Computational Nanotoxicology-Challenges and Perspectives

570 Pages·2019·19.644 MB·\570

by  Agnieszka Gajewicz (Editor), Tomasz Puzyn (Editor)

“This timely book highlights the state of the art in computational approaches currently being
investigated to predict nanomaterial toxicity and support safety by design. It is a must-read for all
those involved in computational nanotoxicology research as well as those working in industry or
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regulatory bodies who seek to understand how these approaches could be used to support safety-by-
design nanotechnology.” o
Dr. Richard L. Marchese Robinson m
University of Leeds, UK
p
The development of computational methods that support human health and environmental risk u
assessment of engineered nanomaterials (ENMs) has attracted great interest because the application t
of these methods enables us to fill existing experimental data gaps. However, considering the high a
degree of complexity and multifunctionality of ENMs, computational methods originally developed t
for regular chemicals cannot always be applied explicitly in nanotoxicology. This book discusses i
the current state of the art and future needs in the development of computational modeling o
techniques for nanotoxicology. It focuses on (i) computational chemistry (quantum mechanics, n
semi-empirical methods, density functional theory, molecular mechanics, molecular dynamics), (ii) a
nanochemoinformatic methods (quantitative structure–activity relationship modeling, grouping, l

read-across), and (iii) nanobioinformatic methods (genomics, transcriptomics, proteomics, meta-
N
bolomics). It reviews methods of calculating molecular descriptors sufficient to characterize the
structure of nanoparticles, specifies recent trends in the validation of computational methods, and a
discusses ways to cope with the uncertainty of predictions. In addition, it highlights the status quo n
and further challenges in the application of computational methods in regulation (e.g., REACH, OECD) o
and in industry for product development and optimization and the future directions for increasing t
acceptance of computational modeling for nanotoxicology. o
x
Agnieszka Gajewicz is assistant professor at the University of Gdansk, Poland. She i
c
has authored more than 45 research publications in leading nanotechnology- and
o
environment-related journals and has received prestigious international and national
awards, including the L’Oréal-UNESCO International Rising Talents Award For Women l
o
in Science and a fellowship of the Polish Minister of Science and Higher Education
g
for outstanding young researchers. Her current research interests include the
y
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. edited by
Tomasz Puzyn is professor at the Laboratory of Environmental Chemometrics, Faculty Agnieszka Gajewicz | Tomasz Puzyn
G
of Chemistry, University of Gdansk, Poland. He has authored more than 120 research
a
papers in leading nanotechnology- and environment-related journals and edited j
e
4 books. His main achievement is the introduction of quantitative structure–activity w
relationship (QSAR) modeling in nanotechnology. He has led two completed EU FP7
i
projects and participated in five H2020 projects conducted within the European c
z Computational Nanotoxicology
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 P
u
the Polish Minister of Science and Higher Education. Prof. Puzyn is founder and CEO of QSAR Lab
z
Ltd., a spin-off company that uses the potential and experience of research staff from the Laboratory y Challenges and Perspectives
of Environmental Chemometrics to support chemical, cosmetic, and pharmaceutical industries in n
developing computational methods for designing innovative products (nanomaterials, ionic liquids)
that are safe for humans and the environment.
V750
ISBN 978-981-4800-64-8

