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E-grāmata: Computational Nanotoxicology: Challenges and Perspectives

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  • Formāts: 568 pages
  • Izdošanas datums: 13-Nov-2019
  • Izdevniecība: Pan Stanford Publishing Pte Ltd
  • ISBN-13: 9781000681420
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Computational Nanotoxicology: Challenges and PerspectivesPalielināt
  • Formāts: 568 pages
  • Izdošanas datums: 13-Nov-2019
  • Izdevniecība: Pan Stanford Publishing Pte Ltd
  • ISBN-13: 9781000681420
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The development of computational methods that support human health and environmental risk assessment of engineered nanomaterials (ENMs) has attracted great interest because the application of these methods enables us to fill existing experimental data gaps. However, considering the high degree of complexity and multifunctionality of ENMs, computational methods originally developed for regular chemicals cannot always be applied explicitly in nanotoxicology. This book discusses the current state of the art and future needs in the development of computational modeling techniques for nanotoxicology. It focuses on (i) computational chemistry (quantum mechanics, semi-empirical methods, density functional theory, molecular mechanics, molecular dynamics), (ii) nanochemoinformatic methods (quantitative structure–activity relationship modeling, grouping, read-across), and (iii) nanobioinformatic methods (genomics, transcriptomics, proteomics, metabolomics). It reviews methods of calculating molecular descriptors sufficient to characterize the structure of nanoparticles, specifies recent trends in the validation of computational methods, and discusses ways to cope with the uncertainty of predictions. In addition, it highlights the status quo and further challenges in the application of computational methods in regulation (e.g., REACH, OECD) and in industry for product development and optimization and the future directions for increasing acceptance of computational modeling for nanotoxicology.

Preface xv
1 Modeling of Nanomaterials for Safety Assessment: From Regulatory Requirements to Supporting Scientific Theories 1(98)
Lara Lamon
David Asturiol
Karin Aschberger
Jos Bessems
Kirsten Gerloff
Andrea-Nicole Richarz
Andrew Worth
1.1 Introduction
2(1)
1.2 Information Requirements for Risk Assessment: Legal Provisions and Guidance
3(7)
1.2.1 Chemical Substances under REACH
4(3)
1.2.2 Cosmetic Products
7(1)
1.2.3 Biocidal Products
8(1)
1.2.4 Plant Protection Products
9(1)
1.2.5 Food Production
9(1)
1.3 Risk Assessment
10(3)
1.4 Properties That Drive NM Behavior (Fate and Toxicity)
13(13)
1.4.1 Theories Underlying Environmental and Biological Fate
22(4)
1.5 Understanding NMs' Fate and Toxicity
26(20)
1.5.1 Theories Underlying Environmental and Biological Fate
26(8)
1.5.1.1 Agglomeration and aggregation kinetics in fluid media
27(1)
1.5.1.2 DLVO theory
27(3)
1.5.1.3 Smoluchowski-Friedlander theory
30(3)
1.5.1.4 Fractal approaches
33(1)
1.5.2 Human Kinetics
34(8)
1.5.2.1 Preabsorption processes
34(3)
1.5.2.2 Absorption
37(2)
1.5.2.3 Distribution
39(1)
1.5.2.4 Metabolism/Dissolution/Transformation/Bio-nano interaction
39(2)
1.5.2.5 Excretion
41(1)
1.5.2.6 Elimination (sum of solubilization and excretion)
42(1)
1.5.3 Toxicodynamics
42(4)
1.6 Standard Test Guideline Methods for Toxicity Testing
46(3)
1.7 Alternative Approaches to Animal Testing
49(26)
1.7.1 Adverse Outcome Pathways
50(1)
1.7.2 In silico Methods
51(13)
1.7.2.1 Supervised and unsupervised methods
51(3)
1.7.2.2 QSAR/QSPR
54(4)
1.7.2.3 Validation of QSARs for regulatory purposes
58(1)
1.7.2.4 Expert systems
58(1)
1.7.2.5 Applicability of QSAR/QSPR approaches to NMs
59(1)
1.7.2.6 Physiologically based kinetic modeling
60(4)
1.7.3 In vitro Methods
64(5)
1.7.4 Grouping and Read-Across
69(2)
1.7.5 Weight of Evidence
71(2)
1.7.6 Integrated Approaches to Testing and Assessment
73(2)
1.8 Concluding Remarks
75(24)
