corona of nanoparticles on a case-by-case basis can provide highly valuable nano-bio interface information that can aid and improve their clinical translation.
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Affiliation(s)
- Nazila Kamaly
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, W12 0BZ London, UK.
| | - Omid C Farokhzad
- Center for Nanomedicine and Department of Anaesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, 02115, USA.
| | - Claudia Corbo
- Department of Medicine and Surgery, Center for Nanomedicine NANOMIB, University of Milan Bicocca, Milan, Italy.
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
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13
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Subedi P, Parajuli S, Alpuche-Aviles MA. Single Entity Behavior of CdSe Quantum Dot Aggregates During Photoelectrochemical Detection. Front Chem 2021; 9:733642. [PMID: 34568283 PMCID: PMC8461012 DOI: 10.3389/fchem.2021.733642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/26/2021] [Indexed: 11/13/2022] Open
Abstract
We demonstrate that colloidal quantum dots of CdSe and CdSe/ZnS are detected during the photooxidation of MeOH, under broad spectrum illumination (250 mW/cm2). The stepwise photocurrent vs. time response corresponds to single entities adsorbing to the Pt electrode surface irreversibly. The adsorption/desorption of the QDs and the nature of the single entities is discussed. In suspensions, the QDs behave differently depending on the solvent used to suspend the materials. For MeOH, CdSe is not as stable as CdSe/ZnS under constant illumination. The photocurrent expected for single QDs is discussed. The value of the observed photocurrents, > 1 pA is due to the formation of agglomerates consistent with the collision frequency and suspension stability. The observed frequency of collisions for the stepwise photocurrents is smaller than the diffusion-limited cases expected for single QDs colliding with the electrode surface. Dynamic light scattering and scanning electron microscopy studies support the detection of aggregates. The results indicate that the ZnS layer on the CdSe/ZnS material facilitates the detection of single entities by increasing the stability of the nanomaterial. The rate of hole transfer from the QD aggregates to MeOH outcompetes the dissolution of the CdSe core under certain conditions of electron injection to the Pt electrode and in colloidal suspensions of CdSe/ZnS.
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Affiliation(s)
- Pradeep Subedi
- Department of Chemistry, University of Nevada, Reno, NV, United States
| | - Suman Parajuli
- Department of Chemistry, University of Nevada, Reno, NV, United States
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14
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Nanosafety vs. nanotoxicology: adequate animal models for testing in vivo toxicity of nanoparticles. Toxicology 2021; 462:152952. [PMID: 34543703 DOI: 10.1016/j.tox.2021.152952] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/10/2021] [Accepted: 09/11/2021] [Indexed: 11/20/2022]
Abstract
Nanotoxicological studies using existing models of normal cells and animals often encounter a paradox: retention of nanoparticles in intracellular compartments for a long time is not accompanied by any significant toxicological effects. Can we expect that the revealed changes will be not harmful after translation to practice, outside of a sterile laboratory and ideally healthy organisms? Age-associated and pathological processes can affect target organs, metabolism, and detoxification in the mononuclear phagocyte system organs and change biodistribution routes, thus making the use of nanomaterial not safe. The potential solution to this issue can be testing the toxic properties of nanoparticles in animal models with chronic diseases. However, current studies of nanotoxicity in animal models with a brain, cardiovascular system, liver, digestive tract, reproductive system, and skin diseases are unsystematic. Even though these studies demonstrate the emergence of new toxic effects that are not present in healthy animals. In this regard, we set the goal of this review as the formulation of the requirements for an animal model capable of assessing the potential toxicity of nanoparticles based on the nanosafety approach.
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15
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Gomes SIL, Amorim MJB, Pokhrel S, Mädler L, Fasano M, Chiavazzo E, Asinari P, Jänes J, Tämm K, Burk J, Scott-Fordsmand JJ. Machine learning and materials modelling interpretation of in vivo toxicological response to TiO 2 nanoparticles library (UV and non-UV exposure). NANOSCALE 2021; 13:14666-14678. [PMID: 34533558 DOI: 10.1039/d1nr03231c] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Assessing the risks of nanomaterials/nanoparticles (NMs/NPs) under various environmental conditions requires a more systematic approach, including the comparison of effects across many NMs with identified different but related characters/descriptors. Hence, there is an urgent need to provide coherent (eco)toxicological datasets containing comprehensive toxicity information relating to a diverse spectra of NPs characters. These datasets are test benches for developing holistic methodologies with broader applicability. In the present study we assessed the effects of a custom design Fe-doped TiO2 NPs library, using the soil invertebrate Enchytraeus crypticus (Oligochaeta), via a 5-day pulse via aqueous exposure followed by a 21-days recovery period in soil (survival, reproduction assessment). Obviously, when testing TiO2, realistic conditions should include UV exposure. The 11 Fe-TiO2 library contains NPs of size range between 5-27 nm with varying %Fe (enabling the photoactivation of TiO2 at energy wavelengths in the visible-light range). The NPs were each described by 122 descriptors, being a mixture of measured and atomistic model descriptors. The data were explored using single and univariate statistical methods, combined with machine learning and multiscale modelling techniques. An iterative pruning process was adopted for identifying automatically the most significant descriptors. TiO2 NPs toxicity decreased when combined with UV. Notably, the short-term water exposure induced lasting biological responses even after longer-term recovery in clean exposure. The correspondence with Fe-content correlated with the band-gap hence the reduction of UV oxidative stress. The inclusion of both measured and modelled materials data benefitted the explanation of the results, when combined with machine learning.
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Affiliation(s)
- Susana I L Gomes
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
| | - Mónica J B Amorim
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
| | - Suman Pokhrel
- Department of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany
- Leibniz Institute for Materials Engineering IWT, Badgasteiner Str. 3, 28359 Bremen, Germany
| | - Lutz Mädler
- Department of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany
- Leibniz Institute for Materials Engineering IWT, Badgasteiner Str. 3, 28359 Bremen, Germany
| | - Matteo Fasano
- Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy
| | - Eliodoro Chiavazzo
- Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy
| | - Pietro Asinari
- Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy
- INRIM, Istituto Nazionale di Ricerca Metrologica, Strada delle Cacce 91, Torino 10135, Italy
| | - Jaak Jänes
- Department of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia
| | - Kaido Tämm
- Department of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia
| | - Jaanus Burk
- Department of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia
| | - Janeck J Scott-Fordsmand
- Department of Bioscience, Aarhus University, Vejlsovej 25, PO BOX 314, DK-8600 Silkeborg, Denmark
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16
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Kianfar E. Protein nanoparticles in drug delivery: animal protein, plant proteins and protein cages, albumin nanoparticles. J Nanobiotechnology 2021; 19:159. [PMID: 34051806 PMCID: PMC8164776 DOI: 10.1186/s12951-021-00896-3] [Citation(s) in RCA: 123] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 05/12/2021] [Indexed: 12/19/2022] Open
Abstract
In this article, we will describe the properties of albumin and its biological functions, types of sources that can be used to produce albumin nanoparticles, methods of producing albumin nanoparticles, its therapeutic applications and the importance of albumin nanoparticles in the production of pharmaceutical formulations. In view of the increasing use of Abraxane and its approval for use in the treatment of several types of cancer and during the final stages of clinical trials for other cancers, to evaluate it and compare its effectiveness with conventional non formulations of chemotherapy Paclitaxel is paid. In this article, we will examine the role and importance of animal proteins in Nano medicine and the various benefits of these biomolecules for the preparation of drug delivery carriers and the characteristics of plant protein Nano carriers and protein Nano cages and their potentials in diagnosis and treatment. Finally, the advantages and disadvantages of protein nanoparticles are mentioned, as well as the methods of production of albumin nanoparticles, its therapeutic applications and the importance of albumin nanoparticles in the production of pharmaceutical formulations.
