International workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“ Ljubljana, 2nd and 3rd October 2015 PROCEEDINGS/ZBORNIK PREDSTAVITEV International workshop/Mednarodni sestanek COMPUTATIONAL APPROACHES IN NANOSCIENCES: „COMPinNANO“ Ljubljana, 2nd and 3rd October 2015 ZBORNIK PREDSTAVITEV/PRESENTATIONS Založnik in organizator/Publisher and organizer: Biotehniška Fakulteta, Oddelek za Biologijo, Večna pot 111, 1000 Ljubljana Uredniki in recenzenti/Editors and reviewers: Anita Jemec (Slovenija), Damjana Drobne (Slovenija), Alok Dhawan (India), Rishi Shanker (India) Organizacijski odbor:/Organisational committee: Anita Jemec, Barbara Drašler, Neža Rugelj, Veno Kononenko, Monika Kos, Sara Novak, Damjana Drobne Oblikovanje/Design: Barbara Drašler and Anita Jemec Kraj in leto izdaje/Place and date of issue: Ljubljana, 2015 Brezplačen izvod/Free copy Dostopno/Available: http://www.bionanoteam.com Financed by: EU FP7 project NanoValid (grant no. 263147); NanoMILE (grant no. 320451) Zaščita dokumenta/Copyright © 2015 Biotehniška Fakulteta, Oddelek za Biologijo Vse pravice pridržane. Reprodukcija po delih ali v celoti na kakršenkoli način in v kateremkoli mediju ni dovoljena brez pisnega dovoljenja avtorja. Kršitve se sankcionirajo v skladu z avtorsko pravno in kazensko zakonodajo. • CIP - Kataložni zapis o publikaciji Narodna in univerzitetna knjižnica, Ljubljana 004.6:620.3(082) INTERNATIONAL Workshop on Computational Approaches in Nanosciences (2015 ; Ljubljana) Proceedings [Elektronski vir] = Zbornik predstavitev / International Workshop on Computational Approaches in Nanosciences - COMPinNANO, Ljubljana, 2nd and 3rd October 2015 ; [uredniki Anita Jemec ... et al.]. - El. knjiga. - Ljubljana : Biotehniška fakulteta, Oddelek za biologijo, 2015 ISBN 978-961-6822-33-6 (pdf) 1. Jemec, Anita 283801856 Foreword The production of nanomaterials has led to growing public and regulatory concern about their safety. The aim of a number of ongoing or past EU projects has been to address innovative, safe and sustainable nano-enabled products. The NanoValid project, as one of them, has developed a set of reliable reference methods and materials for their fabrication, physicochemical characterization, hazard identification and exposure assessment. However, since the experimental toxicological testing of nanomaterials is costly and time-consuming, it is necessary to develop a new approach based on knowledge, methods and tools to reach the goal of predictive nanotoxicology. The workshop “Computational approaches in Nanosciences” hosted the participants from three large scale EU FP7 projects NanoValid, NanoMile, and Modern in order to disseminate the results on computational methods for NP hazard characterisation and exchange ideas with representatives from regulatory bodies and industry. Modellers, computational scientists, experimental (eco)toxicologists and experts form different fields of nanoscience discussed the future of Computational Approaches in Nanosciences. The participants agreed that the most crucial points in the data sharing among academia, industry and regulatory bodies are (i) the organisation of data in large data sets and synchronised communication between different fields and sectors. Calibration and validation of computational models is impossible without utilizing high quality experimental data. Therefore, close collaboration between the computational chemists and experimentalists from different areas (i.e. toxicologists, specialists on characterization) would be crucial for the success. The NanoValid project has significantly contributed toward defining the quality of nanotoxicity data and harmonization of test. In the future they have to be integrated into datebases and shared. Namely, new approaches for the data gap filling are needed which have to be dynamic and consider scientific aspects and new developments in nano-sciences. The participants concluded that the future of nanosafety will rely on both, experimental data and computational methods but we have to adopt successful communication strategies. prof. dr. Damjana Drobne and assist. prof. Anita Jemec Ljubljana, 6.11.2015 Friday, 2nd October 16.15 – 16.30 Welcome speach & Introduction to the workshop Prof. Dr. Damjana Drobne, Assist. Prof. Anita Jemec TOPIC 1: ORGANISATION OF LARGE DATA SETS FOR MODELLING IN GENERAL The topic will deal with the organisation of large data sets using different statistical approaches. 16.30 – 17.00 Assist. Prof. Ddr. David Bogataj, The Mediterranean Institute for Advanced Studies: „Reproducible Research“ 17.00 – 17.30 Assist. Prof. Dr. Cene Fišer, Biotechnical Faculty University of Ljubljana, Slovenia: „Web-databases in systematics and taxonomy“ 17.30 – 18.00 COFFEE BREAK TOPIC 2: DATA SHARING-Academia, Industry and Regulatory bodies The stakeholders will present their view on the nanoscience data sharing. 18.00 – 18.30 Mag. Vladimir Vrečko, Cinkarna Celje (TiO2 producers), Slovenia: „The importance of data interpretation and dissemination for the future of nanotechnology.“ 18.30 –19.00 Dr. Mojca Kos Durjava, National Laboratory of Health, Environment and Food and Mag. Karmen Krajnc, Chemical Office of the Republic of Slovenia, Slovenia: „Chalenges of Regulation and Risk Assessment of Nanomaterials“ Saturday, 3rd October TOPIC 3: QUANTITATIVE NANOSTRUCTURE-ACTIVITY (PROPERTY) RELATIONSHIPS (QNAR, QNPR) MODELLING 9.00 – 9.30 Prof. Dr. Marjan Vračko, National Institute of Chemistry, Slovenia: „Chemometric analysis and QSAR modelling in NANO-toxicology“ 9.30 – 10.00 Dr. Villem Aruoja, National Institute of Chemical Physics and Biophysics, Estonia: „Toxicity of metal oxide nanoparticles to algae, bacteria and protozoa: FP7 project MODERN“ 10.00 – 10.30 COFFEE BREAK and poster session TOPIC 4: MODELLING OF INTERACTIONS BETWEEN NANOMATERIALS AND BIOLOGICAL MODELS Presentation of different databases aimed to collect information about Nanosafety related topics. 10.30 – 11.00 Prof. Dr. Alok Dhawan, CSIR-Indian Institute of Toxicology Research, Lucknow, India: „Toxicity of Nanomaterials: The need for Novel Computational Tools and Approaches in Safety Assessment“ 11.00 – 11.30 Dr. Lokesh Baweja, Institute of Life Sciences, Ahmedabad, Gujarat, India: „Computational approaches to understand the interaction of nanomaterials with biomolecules“ 11.30 – 12.00 Dr. Fabrice Carnal, Institut F.A. Forel, University of Geneva, Switzerland: „Monte Carlo Modelling of Interaction Processes between Nanoparticles and Biomacromolecules of Variable 12.00 – 12.30 Hydrophobicity“ Maja Sopotnik and Prof. Dr. Kristina Sepčić, Biotechnical Faculty, University of Ljubljana, Slovenia: „Interaction of carbon-based nanomaterials with cholinesterases and serum proteins“ 12.30 – 13.30 LUNCH BREAK and poster session Saturday, 3rd October TOPIC 5: ORGANISING DATA FOR RISK ASSESSMENT/DATA QUALITY Prof. Dr. Rishi Shanker, Institute of Life Sciences, Ahmedabad, Gujarat, India: 13.30 – 14.00 „Tracking Nano-footprints in microbial food chain: Observations, Data & Challenges“ Dr. Ashutosh Kumar, Institute of Life Sciences, Ahmedabad, Gujarat, India: 14.00 – 14.30 „Importance of in silico approaches in understanding the interactions of nanoparticles with biological membrane“ 14.30 – 15.00 Assist. Prof. Dr. Anita Jemec, Biotechnical Faculty, University of