2 Current Developments and Recommendations in Computational Nanotoxicology in View of Regulatory Applications 99(58)
Andrea-Nicole Richarz
Lara Lamon
David Asturiol
Andrew P. Worth
2.1 Introduction
100(1)
2.2 Computational Nanotoxicology Research Project Landscape
101(24)
2.2.1 European Nanosafety Research Activities
101(22)
2.2.2 Related International Activities
123(2)
2.3 Challenges and Needs for the Development and Use of Computational Methods
125(3)
2.4 Progress against the Challenges and Needs
128(8)
2.4.1 Results from EU FP7-Funded Research Projects
128(4)
2.4.2 Horizon 2020 Research Projects
132(3)
2.4.3 Other Activities
135(1)
2.5 Conclusions from the Research Landscape Review
136(2)
2.5.1 Conclusions on the Needs Addressed
136(1)
2.5.2 Recommendations for Nanosafety Research
137(1)
2.6 Overall Conclusions on the Availability and Applicability of Computational Approaches for Nanosafety Assessment
138(20)
2.6.1 Inherent Scientific Uncertainties
140(1)
2.6.2 Data Quality and Variability
140(2)
2.6.3 Model Landscape and Regulatory Relevance
142(1)
2.6.4 Model Accessibility and Visibility
142(2)
2.6.5 Practicality of Performing Read-Across for Nanomaterials
144(1)
2.6.6 Need for Infrastructure
145(12)
3 Physicochemical Properties of Nanomaterials from in silico Simulations: An Introduction to Density Functional Theory and Beyond 157(32)
Laura Escorihuela
Alberto Fernandez
Robert Rallo
Benjaml Martorell
3.1 Introduction
158(3)
3.2 Classic Density Functional Theory: Jacob's Ladder
161(7)
3.2.1 Local Density Approximation
163(1)
3.2.2 GGA and Meta-GGA
163(2)
3.2.3 Hybrid Functionals
165(1)
3.2.4 The Limits of Classic DFT
166(2)
3.3 Beyond Classic DFT
168(12)
3.3.1 DFT+U
168(2)
3.3.2 GW
170(1)
3.3.3 Density Functional Tight Binding
171(3)
3.3.4 LS-DFT
174(1)
3.3.5 Time-Dependent DFT
175(1)
3.3.6 Implicit Solvation Models
176(4)
3.4 Concluding Remarks
180(9)
4 Bionano Interactions: A Key to Mechanistic Understanding of Nanoparticle Toxicity 189(28)
David Power
Stefano Poggio
Hender Lopez
Vladimir Lobaskin
4.1 Introduction
190(1)
4.2 Advanced Descriptors of the Bionano Interface
190(4)
4.2.1 Protein Corona
190(2)
4.2.2 Nanoparticle Descriptors and QSARs
192(1)
4.2.3 Biomolecule Descriptors
193(1)
4.2.4 Interaction Descriptors
194(1)
4.3 Multiscale Modeling of the Bionano Interface
194(10)
4.3.1 General Methodology
194(2)
4.3.2 Coarse-Grained Protein Model
196(1)
4.3.3 Coarse-Grained Nanoparticles
197(1)
4.3.4 Generation of Surface Pair Potentials
198(3)
4.3.5 Generation of the Core Potential
201(1)
4.3.6 Calculation of the Adsorption Energy
202(1)
4.3.7 From United-Atom to United-Amino Acid Description
203(1)
4.4 Application of the Method
204(6)
4.4.1 Protein Descriptors
204(1)
4.4.2 Bionano Interface Descriptors
205(3)
4.4.3 United-Amino Acid Model
208(2)
4.5 Conclusions
210(7)
5 From Modeling Nanoparticle-Membrane Interactions toward Nanotoxicology 217(28)
Karandeep Singh
Qingfen Yu
Sabyasachi Dasgupta
Gerhard Gompper
Thorsten Auth
5.1 Particles at Membranes
218(4)
5.1.1 Penetration vs. Wrapping
218(1)
5.1.2 Chemically Specific vs. Generic Models
219(1)
5.1.3 Nanoparticle-Wrapping Endpoints
220(2)
5.2 The Helfrich Model
222(1)
5.3 Predicting Wrapping
223(11)
5.3.1 Spherical Nanoparticles
225(1)
5.3.2 Nonspherical Nanoparticles
226(2)
5.3.3 Soft Nanoparticles
228(1)
5.3.4 Dosage Effects: Cooperative Wrapping
229(2)
5.3.5 Multicomponent Biological Membranes
231(1)
5.3.6 Actual and Spontaneous Membrane Curvature
232(2)
5.4 Experimental Validation
234(2)
5.5 Toward Nanotoxicology
236(9)
6 Descriptors in Nano-QSAR/QSPR Modeling 245(58)
Ewelina Wyrzykowska
Karolina Jagiello
Bakhtiyor Rasulev
Tomasz Puzyn
6.1 Nano-QSAR/QSPR Modeling: Benefits and Challenges
246(2)
6.2 Idea of Descriptors
248(7)
6.2.1 Size Aspect
250(2)
6.2.2 Chemical Composition Aspect
252(1)
6.2.3 Surrounding Aspect
253(2)
6.3 The First Nano-QSAR Model and Its Recalculations
255(21)
6.3.1 Quantum-Mechanical Descriptors
255(3)
6.3.2 SMILES-Based Optimal Descriptors
258(1)
6.3.3 Improved SMILES-Based Optimal Descriptors