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Affiliation(s)
- Ehsan Kianfar
- ERNAM-Erciyes University Nanotechnology Application and Research Center, Kayseri, 38039, Turkey.
- Department of Analytical Chemistry, Faculty of Pharmacy, Erciyes University, Kayseri, 38039, Turkey.
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17
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Soltani M, Moradi Kashkooli F, Souri M, Zare Harofte S, Harati T, Khadem A, Haeri Pour M, Raahemifar K. Enhancing Clinical Translation of Cancer Using Nanoinformatics. Cancers (Basel) 2021; 13:2481. [PMID: 34069606 PMCID: PMC8161319 DOI: 10.3390/cancers13102481] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 05/08/2021] [Accepted: 05/16/2021] [Indexed: 12/14/2022] Open
Abstract
Application of drugs in high doses has been required due to the limitations of no specificity, short circulation half-lives, as well as low bioavailability and solubility. Higher toxicity is the result of high dosage administration of drug molecules that increase the side effects of the drugs. Recently, nanomedicine, that is the utilization of nanotechnology in healthcare with clinical applications, has made many advancements in the areas of cancer diagnosis and therapy. To overcome the challenge of patient-specificity as well as time- and dose-dependency of drug administration, artificial intelligence (AI) can be significantly beneficial for optimization of nanomedicine and combinatorial nanotherapy. AI has become a tool for researchers to manage complicated and big data, ranging from achieving complementary results to routine statistical analyses. AI enhances the prediction precision of treatment impact in cancer patients and specify estimation outcomes. Application of AI in nanotechnology leads to a new field of study, i.e., nanoinformatics. Besides, AI can be coupled with nanorobots, as an emerging technology, to develop targeted drug delivery systems. Furthermore, by the advancements in the nanomedicine field, AI-based combination therapy can facilitate the understanding of diagnosis and therapy of the cancer patients. The main objectives of this review are to discuss the current developments, possibilities, and future visions in naoinformatics, for providing more effective treatment for cancer patients.
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Affiliation(s)
- Madjid Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
- Faculty of Science, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
- Advanced Bioengineering Initiative Center, Multidisciplinary International Complex, K. N. Toosi Univesity of Technology, Tehran 14176-14411, Iran
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Farshad Moradi Kashkooli
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Mohammad Souri
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Samaneh Zare Harofte
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Tina Harati
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Atefeh Khadem
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Mohammad Haeri Pour
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Kaamran Raahemifar
- Faculty of Science, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology (IST), State College, Penn State University, Pennsylvania, PA 16801, USA
- Department of Chemical Engineering, Faculty of Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada
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18
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Gousiadou C, Marchese Robinson RL, Kotzabasaki M, Doganis P, Wilkins TA, Jia X, Sarimveis H, Harper SL. Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish. Nanotoxicology 2021; 15:446-476. [PMID: 33586589 DOI: 10.1080/17435390.2021.1872113] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The possibility of employing computational approaches like nano-QSAR or nano-read-across to predict nanomaterial hazard is attractive from both a financial, and most importantly, where in vivo tests are required, ethical perspective. In the present work, we have employed advanced Machine Learning techniques, including stacked model ensembles, to create nano-QSAR tools for modeling the toxicity of metallic and metal oxide nanomaterials, both coated and uncoated and with a variety of different core compositions, tested at different dosage concentrations on embryonic zebrafish. Using both computed and experimental descriptors, we have identified a set of properties most relevant for the assessment of nanomaterial toxicity and successfully correlated these properties with the associated biological responses observed in zebrafish. Our findings suggest that for the group of metal and metal oxide nanomaterials, the core chemical composition, concentration and properties dependent upon nanomaterial surface and medium composition (such as zeta potential and agglomerate size) are significant factors influencing toxicity, albeit the ranking of different variables is sensitive to the exact analysis method and data modeled. Our generalized nano-QSAR ensemble models provide a promising framework for anticipating the toxicity potential of new nanomaterials and may contribute to the transition out of the animal testing paradigm. However, future experimental studies are required to generate comparable, similarly high quality data, using consistent protocols, for well characterized nanomaterials, as per the dataset modeled herein. This would enable the predictive power of our promising ensemble modeling approaches to be robustly assessed on large, diverse and truly external datasets.
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Affiliation(s)
- C Gousiadou
- School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - R L Marchese Robinson
- School of Chemical and Process Engineering, University of Leeds, Leeds, United Kingdom
| | - M Kotzabasaki
- School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - P Doganis
- School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - T A Wilkins
- School of Chemical and Process Engineering, University of Leeds, Leeds, United Kingdom
| | - X Jia
- School of Chemical and Process Engineering, University of Leeds, Leeds, United Kingdom
| | - H Sarimveis
- School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - S L Harper
- School of Chemical, Biological and Environmental Engineering, Oregon State University, Corvallis, OR, USA.,Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR, USA.,Safer Nanomaterials and Nanomanufacturing Initiative, Oregon Nanoscience and Microtechnologies Institute, Eugene, OR, USA
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19
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Rybińska-Fryca A, Mikolajczyk A, Puzyn T. Structure-activity prediction networks (SAPNets): a step beyond Nano-QSAR for effective implementation of the safe-by-design concept. NANOSCALE 2020; 12:20669-20676. [PMID: 33048104 DOI: 10.1039/d0nr05220e] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
A significant number of experimental studies are supported by computational methods such as quantitative structure-activity relationship modeling of nanoparticles (Nano-QSAR). This is especially so in research focused on design and synthesis of new, safer nanomaterials using safe-by-design concepts. However, Nano-QSAR has a number of important limitations. For example, it is not clear which descriptors that describe the nanoparticle physicochemical and structural properties are essential and can be adjusted to alter the target properties. This limitation can be overcome with the use of the Structure-Activity Prediction Network (SAPNet) presented in this paper. There are three main phases of building the SAPNet. First, information about the structural characterization of a nanomaterial, its physical and chemical properties and toxicity is compiled. Then, the most relevant properties (intrinsic/extrinsic) likely to influence the ENM toxicity are identified by developing "meta-models". Finally, these "meta-models" describing the dependencies between the most relevant properties of the ENMs and their adverse biological properties are developed. In this way, the network is built layer by layer from the endpoint (e.g. toxicity or other properties of interest) to descriptors that describe the particle structure. Therefore, SAPNets go beyond the current standards and provide sufficient information on what structural features should be altered to obtain a material with desired properties.