Ljubljana, Slovenia: „Quality of nanotoxicity data and the importance of harmonization“ 15.00 – 16.00 COFFEE BREAK, poster session and open discussion REPRODUCIBLE RESEARCH ddr. David Bogataj MEDIFAS Reproducible research Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The goal of reproducible research is to tie specific instructions to data analysis and experimental data so that scholarship can be recreated, better understood and verified. In the instructions the complex of conditions should be described precisely. Replication The ultimate standard for strengthening scientific evidence is replication of findings and conducting studies with independent: - Investigators - Data - Analytical methods - Instruments Replication • Replication is particularly important in studies that can impact broad policy or regulation decisions What is Reproducible research? • Scientific Question • Research Protocol • Nature (complex of conditions, entities and their relations) • 1. Measured data (dataset regarding realization of an experiment – raw data) – Data processing code • 2. Analytic data (cleansed data) – Analytical code • Computational results – Presentation code (figures, tables, Numerical Summaries) • Published article (text) Up to now in published articles only short overview of data and code (1 and 2) is presented which is not enough. What problem does Reproducibility solve • Black Box Problem • Transparency • Data Availability • Software/Methods Availability • Improved Transfer of Knowledge Reproducibility • The premise of reproducible research is that with data/code available, researchers can check each other and the whole system is self- correcting • Addresses the most “downstream” aspect of the research process – post-publication • Assumes everyone plays by the same rules and wants to achieve the same goals (i.e scientific discovery) Who Reproduces Research? For reproducibility to be effective as means to check validity, someone needs to do something - Re-run analysis; chech if resulta match - Check the code for bugs/errors - Try alternate aproaches/check sensitivity Validation of the data analysis Availability: • Description of complex of conditions in which the experiment run • Data • Algorithems Reasons: • Other people can run the same algorithms on the sama data and can come to the same conclusions as the researcher What you do not have • Independent data • Independent method Impact of new technologies • Allow us to collect data at much higher throughput • We can get very complex high dimensional datasets almost instantaneously • Computing power that allows us to merge databases in even bigger „megadatabases“ Minimum standards • Experiment(observation) should be described in exactly known complex of conditions. • If the complex of conditions is changed (environment), realization of the experiment can give different results. • It could lead to different conclusions. Problems • Authors must undertake considerable effort to put data/results on the web • Readers must download data/results individually and piece together which data go with which code section, etc • Authors/readers must manually interact with websites • There is no single document to integrate data analysis with textual representations; data, code, and text are not linked Simplify the process Put the data and the code together in the same document • People can execute code in the rights order • Data are read at the right times Document integrates data analysis with all the textual representations (descriptions) that everythings is linked together How do I Make My Work Reproducible • Decide to do it (ideally from the start) • Keep track of things, perhaps with versioning control system to track snapshots/changes • Use statistical software whose operation can be coded (R) • Don’t save output (store raw dataset) • Save raw data and the process that get you there • Save data in non-proprietary formats (ASCII) Single research report document • To document the analysis and • to have the code of the analysis in the same document Documentation of total observation and research process • Documentation preparation system – description of research process including metadata (LaTex) • Description of complex of conditions in which the experiment run • Raw data - dataset • Code – Programing Language (R) for data analysis, statistics, forecasting, optimization, sensitivity analysis and simulations. Literate Statistical Programming with knitr (R) • Text and code all in one place, logical order • Data where results of observations are automatically updated that reflect external changes • Code is live – you need to run code. When error appears it needs to be resolved. CONCLUSION 1.- Reproducibility brings transparency (wrt code+data) and increases transfer of knowledge. Therefore complex of conditions which describe the environment for observation procedures have to be very clearly described. 2.- Important currant discussion is about how to convince researchers to share data. The owners of data should have incentives to publish the dataset, metadata and code -by the system similar as IF evaluation (citation index) of their publications and - market system like available by ScienceDirect, Springer and others where the databases are able to buy or sell if there is not obligatory open system. - The founder of a research should clearly determine which data should be publicly available and which are available on the data market - for sale. Web-databases in systematics and taxonomy Cene Fišer SubBio Lab Odd. za Biologijo Biotehniška Fakulteta Ljubljana, 2.10.2015 Univerza v Ljubljani taxonomy-systematics? 1. Taxonomy in a narrow sense: describing and naming species. 2. Taxonomy in a broad sense: species identification, research of species biology, distribution and conservation. 3. Systematics: inference of species relatedness and hierarchical categorization of species in higher taxonomic categories. an ancient science? Modern synthesis: grounded in evolutionary theory…. Speciation: a process of divergence species concept (what is species?) delimitation criteria (how can I tell apart?) + data species hypothesis De Queiroz, Kevin (2007) 'Species Concepts and Species Delimitation', Systematic Biology, 56:6, 879 - 886 …..interdisciplinary science TYPE: NO LIMITS mating behavior communication LIMITS IN DATA DEPOSITION AND RETRIVAL molecular data distribution-ecology morphology Challenges for taxonomy The discipline will have to reinvent itself if it is to survive and flourish. This discipline is made for the web: it is information-rich and often requires copious illustrations. Godfray, H.C.J. 2002 Challenges for taxonomy. Nature 417, 17-19 (Commentary; reply to correspondence arising: “Towards taxonomy’s ‘glorious revolution’” Taxon specialized websites: not necesserily databases Make your own website: Scratchpads: http://scratchpads.eu/ …moving to level of an individual : increase in strength of analyses Analysis I : Analysis II : Analysis III : moleclar/morphological spatial circumstances of ecological niche differentiation speciation reconstruction downolad spatial downolad spatial downolad sequences, data data quantitative or continous data + + + geomorphological Climate data newly measured characteristcs (Hijmans et al. 2005, data J.climat) analysis analysis gene-flow analysis