259(3)
6.3.4 Periodic Table Descriptors
262(3)
6.3.5 SiRMS Descriptors
265(3)
6.3.6 Liquid Drop Model Descriptors
268(2)
6.3.7 Metal-Ligand Binding Characteristic
270(1)
6.3.8 Full-Particle Descriptors
271(5)
6.4 Other Nanodescriptors
276(14)
6.4.1 Perturbation Approach
277(5)
6.4.2 Image Descriptors
282(3)
6.4.3 Reusing Toxicity Measurements
285(2)
6.4.4 Mixture Descriptors
287(3)
6.5 Summary and Future Perspectives
290(13)
7 Nano-QSAR for Environmental Hazard Assessment: Turning Challenges into Opportunities 303(78)
Willie Peijnenburg
Guangchao Chen
Martina Vijver
7.1 Introduction
304(8)
7.1.1 General
304(1)
7.1.2 Safety Concerns
305(1)
7.1.3 Toxicity of ENMs
306(2)
7.1.4 Environmental Risk Assessment and Safe-by-Design Development of ENMs
308(2)
7.1.5 Handling Nanosafety with the Aid of Computational Toxicology
310(2)
7.2 Inventory of Existing Toxicity Data of Metal-Based ENMs
312(6)
7.2.1 Need for Reliable Experimental Data
312(1)
7.2.2 Overview of Experimental Data
313(3)
7.2.3 Suitability of Experimental Data for QSAR Modeling
316(2)
7.3 Recent Advances toward the Development of QSARs for Metallic ENMs
318(45)
7.3.1 Representation of the Intrinsic Properties of ENMs
318(3)
7.3.2 Overview of Models and Modeling Approaches
321(62)
7.3.2.1 General considerations
321(4)
7.3.2.2 Sources of data actually used for modeling
325(15)
7.3.2.3 Existing nano-QSARs
340(15)
7.3.2.4 Interpretation of mechanisms of ENM biological activities using the models developed
355(8)
7.4 Conclusions and Outlook
363(18)
8 Read-Across to Fill Toxicological Data Gaps: Good Practice to Ensure Success with Nanoparticles 381(20)
Mark T.D. Cronin
Steven J. Enoch
Judith C. Madden
Andrea-Nicole Richarz
8.1 Introduction
382(1)
8.2 Why and When Read-Across Is Used to Predict Toxicity
383(4)
8.2.1 Ethics
383(1)
8.2.2 Cost
383(1)
8.2.3 Compliance with Regulatory Pressures
384(1)
8.2.4 Expectation of Common Properties within a Group
385(1)
8.2.5 The Necessity for Alternatives due to the Difficulty of Testing
385(1)
8.2.6 Lack of Data for New Nanoparticles and New Toxicological Problems
386(1)
8.2.7 Opportunities to Utilize New Methods and Techniques
386(1)
8.3 Good Practice in Read-Across: Ensuring Success
387(7)
8.3.1 Proper Definition of Structure
387(1)
8.3.2 Understanding How Structure Affects Toxicology and Mechanism of Action: Appropriate Grouping
388(1)
8.3.3 High-Quality Experimental Data to Anchor the Read-Across
389(1)
8.3.4 Use Appropriate Computational Tools for Read-Across
390(1)
8.3.5 Justification of Nanoparticle Similarity and Read-Across Hypothesis
391(1)
8.3.6 Inclusion of Toxicokinetics for Members of a Category
391(1)
8.3.7 Identifying and Defining Uncertainties
392(1)
8.3.8 Assessing and Increasing Confidence
393(1)
8.3.9 Description and Documentation of the Read-Across Prediction
394(1)
8.4 Conclusions
394(7)
9 Computational Methods of Interspecies Nanotoxicity Extrapolation: A Step toward the Future 401(36)
Supratik Kar
Shinjita Ghosh
Jerzy Leszczynski
9.1 Introduction
402(3)
9.2 Nanotoxicity
405(3)
9.2.1 Carbon NMs
407(1)
9.2.2 Metal Oxide NMs
407(1)
9.3 Role of Computational Methods in Nanotoxicity Predictions
408(2)
9.4 Interspecies Model: Extrapolation through Toxicity-Toxicity Correlation
410(2)
9.5 Fundamental of i-QSTR Correlation
412(3)
9.6 Necessity of Interspecies Extrapolation
415(2)
9.7 Computational Interspecies Nanotoxicity Models
417(4)
9.8 Species and Endpoints for an Interspecies Model
421(1)
9.9 Future Direction
421(5)
9.10 Conclusion
426(11)
10 On Error Measures for Validation and Uncertainty Estimation of Predictive QSAR Models 437(58)
Supratik Kar Kunal Roy
Jerzy Leszczynski
10.1 Introduction
438(3)
10.2 Concept and Significance of Validation of QSAR Models
441(1)
10.3 Validation Strategies
442(6)
10.3.1 Internal Validation or Cross-Validation
442(1)
10.3.2 External Validation
443(1)
10.3.3 True External Validation
444(1)
10.3.4 Double Cross-Validation
444(1)
10.3.5 Data Randomization or Y-Scrambling
445(1)