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Affiliation(s)
| | - Alicja Mikolajczyk
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland. and University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland. and University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
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20
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Papadiamantis AG, Jänes J, Voyiatzis E, Sikk L, Burk J, Burk P, Tsoumanis A, Ha MK, Yoon TH, Valsami-Jones E, Lynch I, Melagraki G, Tämm K, Afantitis A. Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E2017. [PMID: 33066094 PMCID: PMC7601995 DOI: 10.3390/nano10102017] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 10/03/2020] [Accepted: 10/07/2020] [Indexed: 02/07/2023]
Abstract
A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA).
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Affiliation(s)
- Anastasios G. Papadiamantis
- NovaMechanics Ltd., Nicosia 1065, Cyprus; (A.G.P.); (E.V.); (A.T.)
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK; (E.V.-J.); (I.L.)
| | - Jaak Jänes
- Institute of Chemistry, University of Tartu, 50411 Tartu, Estonia; (J.J.); (L.S.); (J.B.); (P.B.)
| | | | - Lauri Sikk
- Institute of Chemistry, University of Tartu, 50411 Tartu, Estonia; (J.J.); (L.S.); (J.B.); (P.B.)
| | - Jaanus Burk
- Institute of Chemistry, University of Tartu, 50411 Tartu, Estonia; (J.J.); (L.S.); (J.B.); (P.B.)
| | - Peeter Burk
- Institute of Chemistry, University of Tartu, 50411 Tartu, Estonia; (J.J.); (L.S.); (J.B.); (P.B.)
| | | | - My Kieu Ha
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea; (M.K.H.); (T.H.Y.)
| | - Tae Hyun Yoon
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea; (M.K.H.); (T.H.Y.)
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK; (E.V.-J.); (I.L.)
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK; (E.V.-J.); (I.L.)
| | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, 16672 Vari, Greece;
| | - Kaido Tämm
- Institute of Chemistry, University of Tartu, 50411 Tartu, Estonia; (J.J.); (L.S.); (J.B.); (P.B.)
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21
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Xiarchos I, Morozinis AK, Kavouras P, Charitidis CA. Nanocharacterization, Materials Modeling, and Research Integrity as Enablers of Sound Risk Assessment: Designing Responsible Nanotechnology. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e2001590. [PMID: 32656997 DOI: 10.1002/smll.202001590] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 06/03/2020] [Indexed: 06/11/2023]
Abstract
Nanotechnology, as a mature enabling technology, has great potential to boost societal welfare. However, nanomaterials' current and foreseen applications raise serious concerns about their impact on human health and the environment. These concerns emerge because a reliable risk assessment in nanotechnology is yet to be achieved. The reasons for such a shortcoming are the inherent difficulties in characterizing nanomaterials properties. The interaction of characterization with modeling is an open issue and, due to overarching concerns about the reliability of research results, usually framed within the context of research integrity. This essay explores the connection between these different, but deeply intertwined concerns and the way they enable the production of responsible nanotechnology, i.e., nanotechnology devoted to societal welfare.
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Affiliation(s)
- Ioannis Xiarchos
- Research Lab of Advanced, Composite, Nanomaterials, and Nanotechnology (R-NanoLab), School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechniou str., Zographos, Athens, 15780, Greece
| | - Athanasios K Morozinis
- Research Lab of Advanced, Composite, Nanomaterials, and Nanotechnology (R-NanoLab), School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechniou str., Zographos, Athens, 15780, Greece
| | - Panagiotis Kavouras
- Research Lab of Advanced, Composite, Nanomaterials, and Nanotechnology (R-NanoLab), School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechniou str., Zographos, Athens, 15780, Greece
| | - Costas A Charitidis
- Research Lab of Advanced, Composite, Nanomaterials, and Nanotechnology (R-NanoLab), School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechniou str., Zographos, Athens, 15780, Greece
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Winkler DA. Role of Artificial Intelligence and Machine Learning in Nanosafety. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e2001883. [PMID: 32537842 DOI: 10.1002/smll.202001883] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 05/07/2020] [Indexed: 06/11/2023]
Abstract
Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety.
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Affiliation(s)
- David A Winkler
- La Trobe Institute for Molecular Science, La Trobe University, Kingsbury Drive, Bundoora, 3042, Australia
- CSIRO Data61, 1 Technology Court, Pullenvale, 4069, Australia
- School of Pharmacy, University of Nottingham, Nottingham, NG7 2QL, UK
- Monash Institute of Pharmaceutical Sciences, Monash University, 392 Royal Parade, Parkville, 3052, Australia
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Klaper RD. The Known and Unknown about the Environmental Safety of Nanomaterials in Commerce. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e2000690. [PMID: 32407002 DOI: 10.1002/smll.202000690] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 03/17/2020] [Accepted: 03/19/2020] [Indexed: 06/11/2023]
Abstract
The widespread nanomaterial use in commercial products has fed significant concern over environmental health and safety ramifications. Initially, little was known as to how these highly reactive particulates interacted with biological systems. Nanomaterials have introduced complexities not normally considered in traditional safety assessments of chemicals and therefore have generated uncertainty in the reliability of standard tests of safety. Advances in understanding the potential impacts of nanomaterials have occurred since their introduction, particularly for those used in the greatest quantities in commerce. The impact of characteristics such as charge, size, surface functionalization, chemical composition, and certain transformations on the potential effect of nanomaterials in the environment continue to move the field forward. However, generalizations of risk based on any one factor across nanomaterials is not possible. Estimating risk also remains difficult due to the introduction of materials that are new and more complex, minimal information on the specific molecular interactions of nanomaterials and organisms, and the need for more tools for measuring the dynamics of nanomaterial state and fate in complex matrices. Finally, exposure estimates are difficult due to difficulty of environmental monitoring which may be exacerbated by lack of information on nanomaterials in products and new uses in the marketplace.