of ecological barries similarity Taxon specific interactive database SubBio Database: interactive database of subterranean fauna Three main sources of data: - species lists - distributional data - DNA sequences Sources of data: - published sources - own data Quantity: - number of records: 30.300 - number of localites : 7.500 - number of taxa: 4.800 Taxon specific interactive database SubBio Database: interactive database of subterranean fauna Taxon specific interactive database SubBio Database: interactive database of subterranean fauna Web-database as a collaborative tool? Morphology: deposition of figures – deposition of measurements Quantitative data: MYSQL Web-database as a collaborative tool? Morphology: deposition of figures – deposition of measurements Quantitative data: MYSQL SEVERAL IMPEDIMENT: - taxon specific - requires pre-defined protocols - individual level - unintentionally limits exploration A CHALLENGE: - making links with other databases (GeneBank, spatial data) Taxon specific interactive database Strenghts: Weakness: - precise, a possibility to - nature of characters correct errors may limit extent of the - standardized protocols database (few taxa) - different properties linked with voucher specimens - eases team work Global databases – character specific Species names on a web: a chaos Wikipedia (SLO): Niphargus - 206 taxa Zoobank : Niphargus 8 taxa EOL: Niphargus – 204-309 taxa Niphargus – 295 taxa Niphargus – 261 taxa Global databases – character specific GeneBank: respository of sequences http://www.ncbi.nlm.nih.gov/genbank/ Main impediment: species id Global databases – character specific GBIF's vision: "A world in which biodiversity information is freely and universally available for science, society and a sustainable future.„ http://www.gbif.org/ - It provides a single point of access (through this portal and its web services) to more than 570,000,000 records, shared freely by hundreds of institutions worldwide, making it the biggest biodiversity database on the Internet. - The data accessible through GBIF relate to evidence about more than 1.6 million species, collected over three centuries of natural history exploration and including current observations from citizen scientists, researchers and automated monitoring programmes. - More than 1,400 peer-reviewed research publications have cited GBIF as a source of data, in studies spanning the impacts of climate change, the spread of pests and diseases, priority areas for conservation and food security. About one such paper is published each day. - Many GBIF Participant countries have set up national portals using tools, codes and data freely available through GBIF to better inform their citizens and policy makers about their own biodiversity. Global databases – character specific Main impediments: - species id - completness of data for selected taxon - quality of coordinates Global databases – character specific Strenghts: Weakness: - different taxa in one - difficult or impossible place, a promising to revise errors source of information - poor connection for ecology and between databases evolutionary biology dealing with different types of characters Global issues 1. Quality of database-quality of data. 2. Linking the taxon oriented databases with character oriented databases: increase of accuracy and number of taxa. 3. Promotion database publishing: - Citation problem - Rewarding in funding agencies - Role of journals 4. Collaborative network – stimulating collaborators Thanks… Maja Zagmajster Martin Turjak Roman Luštrik The rest of SubBio lab team Taxon specialized websites: not necesserily databases What is the name of this creature? Salticid spiders http://salticidae.org/salticid/main.htm Global databases – character specific Morphology: deposition of figures – deposition of measurements MorphBank http://www.morphbank.net/ Global databases – character specific Morphology: deposition of figures – deposition of measurements MorphBank http://www.morphbank.net/ Beginnings of the databases… Special software packages: DELTA, Lucid Description Language for Taxonomy (DELTA) http://delta-intkey.com/ A TOOL FOR: - descriptions - identifcation keys (different types) - files for phylogenetic/phenetic analysis - comparison of taxa MAIN IMPEDIMENT: limited analytic frame, species – population level Beginnings of the databases… Special software packages: DELTA, Lucid Description Language for Taxonomy (DELTA) http://delta-intkey.com/ SOURCE OF INFORMATION THAT CAN BE INCLUDED: - discrete and continous morphological characters - illustrations and photos - sonograms - videoclips The importance of data interpretation and dissemination for the future of nanotechnology Mag. Vladimir Vrečko 12.10.2015 PE Titanov dioksid 1 Future of nanotechnology 2 2 12.10.2015 R&D Hope you enjoyed and thank you for your attention! 3 3 12.10.2015 R&D Public point of view • Yet another potentialy dangerous technology. • Industry pursues its own interests and can not be trusted. • Research which is funded by private funds can not be trusted. • Even scientists don‘t agree with each other, so there is obviously something wrong. • Public media informs us of the research results that prove nanotechnology is dangerous. • We don‘t want the technology untill we are sure it is safe. 4 4 12.10.2015 R&D Scientists point of view – Material scientists • We have discovered numerous new materials and developed their applications. • Everyone would benefit from their use. • We don‘t see, why industry does not recognise the potential and finance us extensively. • If we could just get money, we would have immediate success on market (everyone would buy our products). • The potential risks, costs, availability of the materials and public opinion are not our concern. • Yet another proof that we are not understood and appropriately valued. 5 5 12.10.2015 R&D Scientists point of view – Risk Assessment groups • We got financed to reveal the safety risks. • Public expects us to prove we are independent. • To prove that nano materials are dangerous is therefore a success and worth publishing. • Such findings are great for PR (ensures headlines and recognition of the research group). • If the material or technology does not manifest risky properties, it is a disapointment and not worth mentioning. • Only if we reveal new safety risks will we have a chance for future funding of our research. 6 6 12.10.2015 R&D Industry point of view • We are interested in new technologies, but we take decisions based on economy. • We can manage the costs, however market is still underdeveloped because the risks and legal frameworks are not yet sufficiently defined. • Public opinion is very sceptical and media are not in favour, publishing frightening stories. • Customers and investors don‘t want to take premature financial risks. • Can it happen to nanotechnology that it will follow the footsteps of GMOs? 7 7 12.10.2015 R&D How about that: Scientific research title: Titanium dioxide nanoparticles induce DNA damage and genetic instability in vivo in mice. Title in newspaper: Scientists say nanoparticles cause DNA damage. Implications for the customers: Be aware of the products that contain nanoparticles. Demand that all products containing nanoparticles shall be labeled (as dangerous?). Don‘t buy such products as they can damage your DNA (scientist have said so). 