10.3.6 Bias and Variance in Prediction Errors
445(3)
10.4 Validation Metrics
448(14)
10.4.1 Metrics for Classification-Based QSAR Models
448(5)
10.4.2 Metrics for Regression-Based QSAR Models
453(9)
10.5 Model Uncertainty Aspects
462(2)
10.5.1 Derivation of Uncertainty in QSAR Predictions: Mathematical Formalization
462(2)
10.6 Prediction Confidence and Conformal Predictions
464(5)
10.6.1 Conformal Prediction Errors in Regression
465(2)
10.6.2 Conformal Prediction Errors in Classification
467(2)
10.7 Randomization: Assessment of Chance Correlation
469(2)
10.7.1 Randomization Metrics cRp2
470(1)
10.7.2 Q2yrand and R2yrand
470(1)
10.8 Applicability Domain and Reliability of Predictions
471(8)
10.9 Open-Source Software Tools for QSAR Model Development and Validation
479(9)
10.10 Conclusion
488(7)
11 Green Toxicology Meets Nanotoxicology: The Process of Sustainable Nanomaterial Development and Use 495(12)
Alexandra Maertens
Thomas Hartung
11.1 Green Toxicology
496(1)
11.2 Green Toxicology Principles Applied to Nanomaterials
497(6)
11.2.1 Principle 1: Design Benign
497(2)
11.2.2 Principle 2: Test Early, Fail Safe
499(1)
11.2.3 Principle 3: Avoid Exposure and thus Testing Needs
500(1)
11.2.4 Principle 4: Make Testing Sustainable
501(1)
11.2.5 Principle 5: Adopt Test Strategies That Are Not Mature Enough for Regulatory Use
502(1)
11.2.6 Principle 6: Green Toxicology as Twenty-First-Century Toxicology
502(1)
11.3 Going Forward
503(4)
12 Issues for and Examples of Computational Design of "Safe-by-Design" Nanomaterials 507(28)
David A. Winkler
12.1 Introduction
508(2)
12.2 Biocorona: Biologically Relevant Entity
510(3)
12.3 System Complexity, Reproducibility, Data Generation, and Curation
513(5)
12.3.1 Complexity of Nanoparticle-Biology Interactions
513(1)
12.3.2 Experimental Issues
514(1)
12.3.3 Data Availability, Reliability, and Processing Issues
515(2)
12.3.4 Social Issues and Medical Applications
517(1)
12.4 QSAR Modeling for Nanomaterials
518(1)
12.5 Read-Across
519(2)
12.6 Toward Safety-by-Design
521(3)
12.7 Nanomedicine Implications
524(2)
12.8 Conclusions and Perspective
526(9)
Index 535
Agnieszka Gajewicz is assistant professor at the University of Gdansk, Poland. She has authored more than 45 research publications in leading nanotechnology- and environment-related journals and has received prestigious international and national awards, including the LOréal-UNESCO International Rising Talents Award For Women in Science and a fellowship of the Polish Minister of Science and Higher Education for outstanding young researchers. Her current research interests include the development and application of machine learning, statistical learning theory, and chemometrics to address problems and challenges in computer-based methods for chemical safety assessment and the design of new chemicals (nanomaterials, ionic liquids) that are safe for human health and the environment.

Tomasz Puzyn is professor at the Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Poland. He has authored more than 120 research papers in leading nanotechnology- and environment-related journals and edited 4 books. His main achievement is the introduction of quantitative structureactivity relationship (QSAR) modeling in nanotechnology. He has led two completed EU FP7 projects and participated in five H2020 projects conducted within the European NanoSafety Cluster. He has received prestigious national and international awards, including fellowships of the Japan Society for the Promotion of Science, the Foundation for Polish Science, and the Polish Minister of Science and Higher Education. Prof. Puzyn is founder and CEO of QSAR Lab Ltd., a spin-off company that uses the potential and experience of research staff from the Laboratory of Environmental Chemometrics to support chemical, cosmetic, and pharmaceutical industries in developing computational methods for designing innovative products (nanomaterials, ionic liquids) that are safe for humans and the environment.
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