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Affiliation(s)
- Rebecca D Klaper
- School of Freshwater Sciences, University of Wisconsin-Milwaukee, 600 East Greenfield Ave., Milwaukee, Wisconsin, 53204, USA
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Xu S, Zheng H, Ma R, Wu D, Pan Y, Yin C, Gao M, Wang W, Li W, Liu S, Chai Z, Li R. Vacancies on 2D transition metal dichalcogenides elicit ferroptotic cell death. Nat Commun 2020; 11:3484. [PMID: 32661253 PMCID: PMC7359333 DOI: 10.1038/s41467-020-17300-7] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Accepted: 06/23/2020] [Indexed: 01/30/2023] Open
Abstract
Sustainable developments of nanotechnology necessitate the exploration of structure-activity relationships (SARs) at nano-bio interfaces. While ferroptosis may contribute in the developments of some severe diseases (e.g., Parkinson's disease, stroke and tumors), the cellular pathways and nano-SARs are rarely explored in diseases elicited by nano-sized ferroptosis inducers. Here we find that WS2 and MoS2 nanosheets induce an iron-dependent cell death, ferroptosis in epithelial (BEAS-2B) and macrophage (THP-1) cells, evidenced by the suppression of glutathione peroxidase 4 (GPX4), oxygen radical generation and lipid peroxidation. Notably, nano-SAR analysis of 20 transition metal dichalcogenides (TMDs) disclosures the decisive role of surface vacancy in ferroptosis. We therefore develop methanol and sulfide passivation as safe design approaches for TMD nanosheets. These findings are validated in animal lungs by oropharyngeal aspiration of TMD nanosheets. Overall, our study highlights the key cellular events as well as nano-SARs in TMD-induced ferroptosis, which may facilitate the safe design of nanoproducts.
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Affiliation(s)
- Shujuan Xu
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, Jiangsu, China
| | - Huizhen Zheng
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, Jiangsu, China
| | - Ronglin Ma
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, Jiangsu, China
| | - Di Wu
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, Jiangsu, China
| | - Yanxia Pan
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, Jiangsu, China
| | - Chunyang Yin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Beijing, 100085, China
| | - Meng Gao
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, Jiangsu, China
| | - Weili Wang
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, Jiangsu, China
| | - Wei Li
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, Jiangsu, China
| | - Sijin Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Beijing, 100085, China
| | - Zhifang Chai
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, Jiangsu, China
| | - Ruibin Li
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, Jiangsu, China.
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Ovais M, Nethi SK, Ullah S, Ahmad I, Mukherjee S, Chen C. Recent advances in the analysis of nanoparticle-protein coronas. Nanomedicine (Lond) 2020; 15:1037-1061. [DOI: 10.2217/nnm-2019-0381] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
In spite of radical advances in nanobiotechnology, the clinical translation of nanoparticle (NP)-based agents is still a major challenge due to various physiological factors that influence their interactions with biological systems. Recent decade witnessed meticulous investigation on protein corona (PC) that is the first surrounds NPs once administered into the body. Formation of PC around NP surface exhibits resilient effects on their circulation, distribution, therapeutic activity, toxicity and other factors. Although enormous literature is available on the role of PC in altering pharmacokinetics and pharmacodynamics of NPs, understanding on its analytical characterization methods still remains shallow. Therefore, the current review summarizes the impact of PC on biological fate of NPs and stressing on analytical methods employed for studying the NP-PC.
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Affiliation(s)
- Muhammad Ovais
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience & Technology (NCNST), Beijing, 100190, PR China
- University of Chinese Academy of Sciences, Beijing, 100049, PR China
| | - Susheel Kumar Nethi
- Department of Experimental & Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455, USA
| | - Saleem Ullah
- Department of Environmental Science & Technology, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8503, Japan
| | - Irshad Ahmad
- Department of Life Sciences, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, 31261, Saudi Arabia
| | - Sudip Mukherjee
- Department of Bioengineering, Rice University, Houston, TX 77030, USA
| | - Chunying Chen
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience & Technology (NCNST), Beijing, 100190, PR China
- University of Chinese Academy of Sciences, Beijing, 100049, PR China
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Bio-functionalized CuO nanoparticles induced apoptotic activities in human breast carcinoma cells and toxicity against Aspergillus flavus: An in vitro approach. Process Biochem 2020. [DOI: 10.1016/j.procbio.2020.01.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Kotzabasaki MI, Sotiropoulos I, Sarimveis H. QSAR modeling of the toxicity classification of superparamagnetic iron oxide nanoparticles (SPIONs) in stem-cell monitoring applications: an integrated study from data curation to model development. RSC Adv 2020; 10:5385-5391. [PMID: 35498319 PMCID: PMC9049038 DOI: 10.1039/c9ra09475j] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 01/21/2020] [Indexed: 11/23/2022] Open
Abstract
The use of in silico approaches for the prediction of biomedical properties of nano-biomaterials (NBMs) can play a significant role in guiding and reducing wetlab experiments. Computational methods, such as data mining and machine learning techniques, can increase the efficiency and reduce the time and cost required for hazard and risk assesment and for designing new safer NBMs. A major obstacle in developing accurate and well-validated in silico models such as Nano Quantitative Structure-Activity Relationships (Nano-QSARs) is that although the volume of data published in the literature is increasing, the data are fragmented in many different publications and are not sufficiently curated for modelling purposes. Moreover, NBMs exhibit high complexity and heterogeneity in their structures, making data collection and curation and QSAR model development more challenging compared to traditional small molecules. The aim of this study was to construct and fully validate a Nano-QSAR model for the prediction of toxicological properties of superparamagnetic iron oxide nanoparticles (SPIONs), focusing on their application as Magnetic Resonance Imaging (MRI) contrast agents for non-invasive stem cell labelling and tracking. To achieve this goal, we first performed an extensive search through the literature for collecting and curating relevant data and we developed a dataset containing both physicochemical and toxicological properties of SPIONs. The data were analysed next, using Automated machine learning (Auto-ML) approaches for optimising the development and validation of nanotoxicity classification QSAR models of SPIONs. Further analysis of relative attribute importances revealed that physicochemical properties such as the size and the magnetic core are the dominant attributes correlated to the toxicity of SPIONs. Our results suggest that as more systematic information from NBM experimental tests becomes available, computational tools could play an important role in supporting the safety-by-design (SbD) concept in regenerative medicine and disease therapeutics.
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Affiliation(s)
- Marianna I Kotzabasaki
- School of Chemical Engineering, National Technical University of Athens 9 Heroon Polytechneiou Street, Zografou Campus 15780 Athens Greece +302107723138 +302107723236 +306936396688 +302107723237
| | - Iason Sotiropoulos
- School of Chemical Engineering, National Technical University of Athens 9 Heroon Polytechneiou Street, Zografou Campus 15780 Athens Greece +302107723138 +302107723236 +306936396688 +302107723237
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens 9 Heroon Polytechneiou Street, Zografou Campus 15780 Athens Greece +302107723138 +302107723236 +306936396688 +302107723237
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Fjodorova N, Novič M, Venko K, Rasulev B. A Comprehensive Cheminformatics Analysis of Structural Features Affecting the Binding Activity of Fullerene Derivatives. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E90. [PMID: 31906497 PMCID: PMC7023229 DOI: 10.3390/nano10010090] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 12/24/2019] [Accepted: 12/27/2019] [Indexed: 01/08/2023]
Abstract
Nanostructures like fullerene derivatives (FDs) belong to a new family of nano-sized organic compounds. Fullerenes have found a widespread application in material science, pharmaceutical, biomedical, and medical fields. This fact caused the importance of the study of pharmacological as well as toxicological properties of this relatively new family of chemicals. In this work, a large set of 169 FDs and their binding activity to 1117 proteins was investigated. The structure-based descriptors widely used in drug design (so-called drug-like descriptors) were applied to understand cheminformatics characteristics related to the binding activity of fullerene nanostructures. Investigation of applied descriptors demonstrated that polarizability, topological diameter, and rotatable bonds play the most significant role in the binding activity of FDs. Various cheminformatics methods, including the counter propagation artificial neural network (CPANN) and Kohonen network as visualization tool, were applied. The results of this study can be applied to compose the priority list for testing in risk assessment related to the toxicological properties of FDs. The pharmacologist can filter the data from the heat map to view all possible side effects for selected FDs.