Computational Nanotoxicology

Computational Nanotoxicology
Challenges and Perspectives
edited by
Agnieszka Gajewicz
Tomasz Puzyn
Published by
Jenny Stanford Publishing Pte. Ltd.
Level 34, Centennial Tower
3 Temasek Avenue
Singapore 039190
Email: [email protected]
Web: www.jennystanford.com
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library.
Computational Nanotoxicology: Challenges and Perspectives
All rights reserved. This book, or parts thereof, may not be reproduced in any form
Copyright © 2020 by Jenny Stanford Publishing Pte. Ltd.
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ISBN 978-981-4800-64-8 (Hardcover)
ISBN 978-1-000-68088-1 (eBook)
Contents
Preface
1. M odelin g of Nanomaterials for Safety Assessment: xv
From Regulatory Requirements to Supporting
Scientific Theories 1
Lara Lamon, David Asturiol, Karin Aschberger,
Jos Bessems, Kirsten Gerloff, Andrea-Nicole Richarz,
and Andrew Worth
1.1 Introduction 2
1.2 Information Requirements for Risk Assessment:
Legal Provisions and Guidance 3
1.2.1 Chemical Substances under REACH 4
1.2.2 Cosmetic Products 7
1.2.3 Biocidal Products 8
1.2.4 Plant Protection Products 9
1.2.5 Food Production 9
1.3 Risk Assessment 10
1.4 Properties That Drive NM Behavior
(Fate and Toxicity) 13
1.4.1 Theories Underlying Environmental
and Biological Fate 22
1.5 Understanding NMs’ Fate and Toxicity 26
1.5.1 Theorie s Underlying Environmental
and Biological Fate 26
1.5.1.1 Agglomeration and
aggregation kinetics in fluid
media 27
1.5.1.2 DLVO theory 27
1.5.1.3 Smoluchowski–Friedlander
theory 30
1.5.1.4 Fractal approaches 33
1.5.2 Human Kinetics 34
1.5.2.1 Preabsorption processes 34
1.5.2.2 Absorption 37
vi Contents
1.5.2.3 Distribution 39
1.5.2.4 Metabolism/Dissolution/
Transformation/Bio-nano
interaction 39
1.5.2.5 Excretion 41
1.5.2.6 Elimination (sum of
solubilization and excretion) 42
1.5.3 Toxicodynamics 42
1.6 Standard Test Guideline Methods for Toxicity
Testing 46
1.7 Alternative Approaches to Animal Testing 49
1.7.1 Adverse Outcome Pathways 50
1.7.2 In silico Methods 51
1.7.2.1 Supervised and unsupervised
methods 51
1.7.2.2 QSAR/QSPR 54
1.7.2.3 Validation of QSARs for
regulatory purposes 58
1.7.2.4 Expert systems 58
1.7.2.5 Applicability of QSAR/QSPR
approaches to NMs 59
1.7.2.6 Physiologically based kinetic
modeling 60
1.7.3 In vitro Methods 64
1.7.4 Grouping and Read-Across 69
1.7.5 Weight of Evidence 71
1.7.6 Integrated Approaches to Testing
and Assessment 73
2. 1Cu.8r rent CDoenvcelluodpimnge nRtesm anardk Rse commendations in 75
Computational Nanotoxicology in View of Regulatory
Applications 99
Andrea-Nicole Richarz, Lara Lamon,
David Asturiol, and Andrew P. Worth
2.1 Introduction 100
2.2 Computational Nanotoxicology Research
Project Landscape 101
2.2.1 European Nanosafety Research
Activities 101
Contents vii
2.2.2 Related International Activities 123
2.3 Challenges and Needs for the Development
and Use of Computational Methods 125
2.4 Progress against the Challenges and Needs 128
2.4.1 Results from EU FP7-Funded
Research Projects 128
2.4.2 Horizon 2020 Research Projects 132
2.4.3 Other Activities 135
2.5 Conclusions from the Research Landscape
Review 136
2.5.1 Conclusions on the Needs Addressed 136
2.5.2 Recommendations for Nanosafety
Research 137
2.6 Overall Conclusions on the Availability and
Applicability of Computational Approaches
for Nanosafety Assessment 138
2.6.1 Inherent Scientific Uncertainties 140
2.6.2 Data Quality and Variability 140
2.6.3 Model Landscape and Regulatory
Relevance 142
2.6.4 Model Accessibility and Visibility 142
2.6.5 Practicality of Performing
Read-Across for Nanomaterials 144
3. Physicoc2h.6em.6 icaNl Pereodp feorrti Iensf oraf sNtrauncotmuraet erials from 145
in silico Simulations: An Introduction to Density
Functional Theory and Beyond 157
Laura Escorihuela, Alberto Fernández, Robert Rallo,
and Benjamí Martorell
3.1 Introduction 158
3.2 Classic Density Functional Theory:
Jacob’s Ladder 161
3.2.1 Local Density Approximation 163
3.2.2 GGA and Meta-GGA 163
3.2.3 Hybrid Functionals 165
3.2.4 The LUimits of Classic DFT 166
3.3 Beyond GCWlassic DFT 168
3.3.1 DFT+ 168
3.3.2 170
viii Contents
3.3.3 Density Functional Tight Binding 171
3.3.4 LS-DFT 174
3.3.5 Time-Dependent DFT 175
3.3.6 Implicit Solvation Models 176
4. 3B.i4o nanoC Ionnteclruadctiinogn sR:e Am Kaeryk st o Mechanistic 180
Understanding of Nanoparticle Toxicity 189
David Power, Stefano Poggio, Hender Lopez, and
Vladimir Lobaskin
4.1 Introduction 190

4.2 Advanced Descriptors of the Bionano Interface 190
4.2.1 ProteinCorona 190
4.2.2 Nanoparticle Descriptors and QSARs 192
4.2.3 Biomolecule Descriptors 193
4.2.4 Interaction Descriptors 194

4.3 Multiscale Modeling of the Bionano Interface 194
4.3.1 GeneralMethodology 194
4.3.2 Coarse-Grained Protein Model 196
4.3.3 Coarse-Grained Nanoparticles 197
4.3.4 Generation of Surface Pair Potentials 198
4.3.5 Generation of the Core Potential 201
4.3.6 Calculation of the Adsorption Energy 202
4.3.7 From United-Atom to United–Amino
Acid Description 203
4.4 Application of the Method 204
4.4.1 Protein Descriptors 204
4.4.2 Bionano Interface Descriptors 205
4.4.3 United–Amino Acid Model 208
5. 4Fr.5o m MCoodnelcilnugs iNoannsoparticle–Membrane Interactions 210
toward Nanotoxicology 217
Karandeep Singh, Qingfen Yu, Sabyasachi Dasgupta,
Gerhard Gompper, and Thorsten Auth
5.1 Particles at Membranes 218
5.1.1 Penetration vs. Wrapping 218
5.1.2 Chemically Specific vs. Generic Models 219
5.1.3 Nanoparticle-Wrapping Endpoints 220