8 8 12.10.2015 R&D What is needed? • Industry needs stable and known market conditions and legal boundaries. • It is willing to take reasonable precautonary measures, but doesn‘t want unpredictable environment for investments. • Scientists should be aware of their responsibility at shaping public opinion. • They should publish also the results in which they confirm that some nanomaterials do not pose safety risks. • Market will grow only if all the stakeholders will feel secure. 9 9 12.10.2015 R&D What is needed? • Scientist shall pursue balanced and objective approach and help regulatory bodies at designing legal frameworks. • Scientists shall not forget that they are after all financed from the money which is mainly created by the industry. • Scientists and industry shall join forces and through public media present nanotechnology as mature technology, which has many advantages. And, normaly, some potential risks, which we can however control through joint activities of researchers, legal bodies and manufacturers. 10 10 12.10.2015 R&D What is needed? We shall persuade all stakeholders that they have nothing to be affraid of. 11 11 12.10.2015 R&D How shall we act? So? Or so? 12 12 12.10.2015 R&D Hope to see you in bright future! 13 13 12.10.2015 R&D Chalenges of Regulation and Risk Assessment of Nanomaterials Part 2: ECHA - NMWG Dr. Mojca Kos Durjava COMPinNANO, Ljubljana, 2.-3.10.2015 Risk Assessment of Nanomaterials EC and OECD - Risk assessment of nanomaterials can be managed through existing regulatory frameworks, adapted to take into account the specific properties of manufactured nanomaterials. REACH, CLP - EC is modifying some of the technical provisions in the REACH Annexes - amendments in 2016 Biocides – Biocidal Products Regulation (BPR), 2013: contains a definition of nanomaterial. Dr. Mojca Kos Durjava: Nanomaterials Working Group at ECHA 2 Risk Assessment of Nanomaterials EC launched a comprehensive REACH Implementation Project on Nanomaterials (RIPoN) in 2009: • RIPON1 : Substance Identity • RIPON2 : Information Requirements • RIPON3 : Chemical Safety Assessment CARACAL is an expert group which advises the European Commission and ECHA on questions related to REACH and CLP. CASG nano - Competent Authority Subgroup on Nanomaterials • Nanomaterials in REACH • Classification, Labelling and Packaging of Nanomaterials in REACH and CLP. Dr. Mojca Kos Durjava: Nanomaterials Working Group at ECHA 3 OECD - WPMN Working Party on Manufactured Nanomaterials - WPMN • is linked to the implementation of REACH - TG and guidance. OECD and WPMN SG on Risk Assessment and Regulatory Mitigation SG- AP • SG on Exposure and Exposure Mitigation Exp. Mitigation • SG on the Environmentally Sustainable use of Nanomaterials LCA • SG on Testing Assessment of Manufactured Nanomaterials SG-TA:  TG update  Assessment of data  In vitro work  Alternative methods (e.g. read-across) Dr. Mojca Kos Durjava: Nanomaterials Working Group at ECHA 4 ECHA - NMWG NMWG – Nanomaterials Working Group The aim – to discuss topical scientific and technical issues relevant to the implementation of REACH, CLP and Biocidal Product Regulation in relation to nanomaterials. From 2013, 7th meeting in November 2015, chair Frank Le Curieux from ECHA 50 or more participants at meetings, twice a year: • MSCA – Member States Competent Authorities • European Commission (DG ENT and DG ENV) • DG Joint Research Centre • ECHA-NMWG Accredited Stakeholders Observers (ASO) • ECHA representatives • Invited speakers from industry, science Dr. Mojca Kos Durjava: Nanomaterials Working Group at ECHA 5 ECHA - NMWG NMWG – Nanomaterials Working Group • ECHA presentations on their work on NM • Industry presentations of dossiers of registered NM (10 NM – cerium oxide, calcium carbonate, zinc oxide, multi-wall nanotubes, titanium dioxide,…) • Scientists presentations on their research in the field of NM (NanoREG, NanoValid, Marina,…) • Other presentations Discussion, working in groups. Dr. Mojca Kos Durjava: Nanomaterials Working Group at ECHA 6 ECHA - NMWG ECHA guidance on RA of NM Already developed by ECHA: a technical manual on how to include information on nanomaterial in a IUCLID dossier which is an integral part of every REACH registration. REACH amendments of Annexes (VII-X) for nanomaterials  ECHA guidance on RA of NM before 2018 registration deadline • ECHA guidance (updating) • Practical guides/examples Advice and expertise from the NMWG. Dr. Mojca Kos Durjava: Nanomaterials Working Group at ECHA 7 ECHA - NMWG IUCLID 6 IUCLID is is a software application to capture, store, maintain and exchange data on intrinsic and hazard properties of chemical substances. Report generator for Chemical Safety Report - CSR. „Assessment entity“ concept – a feature for IUCLID 6 • Enable transparent reporting of hazard, use and exposure information for NM. Multiple composition or multiple forms of the same substance. Many constituents within the substance. Variable composition of the substance. Forming of tranformation products on use. Dr. Mojca Kos Durjava: Nanomaterials Working Group at ECHA 8 ECHA - NMWG Read across and grouping of NM Colaboration of ECHA, JRC, NanoREG and RIVM. Many nanoforms on the market: • Are studies from one form applicable to other nanoforms? • How and when can data be used on nanoforms or beetwen non-nano and nanoforms? Case studies presented; Develop a decision framework; Link to the OECD discussion; Template for reporting on read – across. Dr. Mojca Kos Durjava: Nanomaterials Working Group at ECHA 9 ECHA - NMWG Read across and grouping of NM Dr. Mojca Kos Durjava: Nanomaterials Working Group at ECHA 10 ECHA - NMWG Read across and grouping of NM Dr. Mojca Kos Durjava: Nanomaterials Working Group at ECHA 11 ECHA - NMWG Read across and grouping of NM Dr. Mojca Kos Durjava: Nanomaterials Working Group at ECHA 12 ECHA - NMWG Read across and grouping of NM • The focus of the framework is on Pchem data as well as their relation to the different steps in the life cycle and biological pathway (exposure, kinetics, hazard). • Identify Pchem that matter and provide strategy for systematic assessment. • Needs to be validated in practice. • Future implementation of growing experience and understanding on behaviour, fate, toxicokinetics and toxicity. ECHA guidance will be developed on read across. Dr. Mojca Kos Durjava: Nanomaterials Working Group at ECHA 13 ECHA - NMWG NMWG group • until guidances for REACH and CLP are developed. National Laboratory of Health, Environment and Food Dr. Mojca Kos Durjava mojca.durjava@nlzoh.si +386 24500 234 Dr. Mojca Kos Durjava: Nanomaterials Working Group at ECHA 14 Challenges of Regulation and Risk Assesment of Nanomaterials part.1 Karmen Krajnc, M.Sc. Chemicals Office of the Republic of Slovenia 2 EU – strategic documents  Towards a European Strategy for Nanotechnology 2004  European Nanotechnology Action Plan 2005-2009  Implementation Reports 2007, 2009  Code of conduct for nanotechnology research 2008  Communication from the Commission to the European Parliament, THE Council and the European Economic and Social Committee 2008 (first regulatory review) 2012 (second regulatory review) http://ec.europa.eu/research/industrial_technologies/pdf/policy/communication-from-the- commission- second-regulatory-review-on-nanomaterials_en.pdf 3 Definition (Commission Recommendation 2011/696)  ‘Nanomaterial’ means a natural, incidental or manufactured material containing particles, in an unbound state or as an aggregate or as an agglomerate and where, for 50 % or more of the particles in the number size distribution, one or more external dimensions is in the size range 1 nm-100 nm.  