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Affiliation(s)
- Natalja Fjodorova
- National Institute of Chemistry, SI-1000 Ljubljana, Slovenia; (M.N.); (K.V.)
| | - Marjana Novič
- National Institute of Chemistry, SI-1000 Ljubljana, Slovenia; (M.N.); (K.V.)
| | - Katja Venko
- National Institute of Chemistry, SI-1000 Ljubljana, Slovenia; (M.N.); (K.V.)
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA;
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29
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Affiliation(s)
- Hagit Sason
- Faculty of Biomedical Engineering Technion – Israel Institute of Technology Haifa Israel
| | - Yosi Shamay
- Faculty of Biomedical Engineering Technion – Israel Institute of Technology Haifa Israel
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30
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Abstract
Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health.
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Affiliation(s)
- Hao Zhu
- Department of Chemistry and Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey 08102, USA;
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31
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Green Micro- and Nanoemulsions for Managing Parasites, Vectors and Pests. NANOMATERIALS 2019; 9:nano9091285. [PMID: 31505756 PMCID: PMC6781030 DOI: 10.3390/nano9091285] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 08/02/2019] [Accepted: 08/12/2019] [Indexed: 11/17/2022]
Abstract
The management of parasites, insect pests and vectors requests development of novel, effective and eco-friendly tools. The development of resistance towards many drugs and pesticides pushed scientists to look for novel bioactive compounds endowed with multiple modes of action, and with no risk to human health and environment. Several natural products are used as alternative/complementary approaches to manage parasites, insect pests and vectors due to their high efficacy and often limited non-target toxicity. Their encapsulation into nanosystems helps overcome some hurdles related to their physicochemical properties, for instance limited stability and handling, enhancing the overall efficacy. Among different nanosystems, micro- and nanoemulsions are easy-to-use systems in terms of preparation and industrial scale-up. Different reports support their efficacy against parasites of medical importance, including Leishmania, Plasmodium and Trypanosoma as well as agricultural and stored product insect pests and vectors of human diseases, such as Aedes and Culex mosquitoes. Overall, micro- and nanoemulsions are valid options for developing promising eco-friendly tools in pest and vector management, pending proper field validation. Future research on the improvement of technical aspects as well as chronic toxicity experiments on non-target species is needed.
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Acharya K, Werner D, Dolfing J, Barycki M, Meynet P, Mrozik W, Komolafe O, Puzyn T, Davenport RJ. A quantitative structure-biodegradation relationship (QSBR) approach to predict biodegradation rates of aromatic chemicals. WATER RESEARCH 2019; 157:181-190. [PMID: 30953853 DOI: 10.1016/j.watres.2019.03.086] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 03/21/2019] [Accepted: 03/27/2019] [Indexed: 06/09/2023]
Abstract
The objective of this work was to develop a QSBR model for the prioritization of organic pollutants based on biodegradation rates from a database containing globally harmonized biodegradation tests using relevant molecular descriptors. To do this, we first categorized the chemicals into three groups (Group 1: simple aromatic chemicals with a single ring, Group 2: aromatic chemicals with multiple rings and Group3: Group 1 plus Group 2) based on molecular descriptors, estimated the first order biodegradation rate of the chemicals using rating values derived from the BIOWIN3 model, and finally developed, validated and defined the applicability domain of models for each group using a multiple linear regression approach. All the developed QSBR models complied with OECD principles for QSAR validation. The biodegradation rate in the models for the two groups (Group 2 and 3 chemicals) are associated with abstract molecular descriptors that provide little relevant practical information towards understanding the relationship between chemical structure and biodegradation rates. However, molecular descriptors associated with the QSBR model for Group 1 chemicals (R2 = 0.89, Q2loo = 0.87) provided information on properties that can readily be scrutinised and interpreted in relation to biodegradation processes. In combination, these results lead to the conclusion that QSBRs can be an alternative tool to estimate the persistence of chemicals, some of which can provide further insights into those factors affecting biodegradation.
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Affiliation(s)
- Kishor Acharya
- School of Engineering, Cassie Building, Newcastle University, Newcastle Upon Tyne, NE1 7RU, United Kingdom.
| | - David Werner
- School of Engineering, Cassie Building, Newcastle University, Newcastle Upon Tyne, NE1 7RU, United Kingdom
| | - Jan Dolfing
- School of Engineering, Cassie Building, Newcastle University, Newcastle Upon Tyne, NE1 7RU, United Kingdom
| | - Maciej Barycki
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308, Gdańsk, Poland
| | - Paola Meynet
- School of Engineering, Cassie Building, Newcastle University, Newcastle Upon Tyne, NE1 7RU, United Kingdom
| | - Wojciech Mrozik
- School of Engineering, Cassie Building, Newcastle University, Newcastle Upon Tyne, NE1 7RU, United Kingdom
| | - Oladapo Komolafe
- School of Engineering, Cassie Building, Newcastle University, Newcastle Upon Tyne, NE1 7RU, United Kingdom
| | - Tomasz Puzyn
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308, Gdańsk, Poland
| | - Russell J Davenport
- School of Engineering, Cassie Building, Newcastle University, Newcastle Upon Tyne, NE1 7RU, United Kingdom
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Yan X, Sedykh A, Wang W, Zhao X, Yan B, Zhu H. In silico profiling nanoparticles: predictive nanomodeling using universal nanodescriptors and various machine learning approaches. NANOSCALE 2019; 11:8352-8362. [PMID: 30984943 DOI: 10.1039/c9nr00844f] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Rational nanomaterial design is urgently demanded for new nanomaterial development with desired properties. However, computational nanomaterial modeling and virtual nanomaterial screening are not applicable for this purpose due to the complexity of nanomaterial structures. To address this challenge, a new computational workflow is established in this study to virtually profile nanoparticles by (1) constructing a structurally diverse virtual gold nanoparticle (GNP) library and (2) developing novel universal nanodescriptors. The emphasis of this study is the second task by developing geometrical nanodescriptors that are suitable for the quantitative modeling of GNPs and virtual screening purposes. The feasibility, rigor and applicability of this novel computational method are validated by testing seven GNP datasets consisting of 191 unique GNPs of various nano-bioactivities and physicochemical properties. The high predictability of the developed GNP models suggests that this workflow can be used as a universal tool for nanomaterial profiling and rational nanomaterial design.
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Affiliation(s)
- Xiliang Yan
- School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100, China
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Utembe W, Wepener V, Yu IJ, Gulumian M. An assessment of applicability of existing approaches to predicting the bioaccumulation of conventional substances in nanomaterials. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2018; 37:2972-2988. [PMID: 30117187 DOI: 10.1002/etc.4253] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 01/24/2018] [Accepted: 08/11/2018] [Indexed: 06/08/2023]
Abstract
The experimental determination of bioaccumulation is challenging, and a number of approaches have been developed for its prediction. It is important to assess the applicability of these predictive approaches to nanomaterials (NMs), which have been shown to bioaccumulate. The octanol/water partition coefficient (KOW ) may not be applicable to some NMs that are not found in either the octanol or water phases but rather are found at the interface. Thus the KOW values obtained for certain NMs are shown not to correlate well with the experimentally determined bioaccumulation. Implementation of quantitative structure-activity relationships (QSARs) for NMs is also challenging because the bioaccumulation of NMs depends on nano-specific properties such as shape, size, and surface area. Thus there is a need to develop new QSAR models based on these new nanodescriptors; current efforts appear to focus on digital processing of NM images as well as the conversion of surface chemistry parameters into adsorption indices. Water solubility can be used as a screening tool for the exclusion of NMs with short half-lives. Adaptation of fugacity/aquivalence models, which include physicochemical properties, may give some insights into the bioaccumulation potential of NMs, especially with the addition of a biota component. The use of kinetic models, including physiologically based pharmacokinetic models, appears to be the most suitable approach for predicting bioaccumulation of NMs. Furthermore, because bioaccumulation of NMs depends on a number of biotic and abiotic factors, it is important to take these factors into account when one is modeling bioaccumulation and interpreting bioaccumulation results. Environ Toxicol Chem 2018;37:2972-2988. © 2018 SETAC.
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Affiliation(s)
- Wells Utembe
- National Institute for Occupational Health, Johannesburg, South Africa
| | - Victor Wepener
- Unit for Environmental Sciences and Management, North West University, Potchefstroom, South Africa
| | | | - Mary Gulumian
- National Institute for Occupational Health, Johannesburg, South Africa
- Haematology and Molecular Medicine, University of the Witwatersrand, Parktown, Johannesburg, South Africa
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Burk J, Sikk L, Burk P, Manshian BB, Soenen SJ, Scott-Fordsmand JJ, Tamm T, Tämm K. Fe-Doped ZnO nanoparticle toxicity: assessment by a new generation of nanodescriptors. NANOSCALE 2018; 10:21985-21993. [PMID: 30452031 DOI: 10.1039/c8nr05220d] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In the search for novel tools to combat cancer, nanoparticles (NPs) have attracted a lot of attention. Recently, the controlled release of cancer-cell-killing metal ions from doped NPs has shown promise, but fine tuning of dissolution kinetics is required to ensure specificity and minimize undesirable toxic side-effects. Theoretical tools to help in reaching a proper understanding and finally be able to control the dissolution kinetics by NP design have not been available until now. Here, we present a novel set of true nanodescriptors to analyze the charge distribution, the effect of doping and surface coating of whole metal oxide NP structures. The polarizable model of oxygen atoms enables light to be shed on the charge distribution on the NP surface, allowing the in detail study of the factors influencing the release of metal ions from NPs. The descriptors and their capabilities are demonstrated on a Fe-doped ZnO nanoparticle system, a system with practical outlook and available experimental data.
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Affiliation(s)
- Jaanus Burk
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia.
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Oztuna A, Nazir H. Pentafluoropropionic Anhydride Functionalized PAMAM Dendrimer as miRNA Delivery Reagent. JOURNAL OF THE TURKISH CHEMICAL SOCIETY, SECTION A: CHEMISTRY 2018. [DOI: 10.18596/jotcsa.463855] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
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37
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Affiliation(s)
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
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38
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Saini B, Srivastava S. Nanotoxicity prediction using computational modelling - review and future directions. ACTA ACUST UNITED AC 2018. [DOI: 10.1088/1757-899x/348/1/012005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches. Sci Rep 2018; 8:3141. [PMID: 29453389 PMCID: PMC5816655 DOI: 10.1038/s41598-018-21431-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 02/05/2018] [Indexed: 12/14/2022] Open
Abstract
Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials.
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Luan F, Tang L, Zhang L, Zhang S, Monteagudo MC, Cordeiro MD. A further development of the QNAR model to predict the cellular uptake of nanoparticles by pancreatic cancer cells. Food Chem Toxicol 2018; 112:571-580. [DOI: 10.1016/j.fct.2017.04.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Revised: 03/14/2017] [Accepted: 04/11/2017] [Indexed: 02/06/2023]
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Jeong J, Kim SH, Lee S, Lee DK, Han Y, Jeon S, Cho WS. Differential Contribution of Constituent Metal Ions to the Cytotoxic Effects of Fast-Dissolving Metal-Oxide Nanoparticles. Front Pharmacol 2018; 9:15. [PMID: 29403385 PMCID: PMC5786562 DOI: 10.3389/fphar.2018.00015] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Accepted: 01/05/2018] [Indexed: 12/04/2022] Open
Abstract
The main mechanism of toxicity for fast-dissolving nanoparticles (NPs) is relatively simple as it originates from the intrinsic toxicity of their constituent elements rather than complicated surface reactivity. However, there is little information about the compared toxicity of fast-dissolving NP and its constituent ion, which is essential for understanding the mechanism of NP toxicity and the development of a structure-toxicity relationship (STR) model. Herein, we selected three types of fast-dissolving metal-oxide NPs (CoO, CuO, and ZnO) and constituent metal chlorides (CoCl2, CuCl2, and ZnCl2) to compare dose-response curves between NP and its constituent metal. These materials were treated relevant cell lines for inhalation setting (i.e., differentiated THP-1 cells for macrophages and A549 cells for alveolar epithelial cells) and cytotoxicity as an endpoint was evaluated at 24 h post-incubation. The results showed that CoO and CuO NPs in both cell types showed similar patterns of dose-response curves and cytotoxic potential compared to that of their respective metal chloride. On the other hand, ZnO NPs in both cell types showed a completely different dose-response curve compared to that of ZnCl2: ZnO NPs showed modest slope and much less potential for cytotoxicity compared to that of ZnCl2. These results imply that fast-dissolving metal-oxide NPs are not always have similar dose-response curves and toxic potentials compared to their constituent metal chlorides and this may be due to the differential mechanism of intracellular uptake of these substances and their interaction with intracellular detoxification molecules. Further investigations are needed for the use of toxic potential of metal ions as a predicting factors of fast-dissolving NPs toxicity. In addition, chelating agent specific for dissolved metal ions can be applied for the treatment of these fast-dissolving NPs.
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Affiliation(s)
- Jiyoung Jeong
- Laboratory of Toxicology, Department of Medicinal Biotechnology, College of Health Sciences, Dong-A University, Busan, South Korea
| | - Sung-Hyun Kim
- Laboratory of Toxicology, Department of Medicinal Biotechnology, College of Health Sciences, Dong-A University, Busan, South Korea
| | - Seonghan Lee
- Laboratory of Toxicology, Department of Medicinal Biotechnology, College of Health Sciences, Dong-A University, Busan, South Korea
| | - Dong-Keon Lee
- Laboratory of Toxicology, Department of Medicinal Biotechnology, College of Health Sciences, Dong-A University, Busan, South Korea
| | - Youngju Han
- Laboratory of Toxicology, Department of Medicinal Biotechnology, College of Health Sciences, Dong-A University, Busan, South Korea
| | - Soyeon Jeon
- Laboratory of Toxicology, Department of Medicinal Biotechnology, College of Health Sciences, Dong-A University, Busan, South Korea
| | - Wan-Seob Cho
- Laboratory of Toxicology, Department of Medicinal Biotechnology, College of Health Sciences, Dong-A University, Busan, South Korea
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Abstract
This chapter presents an outline of the recent available information regarding safety, toxicity, and efficacy of nano drug delivery systems. Of particular importance is the evaluation of several key factors to design nontoxic and effective nanoformulations. Among them, we focus on nanostructure materials and synthesis methods, mechanisms of interactions with biological systems, treatment of nanoparticles, manufacture impurities, and nanostability. Emphasis is given to in silico, in vitro, and in vivo models used to assess and predict the toxicity of these new formulations. Additionally, some examples of in vitro and in vivo studies of specific nanoderivatives are also presented in this chapter.
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Gozalbes R, Vicente de Julián-Ortiz J. Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation. ACTA ACUST UNITED AC 2018. [DOI: 10.4018/ijqspr.2018010101] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Chemoinformatics methodologies such as QSAR/QSPR have been used for decades in drug discovery projects, especially for the finding of new compounds with therapeutic properties and the optimization of ADME properties on chemical series. The application of computational techniques in predictive toxicology is much more recent, and they are experiencing an increasingly interest because of the new legal requirements imposed by national and international regulations. In the pharmaceutical field, the US Food and Drug Administration (FDA) support the use of predictive models for regulatory decision-making when assessing the genotoxic and carcinogenic potential of drug impurities. In Europe, the REACH legislation promotes the use of QSAR in order to reduce the huge amount of animal testing needed to demonstrate the safety of new chemical entities subjected to registration, provided they meet specific conditions to ensure their quality and predictive power. In this review, the authors summarize the state of art of in silico methods for regulatory purposes, with especial emphasis on QSAR models.
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Din MI, Arshad F, Hussain Z, Mukhtar M. Green Adeptness in the Synthesis and Stabilization of Copper Nanoparticles: Catalytic, Antibacterial, Cytotoxicity, and Antioxidant Activities. NANOSCALE RESEARCH LETTERS 2017; 12:638. [PMID: 29282555 PMCID: PMC5745208 DOI: 10.1186/s11671-017-2399-8] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Accepted: 12/01/2017] [Indexed: 05/08/2023]
Abstract
Copper nanoparticles (CuNPs) are of great interest due to their extraordinary properties such as high surface-to-volume ratio, high yield strength, ductility, hardness, flexibility, and rigidity. CuNPs show catalytic, antibacterial, antioxidant, and antifungal activities along with cytotoxicity and anticancer properties in many different applications. Many physical and chemical methods have been used to synthesize nanoparticles including laser ablation, microwave-assisted process, sol-gel, co-precipitation, pulsed wire discharge, vacuum vapor deposition, high-energy irradiation, lithography, mechanical milling, photochemical reduction, electrochemistry, electrospray synthesis, hydrothermal reaction, microemulsion, and chemical reduction. Phytosynthesis of nanoparticles has been suggested as a valuable alternative to physical and chemical methods due to low cytotoxicity, economic prospects, environment-friendly, enhanced biocompatibility, and high antioxidant and antimicrobial activities. The review explains characterization techniques, their main role, limitations, and sensitivity used in the preparation of CuNPs. An overview of techniques used in the synthesis of CuNPs, synthesis procedure, reaction parameters which affect the properties of synthesized CuNPs, and a screening analysis which is used to identify phytochemicals in different plants is presented from the recent published literature which has been reviewed and summarized. Hypothetical mechanisms of reduction of the copper ion by quercetin, stabilization of copper nanoparticles by santin, antimicrobial activity, and reduction of 4-nitrophenol with diagrammatic illustrations are given. The main purpose of this review was to summarize the data of plants used for the synthesis of CuNPs and open a new pathway for researchers to investigate those plants which have not been used in the past. Graphical abstract Proposed Mechanism for Antibacterial activity of copper nanoparticles.
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Affiliation(s)
| | - Farhan Arshad
- Institute of Chemistry, University of Punjab, Lahore, 54590, Pakistan
| | - Zaib Hussain
- Institute of Chemistry, University of Punjab, Lahore, 54590, Pakistan
| | - Maria Mukhtar
- Department of Zoology, University of Punjab, Lahore, 54590, Pakistan
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Wang W, Sedykh A, Sun H, Zhao L, Russo DP, Zhou H, Yan B, Zhu H. Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling. ACS NANO 2017; 11:12641-12649. [PMID: 29149552 PMCID: PMC5772766 DOI: 10.1021/acsnano.7b07093] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The discovery of biocompatible or bioactive nanoparticles for medicinal applications is an expensive and time-consuming process that may be significantly facilitated by incorporating more rational approaches combining both experimental and computational methods. However, it is currently hindered by two limitations: (1) the lack of high-quality comprehensive data for computational modeling and (2) the lack of an effective modeling method for the complex nanomaterial structures. In this study, we tackled both issues by first synthesizing a large library of nanoparticles and obtained comprehensive data on their characterizations and bioactivities. Meanwhile, we virtually simulated each individual nanoparticle in this library by calculating their nanostructural characteristics and built models that correlate their nanostructure diversity to the corresponding biological activities. The resulting models were then used to predict and design nanoparticles with desired bioactivities. The experimental testing results of the designed nanoparticles were consistent with the model predictions. These findings demonstrate that rational design approaches combining high-quality nanoparticle libraries, big experimental data sets, and intelligent computational models can significantly reduce the efforts and costs of nanomaterial discovery.
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Affiliation(s)
- Wenyi Wang
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Alexander Sedykh
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
- Sciome, Research Triangle Park, North Carolina 27709, United States
| | - Hainan Sun
- School of Environmental Science and Engineering, Shandong University, Jinan 250100, China
| | - Linlin Zhao
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Daniel P. Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Hongyu Zhou
- School of Environment, Jinan University, Guangzhou 510632, China
| | - Bing Yan
- School of Environmental Science and Engineering, Shandong University, Jinan 250100, China
- Corresponding Authors. (B. Yan): . (H. Zhu):
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
- Department of Chemistry, Rutgers University, Camden, New Jersey 08102, United States
- Corresponding Authors. (B. Yan): . (H. Zhu):
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A two-step model of TiO 2 nanoparticle toxicity in human liver tissue. Toxicol Appl Pharmacol 2017; 334:47-54. [DOI: 10.1016/j.taap.2017.08.018] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2017] [Revised: 08/21/2017] [Accepted: 08/24/2017] [Indexed: 12/17/2022]
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Mikolajczyk A, Sizochenko N, Mulkiewicz E, Malankowska A, Nischk M, Jurczak P, Hirano S, Nowaczyk G, Zaleska-Medynska A, Leszczynski J, Gajewicz A, Puzyn T. Evaluating the toxicity of TiO 2-based nanoparticles to Chinese hamster ovary cells and Escherichia coli: a complementary experimental and computational approach. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2017; 8:2171-2180. [PMID: 29114443 PMCID: PMC5669235 DOI: 10.3762/bjnano.8.216] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 09/18/2017] [Indexed: 05/25/2023]
Abstract
Titania-supported palladium, gold and bimetallic nanoparticles (second-generation nanoparticles) demonstrate promising photocatalytic properties. However, due to unusual reactivity, second-generation nanoparticles can be hazardous for living organisms. Considering the ever-growing number of new types of nanoparticles that can potentially contaminate the environment, a determination of their toxicity is extremely important. The main aim of presented study was to investigate the cytotoxic effect of surface modified TiO2-based nanoparticles, to model their quantitative nanostructure-toxicity relationships and to reveal the toxicity mechanism. In this context, toxicity tests for surface-modified TiO2-based nanoparticles were performed in vitro, using Gram-negative bacteria Escherichia coli and Chinese hamster ovary (CHO-K1) cells. The obtained cytotoxicity data were analyzed by means of computational methods (quantitative structure-activity relationships, QSAR approach). Based on a combined experimental and computational approach, predictive models were developed, and relationships between cytotoxicity, size, and specific surface area (Brunauer-Emmett-Teller surface, BET) of nanoparticles were discussed.
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Affiliation(s)
- Alicja Mikolajczyk
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Natalia Sizochenko
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
- Interdisciplinary Center for Nanotoxicity, Jackson State University, 39217, Jackson, MS, USA
| | - Ewa Mulkiewicz
- Department of Environmental Analytics, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Anna Malankowska
- Department of Environmental Technology, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Michal Nischk
- Department of Environmental Technology, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Przemyslaw Jurczak
- Department of Biomedical Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Seishiro Hirano
- Center for Environmental Risk Research, National Institute for Environmental Studies, Tsukuba, 16-2 Onogawa, Ibaraki 305-8506, Japan
| | - Grzegorz Nowaczyk
- NanoBioMedical Centre, Adam Mickiewicz University, Umultowska 85, 61-614 Poznan, Poland
| | - Adriana Zaleska-Medynska
- Department of Environmental Technology, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Jackson State University, 39217, Jackson, MS, USA
| | - Agnieszka Gajewicz
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Tomasz Puzyn
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
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48
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Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology. Food Chem Toxicol 2017; 112:478-494. [PMID: 28943385 DOI: 10.1016/j.fct.2017.09.037] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 08/31/2017] [Accepted: 09/19/2017] [Indexed: 11/20/2022]
Abstract
Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known "OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models", with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles.
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49
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Chen G, Vijver MG, Xiao Y, Peijnenburg WJGM. A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials. MATERIALS 2017; 10:ma10091013. [PMID: 28858269 PMCID: PMC5615668 DOI: 10.3390/ma10091013] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2017] [Revised: 08/08/2017] [Accepted: 08/28/2017] [Indexed: 11/16/2022]
Abstract
Gathering required information in a fast and inexpensive way is essential for assessing the risks of engineered nanomaterials (ENMs). The extension of conventional (quantitative) structure-activity relationships ((Q)SARs) approach to nanotoxicology, i.e., nano-(Q)SARs, is a possible solution. The preliminary attempts of correlating ENMs' characteristics to the biological effects elicited by ENMs highlighted the potential applicability of (Q)SARs in the nanotoxicity field. This review discusses the current knowledge on the development of nano-(Q)SARs for metallic ENMs, on the aspects of data sources, reported nano-(Q)SARs, and mechanistic interpretation. An outlook is given on the further development of this frontier. As concluded, the used experimental data mainly concern the uptake of ENMs by different cell lines and the toxicity of ENMs to cells lines and Escherichia coli. The widely applied techniques of deriving models are linear and non-linear regressions, support vector machine, artificial neural network, k-nearest neighbors, etc. Concluded from the descriptors, surface properties of ENMs are seen as vital for the cellular uptake of ENMs; the capability of releasing ions and surface redox properties of ENMs are of importance for evaluating nanotoxicity. This review aims to present key advances in relevant nano-modeling studies and stimulate future research efforts in this quickly developing field of research.
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Affiliation(s)
- Guangchao Chen
- 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.
| | - Yinlong Xiao
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
| | - Willie J G M 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), 3720 BA Bilthoven, The Netherlands.
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50
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Toropova AP, Toropov AA, Leszczynska D, Leszczynski J. CORAL and Nano-QFAR: Quantitative feature - Activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co 3O 4, and TiO 2). ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2017; 139:404-407. [PMID: 28192776 DOI: 10.1016/j.ecoenv.2017.01.054] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 01/25/2017] [Accepted: 01/31/2017] [Indexed: 06/06/2023]
Abstract
Quantitative feature - activity relationships (QFAR) approach was applied to prediction of bioavailability of metal oxide nanoparticles. ZnO, CuO, Co3O4, and TiO2 nanoxides were considered. The computational model for bioavailability of investigated species is asserted. The model was calculated using the Monte Carlo method. The CORAL free software (http://www.insilico.eu/coral) was used in this study. The developed model was tested by application of three different splits of data into the training and validation sets. So-called, quasi-SMILES are used to represent the conditions of action of metal oxide nanoparticles. A new paradigm of building up predictive models of endpoints related to nanomaterials is suggested. The paradigm is the following "An endpoint is a mathematical function of available eclectic data (conditions)". Recently, the paradigm has been checked up with endpoints related to metal oxide nanoparticles, fullerenes, and multi-walled carbon-nanotubes.
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Affiliation(s)
- Alla P Toropova
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milan, Italy.
| | - Andrey A Toropov
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milan, Italy
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch Street, Jackson, MS 39217-0510, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA
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