In specific cases and where warranted by concerns for the environment, health, safety or competitiveness the number size distribution threshold of 50 % may be replaced by a threshold between 1 and 50 %.  By derogation, fullerenes, graphene flakes and single wal carbon nanotubes with one or more external dimensions below 1 nm should be considered as nanomaterials. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2011:275:0038:0040:en:PDF  Consideration of possible changes to this definition https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/towards-review-ec- recommendation-definition-term-nanomaterial-part-3-scientific-technical 4 Cosmetic regulation(1223/2009)  definition:‘nanomaterial’ means an insoluble or biopersistant and intentionally manufactured material with one or more external dimensions, or an internal structure, on the scale from 1 to 100 nm.  colorants, preservatives and UV-filters must be authorized (positive lists: Annexes IV, V and VI)  titanium dioxide (nano), zinc oxide (nano), tris-biphenyl triazine (nano) and carbon black (nano) shal be added shortly  notifications of safety information for nano substances(art.16)  labeling: „nano“ 5 Biocidal regulation (528/2012)  specific approval of nano form of biocidal substance (art. 4(4))  labeling obligation for treated articles, art. 58(3): the name fol owed by the word ‘nano’ in brackets  nano-definition 6 REACH-nanomaterials registered  Carbon black  Cerium dioxide  Calcium carbonate  Zinc oxide  Silver  MWNT (multi-wall nanotubes)  MWNT as a form of graphite  Titanium dioxide  Silicate(2-), hexafluoro-, disodium, reaction products with lithium magnesium sodium silicate 7 Substance evaluation  Substances selected for CoRAP (Community rolling action plan) based on initial grounds of concern: evaluated by member states, coordinated by ECHA  Silicon dioxide (synthetic amorphous silica - SAS) – the Netherlands, 2012 http://echa.europa.eu/documents/10162/a94c8df7-81c5-4946-80ae-dfa9275897e1  Silver – the Netherlands, 2012 (ongoing)  Titanium dioxide – France, 2015 (not started yet) 8 OECD Working Party on Nanomaterials  TESTING PROGRAMME ON MANUFACTURED NANOMATERIAL http://www.oecd.org/chemicalsafety/nanosafety/testi ng-programme-manufactured-nanomaterials.htm  Cerium oxide  Multi-wal ed carbon nanotubes (MWCNTs)  Single-walled carbon nanotubes (SWCNTs)  Dendrimers  Nanoclays  Fullerenes (C60)  Silicon dioxide  Gold nanoparticles  Silver nanoparticles  Titanium dioxide, Zinc oxide (not publicly available yet) 9 REACH NANO legislative challenges  Definition of „nanomaterial“  REACH Annexes (registration purposes)  Discussion on possible EU NANO DATABASE 10 Chemicals Office of the RS- nano activities  Nanoportal  Identified 30 companies (producers, users of nanosubstances)  Ag, Zn, Al, TiO2, soot, ZnO, SiO2, Fe.H2SO4, Si, Al2O3, graphite-C, SiC,Cu, CdS, FeCl3, B, Fe, Cr, CuO, MoSi, MoS2, Wox  Coatings/Surface modification,  Pigments,  Polymers/Composits,  Cosmetic,  Medical devices & Medicinal products,  Textiles,  Electronics.  Awareness raising 11 Nanoportal http://www.uk.gov.si/ 12 13 Awareness raising  http://www.kemijskovaren.si/files/nano_knjiga.pdf Additional links:  http://ec.europa.eu/nanotechnology/index_en.html,  http://ec.europa.eu/environment/chemicals/nanotech/index_en. htm  http://echa.europa.eu/regulations/nanomaterials  https://ec.europa.eu/jrc/en/scientific-tool/jrc-web-platform- nanomaterials  https://ec.europa.eu/jrc/en/scientific-tool/nanohub  http://ec.europa.eu/health/nanotechnology/policy/index_en.htm 15 Thank you for your attention http://www.uk.gov.si/ karmen.krajnc@gov.si tel:01/478 6051 Chemometric analysis and QSAR modelling in NANO-toxicology Marjan Vračko National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia, marjan.vracko@ki.si Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Chemometric analysis and QSAR modelling in NANO-toxicology Outline 1. Introduction 2. New toxicological paradigm 3. QSAR (Qantitative Structure-Activity Relationship) working sheme and strategy 4. NANO-descriptors 5. Chemometrical analysis of proteomic data Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 New paradigm in toxicology... Exposure: Molecular Organelle Cellular Tissue Organ Organism Population Function of event event event event event event event Dose and time In chemico In vitro In vivo Clinical, Epidemiological observations Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 OECD document… Adverse Outcome Pathway (AOP) Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Example: skin sensitization (chemicals)… Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 In silico methods… • In silico methods are important to aquire the data for AOP scheme… • Under term In silico we understand different computational modelling approaches; molecular mechanics, dynamics, QSAR, modeling of ADME properties, and also chemometrical analysis of (proteomics, genomics, metabonomics) data,… • MOLECULES - NANO particles? Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 QSAR, Read accross (grouping) in REACH and ECHA Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 NANO and EU Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Scheme of QSAR – strategy How to extract the hidden knowledge from DATA SET DATA BASE Information about molecules Information on toxicity and properties Different details about experiments Molecular structures Uniformly presented toxicity or property •Experimental (X-ray, NMR…) •dose •Optimized (different approximations) •Affiliation to toxicity class, mode of action, .. Physical/chemical properties (logP,MW) Descriptors QSAR Model is mathematical relationship between descriptors and toxicity (property) QSAR model must be VALIDATED! Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Scheme of QSAR – strategy How to extract the hidden knowledge from DATA SET? QSAR hypothesis: The property is a function of chemical structure! The validated QSAR model can be used to predict the property for a ‘new - hypothetical’ compound. ONLY FOR THE PROPERTY FOR WHICH IT WAS TRAINED. Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Two main application areas of QSAR models Active substances (drug) research: 1. Searching for new lead compounds 2. Searching for better analogues Regulatory assessment: 1. Predicting of ‘missing’ values for risk assessment 2. Categorisation of compounds for labeling 3. Priority setting Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 NANO: Scheme of QSAR – strategy How to extract the hidden knowledge from DATA SET DATA BASE Information about molecules Information on toxicity and properties Different details about experiments Molecular structures Uniformly presented toxicity or property •Experimental (X-ray, NMR…) •dose •Optimized (different approximations) •Affiliation to toxicity class, mode of action, .. Physical/chemical properties (logP,MW) Descriptors QSAR Model is mathematical relationship between descriptors and toxicity (property) QSAR model must be VALIDATED! NANO? Molecules – NANO particles ?? The properties of NANO particles (strongly) depend on their the size and the shape. An accent is placed is on descriptors. Gold nanoparticles are red to black. Red glass (Wikipedia): First produced in late Roman Empire. The knowledge was lost and rediscovered in the 17th century by either Johann Kunckel in Potsdam or by the Florentine glassmaker Antonio Neri in Italy. Chemist and winner of the 1925 Nobel Prize in Chemistry Richard Adolf Zsigmondy was able to understand and explain that small colloids of gold were responsible for the red colour. Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Representation of a molecule – How to apply for NANO? Different levels of the representation of molecules: • 1D - Information on constituents (which atoms, or which groups of atoms) • 2D - Structural formula gives information on atoms and bonds between atoms, but no information on metrical parameters (distances between atoms, angles between bonds) • 3D - Coordinates of all atoms (information on all metrical properties) • Quantum chemical descriptors are calculated from QC results – they describe the electronic properties • Structure can be described by fragments • Etc. Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 1D descriptors Molecules (constitutional): Information on constituents: • Number of atoms • Number of particular atom groups (fragments) • … NANO particles: • Size • Shape • Chemical constitution • … Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 2D descriptors - Topological indices Topological indices are numbers deduced from structural formula of a molecule (2D representation). NANO particles: • Shape indices • Analysis from electron microscopy pictures. Ghorbani, Modjtaba. Computing Wiener index of C-24 fulleren. Journal Of Computational And Theoretical Nanoscience, 2015,12, 1847-1851. Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Example: Wiener index (name proposed by H. Hosoya) is an integer number deduced from structural formula (graph). Molecule W Wiener index for cyclobutane: Methane 0 Propan 4 Cyclopropane 3 N-butane 10 1 2 Isobutane 9 Cyclobutane 8 4 3 W = d12 + d13 + d14 +d23 +d24 +d34 = 1 + 2 + 1 +1 + 2 + 1 = 8 Wiener index has a high degeneracy: different non-isomorfic graphs have the same W. Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Atempts to lower the degree of degeneration: • Hyper-Wiener index • Szeged index • Three-dimensional Wiener index • Hosoya’s index Z Second generation topology indices They are real numbers deduced from graphs. One of the most successful is molecular connectivity index c proposed by Randic:  = Sij(vivj) -1/2 Sum runs over edges (bonds), v is the vertex degree on the endpoints. Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Calculation of connectivity index for 3-methylheptane  = S (v v ) -1/2 ij i j 1 1  577 . 0 1*3 3 1  333 . 0 2 1 1  3*3 1 577 . 0  500 . 0 1*3 2* 2 3 1 1 1  408 . 0  707 . 0 3* 2 2 1* 2 1  577 . 0 1*3 1   3* 577 . 0  333 . 0  408 . 0  500 . 0  707 . 0  679 . 3 Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 3D descriptors Three-dimensional structure of molecules is not unanimously defined. Rigid molecules are rare, most of the molecules are flexible. A molecule can have a different 3D structure in vacuo, in crystaline form, in water environment, or in protein environment. Experimental determinations: • X ray diffraction measurements • 2D-NMR measurements – method enables determination of ligand- receptor geometry Theoretical determination: • Quantum chemical optimization • Molecular dynamics Result (3D structure) depends on the selected method. Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 A Flavonoid derivate: 3D structure determinated with rule based generator Tautomer of 7-hydroxy-2-phenyl-4-benzopyrone C1(=CC=CC=C1)C3=CC(C2=C(CC(C =C2)=O)O3)=O Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 A Flavonoid derivate: 3D structure determinated with rule based generator or with Molecular Mechanics Optimization Rule based system MM optimization Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 A Flavonoid derivate: 3D structure determinated with rule based generator or with QC program GAUSSIAN (HF-6-31g approximation) Input # HF/6-31G(d) opt Test1 0 1 C 0 -0.809889 -2.481124 0.027570 C 0 -0.809889 -3.818124 0.027570 C 0 0.347987 -4.486624 0.027570 C 0 1.505863 -3.818124 0.027570 C 0 1.505863 -2.481124 0.027570 C 0 0.347987 -1.812624 0.027570 C 0 0.347987 -0.475624 0.027570 O 0 -0.853279 0.151261 0.027570 C 0 -0.722044 1.499890 0.027570 C 0 0.450229 2.142809 0.027570 C 0 1.574749 1.394039 0.027570 C 0 1.371375 0.058435 0.027570 C 0 -2.020291 2.245149 0.039936 C 0 -1.793136 3.659064 -0.435801 C 0 -0.663327 4.173114 0.051266 C 0 0.479580 3.479435 0.039418 O 0 2.679662 1.882333 0.027570 O 0 -2.545154 4.254249 -1.170300 H 0 -1.762517 -1.931124 0.027570 H 0 -1.762517 -4.368124 0.027570 H 0 0.347987 -5.586624 0.027570 H 0 2.458491 -4.368124 0.027570 H 0 2.458491 -1.931124 0.027570 H 0 2.266345 -0.581120 0.027570 H 0 -2.427220 2.263510 1.075717 H 0 -2.745599 1.738385 -0.635258 H 0 -0.671772 5.189531 0.471773 H 0 1.444041 4.008410 0.039418 Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 A Flavonoid derivate: 3D structure determinated with rule based generator or with QC program GAUSSIAN (HF-6-31g approximation) Original Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 A Flavonoid derivate: 3D structure determinated with rule based generator or with QC program GAUSSIAN (HF-6-31g approximation) Midle point Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 A Flavonoid derivate: 3D structure determinated with rule based generator or with QC program GAUSSIAN (HF-6-31g approximation) Final Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Quantum chemical descriptors HF calculations are time consuming. For our molecule from Gaussian output: . . ON A TOMBSTONE, "HERE LIES LESTER MOORE, FOUR SLUGS FROM A 44, NO LES, NO MORE". Job cpu time: 0 days 1 hours 26 minutes 23.4 seconds. File lengths (MBytes): RWF= 58 Int= 0 D2E= 0 Chk= 6 Scr= 1 Normal termination of Gaussian 09 at Thu Jun 28 17:48:11 2012. Approximations to HF (semiempirical methods) : AM1, CNDO, MINDO, etc. Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Quantum chemical descriptors They are calculated from eigenvalues and eigenvectors of Hartree-Fock equation, or alternatively, from an approximation to it (AM1, CNDO, MINDO, etc.) Eigenvalues are molecular orbital energies. According Koopmans’ theorem: Ionisation potential = -EHOMO Electron affinity = -ELUMO Gap = ELUMO - EHOMO Eigenvalues are used to calculate the charge distribution and descriptors related to it (dipole moment and higher moments). Example: CODESSA calculates about 300 quantum chemical descriptors. Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Quantum chemical descriptors Gaussian output Our molecule: 1. Optimized geometry 2. EHOMO = -0.33343 Hartree, ELUMO = 0.06510 Hartree 3. Multipole moments: Electronic spatial extent (au): = 5332.5111 Charge= 0.0000 electrons Dipole moment (field-independent basis, Debye): X= 5.2339 Y= -1.4411 Z= 0.0844 Tot= 5.4293 Quadrupole moment (field-independent basis, Debye-Ang): XX= -101.3395 YY= -116.5999 ZZ= -103.6957 XY= -7.8858 XZ= 1.5863 YZ= 4.5278 Traceless Quadrupole moment (field-independent basis, Debye-Ang): XX= 5.8722 YY= -9.3882 ZZ= 3.5160 XY= -7.8858 XZ= 1.5863 YZ= 4.5278 Octapole moment (field-independent basis, Debye-Ang**2): XXX= 127.9448 YYY= -41.1843 ZZZ= 1.2466 XYY= 66.4476 XXY= 59.6734 XXZ= 2.9705 XZZ= -14.6642 YZZ= -3.5759 YYZ= -1.1079 XYZ= 6.4639 Hexadecapole moment (field-independent basis, Debye-Ang**3): XXXX= -5387.5410 YYYY= -1605.9836 ZZZZ= -157.2575 XXXY= -384.5756 XXXZ= 75.5886 YYYX= -15.4537 YYYZ= 32.9132 ZZZX= -3.2400 ZZZY= -4.7132 XXYY= -1293.9863 XXZZ= -1012.2994 YYZZ= -250.1014 XXYZ= 57.5382 YYXZ= -5.9352 ZZXY= 21.7033 Etc. Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Quantum chemical descriptors in NANO A promised area in NANO QSAR, possible descriptors: • gaps, • proton, electron affinity, • surface charges, • electronic properties related to reactivity (formation of metal cations). Problems: • NANO particles are large, poorley defined systems. Ab initio calculations require large scale computer abilities. I. Lynch et al. A strategy for grouping of nanomaterials based on key physico- chemical descriptors as a basis for safer-by-design NMs. Nano Today, 2014, 9, 266-270. Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Electron distribution of graphene Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Descriptors - résumé • Descriptors are parameters, which represent the molecular structure in QSAR model • Different chemical and physical parameters can be used as descriptors (logP, solubility, etc.) • Thousands of descriptors can be calculated from chemical structures • Dozens of programs are available (commercial and free) to calculate the descriptors (DRAGON, CODESSA, POLLY, MDL, PETRA…….) • We are far from clear NANO-QSAR concept…. • Far from NANO particle design (hypothetical NP ?) E. Burello, A. Worth. Predicting toxicity of nanoparticles. Nature Nanotechnology, 2011, 6, 138-139. Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 NANO data bases relevant for toxicity assessment N. Jeliazkova et al. The eNanoMapper database for nanomaterial safety information. Beilstein J. Nanotechnology, 2015, 6, 1609-1634. Data bases relevant for toxicity assessment: • http://www.nanomaterialregistry.org • http://www.nanoparticlelibrary.net • http://nbi.oregonstate.org • http://cananolab.nci.nih.gov/caNanoLab/ • http://www.internano.org • http://icon.rice.edu/report.cfm • http://ncl.cancer.gov • http://www.napira.eu • http://nanopartikel.info • http://nanowerk.com • http://www.nanosafetycluster.eu/ Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Chemometrical analysis of -omic data.. In vitro study: 1. Cells are treated with different NANO particles… 2. The -omic status is measured (ca 3000 proteins pro measurements) 3. Statistical relevant -omics are selected? Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Pilot study 1: Biodescriptors-2D proteomic maps Study : M. Vracko, S. C. Basak. Similarity study of proteomic maps. Chemometrics and Intelligent Laboratory Systems 70 (2004) 33–38 Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Data from: N.L. Anderson, R. Esquer-Blasco, F. Richardson, P. Foxworthy, P. Eacho, The effects of peroxisome proliferators on protein abundances in mouse liver, Toxicol. Appl. Pharmacol. 135 (1996) 75– 89. Similarity indices between control map and treated maps are reported: N a b 1 , z z a b The similarity index: s   i i a b N 2 max( , ) 1 z z i i i Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Study 2:Information on biological status of cells biodescriptors, proteomics M. Vracko, S. C. Basak, K. Geiss,F. Witzmann. Proteomic Maps-Toxicity Relationship of Halocarbons Studied with Similarity Index and Genetic Algorithm. J. Chem. Inf. Model. 2006, 46, 130-136. • The comparison of proteomic maps obtained from hepatocytes, which were treated 14 halocarbons. • Six biological endpoints were determined in vitro: EC50 , LEC , EC20 , MIT ROS SH EC50 , LEC , LEC LDH LP CAT • From each map 263 spots were taken to study the similarity among maps. • The similarity between two maps was expressed with similarity index. • The clustering structure is graphycally presented with hierarchical clustering method • With genetic algorithm we selected proteins related to endpoints. Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Information on biological status of cells biodescriptors, proteomics N a b 1 , z z a b The similarity index: s   i i a b N 2 max( , ) 1 z z i i i control 111- 112- 112- 12-C2Br2H4 12- 12-C2Cl2H4 C2Cl3H C2Cl4 C2Cl4H2 CBr2H2 CBrClH2 CCl2H2 CCl3H CHBr3 C2Cl3H3 C2Br3H3 C2Cl3H3 C2BrClH4 control 1.0000 0.8614 0.8529 0.7890 0.8323 0.8380 0.8540 0.8816 0.6426 0.8438 0.6850 0.6972 0.7220 0.5177 0.8748 111- 0.8614 1.0000 0.8270 0.7762 0.8128 0.8419 0.8426 0.8576 0.6685 0.8577 0.6715 0.6736 0.6914 0.5205 0.8498 C2Cl3H3 112- 0.8529 0.8270 1.0000 0.7812 0.7699 0.8542 0.8209 0.8413 0.6458 0.8484 0.6553 0.6751 0.6890 0.5081 0.8451 C2Br3H3 112- 0.7890 0.7762 0.7812 1.0000 0.8091 0.7777 0.8251 0.7521 0.6801 0.7607 0.6718 0.6585 0.6685 0.5404 0.7637 C2Cl3H3 12- 0.8323 0.8128 0.7699 0.8091 1.0000 0.7866 0.8688 0.7926 0.6483 0.7842 0.6822 0.6725 0.6737 0.5431 0.7865 C2Br2H4 12- 0.8380 0.8419 0.8542 0.7777 0.7866 1.0000 0.8279 0.8409 0.6287 0.8400 0.6383 0.6489 0.6489 0.5007 0.8300 C2BrClH4 12- 0.8540 0.8426 0.8209 0.8251 0.8688 0.8279 1.0000 0.8081 0.6620 0.8208 0.6839 0.6851 0.6847 0.5269 0.8195 C2Cl2H4 C2Cl3H 0.8816 0.8576 0.8413 0.7521 0.7926 0.8409 0.8081 1.0000 0.6281 0.8332 0.6618 0.6900 0.7025 0.5050 0.8746 C2Cl4 0.6426 0.6685 0.6458 0.6801 0.6483 0.6287 0.6620 0.6281 1.0000 0.6473 0.6390 0.6243 0.6187 0.5812 0.6519 C2Cl4H2 0.8438 0.8577 0.8484 0.7607 0.7842 0.8400 0.8208 0.8332 0.6473 1.0000 0.6328 0.6694 0.6837 0.5008 0.8619 CBr2H2 0.6850 0.6715 0.6553 0.6718 0.6822 0.6383 0.6839 0.6618 0.6390 0.6328 1.0000 0.6712 0.7298 0.6413 0.6657 CBrClH2 0.6972 0.6736 0.6751 0.6585 0.6725 0.6489 0.6851 0.6900 0.6243 0.6694 0.6712 1.0000 0.7162 0.5275 0.6750 CCl2H2 0.7220 0.6914 0.6890 0.6685 0.6737 0.6489 0.6847 0.7025 0.6187 0.6837 0.7298 0.7162 1.0000 0.5355 0.7084 CCl3H 0.5177 0.5205 0.5081 0.5404 0.5431 0.5007 0.5269 0.5050 0.5812 0.5008 0.6413 0.5275 0.5355 1.0000 0.5048 CHBr3 0.8748 0.8498 0.8451 0.7637 0.7865 0.8300 0.8195 0.8746 0.6519 0.8619 0.6657 0.6750 0.7084 0.5048 1.0000 Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Hierarchical clustering of 14 halocarbons + control 1-SimilarityIndex between two maps Two carbon atoms One carbon atom Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 M. Vracko, S. C. Basak, K. Geiss,F. Witzmann. Proteomic Maps-Toxicity Relationship of Halocarbons Studied with Similarity Index and Genetic Algorithm. J. Chem. Inf. Model. 2006, 46, 130-136. Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Laboratory for chemometrics Thank you for your attention! Researchers Young researchers Emeritus •Marjana Novič •Alja Plošnik Prof. Jure Zupan • Prof. Milan Randić Marjan Tušar •Jure Borišek • Natalja Fjodorova •Lidija Avsenik •Marjan Vračko •Špela Župerl •Viki Drgan •Nikola Minovski •Katja Venko Workshop on COMPUTATIONAL APPROACHES IN NANOSCIENCES „COMPinNANO“, Ljubljana, 2. - 3. October 2015 Toxicity of metal oxide nanoparticles: FP7 project MODERN Villem Aruoja, PhD Laborartory of Environmental Toxicology, National Institute of Chemical Physics and Biobphysics, Tallinn, Estonia Our zoo Particle ingesters „Particle-proof“ Eukaryotes Prokaryotes Crustaceans Crustac C e r a ustaceans Protozoa Algae Yeast Bacteria Bacte B ri a a cteria Daphnia Themnocephalus Tetrahymena Pseudo- Saccharomyces Vibrio fischeri Escherichia coli magna platyurus thermophila kirchneriella cerevisiae subcapitata Primary Consumers Decomposers producers human and animal cell lines (human alveolar epithelial cells A549, human epithelial colorectal cells Caco2 , murine fibroblast cell line Balbc/3T3) Villem Aruoja, NIPB, Estonia Slide by Anne Kahru MODeling the EnviRonmental and human health effects of Nanomaterials Villem Aruoja, NIPB, Estonia QNAR BIOLOGICAL DATA DESCRIPTORS M O D E L VALIDATION (hypothesis testing) Villem Aruoja, NIPB, Estonia Data? • Literature? • Industry? Andre Nel*: “You have 2,5 years left, start generating data now!” * Director of University of California's Center for Environmental Implications of Nanotechnology Villem Aruoja, NIPB, Estonia Flame Spray Pyrolysis Villem Aruoja, NIPB, Estonia Selection of nanoparticles • CuO, Co O , Sb O , TiO , WO , ZnO, 3 4 2 3 2 3 Mn O , Fe O , MgO, Al O , SiO , Pd 3 4 3 4 2 3 2 (Initial subset in project description: 23 metal oxides: ZnO, CuO, Co O , Fe O , Sb O , TiO , 3 4 3 4 2 3 2 WO , Al O , CeO , Y O , CoO, Ni O , Cr O , 3 2 3 2 2 3 2 3 2 3 Fe O , HfO , In O , SnO , ZrO , Gd O , La O , 2 3 2 2 3 2 2 2 3 2 3 Mn O , NiO, Yb O and Ag, Au, Pt) 2 3 2 3 Villem Aruoja, NIPB, Estonia Villem Aruoja, NIPB, Estonia FSP – small and crystalline NPs Villem Aruoja, NIPB, Estonia Aruoja et al. Env Sci: Nano, 2015, DOI: 10.1039/C5EN00057B Preparation of MOx NPs suspensions MQ water sonication 25 ml 4 min. 5 mg Branson Villem Aruoja, NIPB, Estonia Digital sonifier Size, hydrodynamic size BET size DI water Algal growth medium (pH=8.0) ( d ) BET Specific Metal solubility Metal solubility Sample surface area z-average % at 10 mg/l % at z-average ζ- % at 10 mg/l % at (SSA); m2/g ζ-potential, nm hydrodynamic pH (AAS or ICP- 200 mg/l hydrodynamic potential, (AAS or ICP- 100 mg/l mV size, nm MS*) (TXRF) size, nm mV MS*) (TXRF) ZnO 53 20.4 171 16.4 6.6 56.1* 5.0 696 -13.1 25.7 3.18 Pd 33 15.1 127 -27.8 6.1 <0.5 NA 151 -18.6 0.24 NA CuO 72 13.1 130 17 6.2 5.14 0.88 769 -6.2 1.16 0.26 Co 85 11.5 99 23 6.1 1.25 6.8 916 10.7 0.18 0.82 3O4 TiO 123 12.2 171 -13.6 6.2 <0.83 0.10 717 -15.1 0.42 0.01 2 Mn 81 15.2 395 -14.4 7.0 11.1 4.8 920 -9.8 9.45 6.62 3O4 Fe 120 9.7 128 22.2 5.9 <1.38 7.1 1005 -12.1 1.66 0.17 3O4 Al 134 11.4 95 39.2 6.0 0.40* NA 1232 8.9 0.42 NA 2O3 SiO 289 7.8 148 -33.2 6.0 NA NA 154 -19.8 NA NA 2 WO 79 10.6 63 -45.3 5.0 63.2* 2.3 191 -20.4 66.7 75.7 3 MgO 123 13.6 1964 6.9 9.6 38.1 NA 1581 6.4 87.9† NA Sb 56 20.5 125 -24.3 4.2 56.3 NA 414 -15.9 21.2 NA 2O3 Villem Aruoja, NIPB, Estonia Aruoja et al. Env Sci: Nano, 2015, DOI: 10.1039/C5EN00057B Stability of suspensions in MQ water vs algal test medium In MQ water: 1 week 100 mg/L In algal test medium (OECD 201): 1 day. Composition of the algal test medium. Component mg/L 1 NH Cl 15 4 2 MgCl *6H O 12 2 2 3 CaCl *2H O 18 2 2 4 MgSO *7H O 15 4 2 5 KH PO 1,6 2 4 6 NaHCO 50 3 7 Na EDTA*2H 0 0,1 2 2 8 FeCl *6H O 0,08 3 2 9 H BO 0,185 3 3 10 MnCl *4H O 0,415 2 2 11 ZnCl 0,003 100 mg/L 2 12 CoCl *6H O 0,0015 2 2 Villem Aruoja, NIPB, Estonia 13 Na MoO *2H O 0,007 2 4 2 Aruoja et al. Env Sci: Nano, 2015, DOI: 10.1039/C5EN00057B 14 CuCl *2H O 0,00001 2 2 Algal growh inhibition assay (OECD 201) • Primary producers • Sensitive to toxicants • Very sensitive to heavy metals 100 µm Villem Aruoja, NIPB, Estonia 13 EC50 Villem Aruoja, NIPB, Estonia Aruoja et al. Env Sci: Nano, 2015, DOI: 10.1039/C5EN00057B TiO – 8mg/l 2 TiO2 1,4000 1,2000 1,0000 0,8000 0,6000 0,4000 0,2000 Villem Aruoja, NIPB, Estonia 0,0000 0,00 1,00 2,00 3,00 4,00 5,00 6,00 7,00 8,00 9,00 TiO – 8mg/l 2 Villem Aruoja, NIPB, Estonia TiO – 8mg/l 2 Villem Aruoja, NIPB, Estonia Co O – 3.5mg/l 3 4 Co3O4 1,4000 1,2000 1,0000 0,8000 0,6000 0,4000 0,2000 Villem Aruoja, NIPB, Estonia 0,0000 0,00 0,50 1,00 1,50 2,00 2,50 3,00 3,50 4,00 Co O – 3.5mg/l 3 4 Villem Aruoja, NIPB, Estonia Co O – 3.5mg/l 3 4 Villem Aruoja, NIPB, Estonia Palladium – 5mg/l Pd 1,4000 1,2000 1,0000 0,8000 0,6000 0,4000 0,2000 Villem Aruoja, NIPB, Estonia 0,0000 0,00 1,00 2,00 3,00 4,00 5,00 6,00 Palladium – 5mg/l Villem Aruoja, NIPB, Estonia Palladium – 5mg/l Villem Aruoja, NIPB, Estonia Palladium – passage to clean medium Villem Aruoja, NIPB, Estonia ’Spot’-test Inoculation Bacterial growth Washing with MQ Exposure to MOx NPs 4-48h spot to log phase water Bacterial colonies from Dilution (1:50) of the The cells are harvested by 100 μL of cells suspension and 100 μL of After 4 h or 24 h of exposure, 5 Petri dish are overnight culture in 20 mL centrifugation at 7000 rpm MOx NPs suspension in MQ water were μL of the cell culture from each transferred to 3ml of fresh medium (in 100 mL for 7 min at 20 °C in 50 mL pipetted into the 96-well microplates. well (treated or not treated) liquid LB growth media culture flask) and further centrifuge tubes. After that MQ water without test chemicals was was pipetted (“spotted”) onto and grown overnight in cultivation at 30 °C on a the cells were washed twice inoculated with test strains in parallel the LB agar medium plates and 14 mL round-bottom shaker at 200 rpm for 4-4.5 with MQ water and and served as a control culture (not incubated at 30 °C for 24 h. The polypropylene culture hours until bacteria reach resuspended in MQ water to treated). The microplates were growth of bacteria (formation tube at 30 °C on a mid-exponential growth a density of ~107 CFU/mL incubated for 4 hours or 24 hours at 30 of colonies) was evaluated shaker (200 rpm). phase (OD ~0.6). (OD =0.1). visually on LB agar. 600nm 600 °C in the dark, without shaking. Cells + MOx NPs in MQ (100μl+100μl) Resuspension in MQ water to OD =0,1 (~107 600 CFU/mL) Colonies from Petri Further cultivation in Centrifugation Exposure for 4-48 hours dish and overnight 100 mL flask until and washing at 30 °C in the dark growing at 30oC in mid-exponential with MQ water LB media growth phase 5μL spots to the agar medium plates 4h-48h Suppi et al., J Haz Mat 2015; 286: 75-84 „Spot test“ concentration, mg/l Algal cells after 24h incubation with NPs in deionized water Villem Aruoja, NIPB, Estonia Suppi et al., J Haz Mat 2015; 286: 75-84 Results from 24-h ’spot’-test with bacteria E. coli pSLlux E. coli MG1655 S. aureus WO3 Co O no bacterial 3 4 growth ZnO Sb O 2 3 Pd CuO 3,5DCP Positive control Fe O 3 4 SiO2 Al O 2 3 Mn O 3 4 TiO2 MgO 3,5DCP Positive control Contr. 0,01 0,1 1 10 100 Contr. 0,01 0,1 1 10 100 Co C n o t nrt.r. 0,0 0, 1 01 0,1 11 10 10 10 1 0 00 1000 mg/L Co O , CuO, ZnO, Fe O and Mn O inhibited bacterial colony 3 4 3 4 3 4 Villfeo m r A m ruoj iang , NIP Ba , E bi sto l ni iaty in the ’spot’-test at concentrations ≤ 100 mg/L. Environmental Science: Nano, 2015, DOI: 10.1039/C5EN00057B Kinetic bioluminescence inhibition test (ISO 21338:2010) with Vibrio fischeri Negative control (2% NaCl) Vibrio fischeri e Non-toxic sample (turbid) nc Fe O : 91 mg/L 3 4 e Pd: 91 mg/L sc Toxic sample 1 (turbid) ine Toxic sample 2 (turbid) um 96-well microplate L Time [sec] 1-5 sec 30 sec Villem Aruoja, NIPB, Estonia Slide by Anne Kahru luminometer 4h; WO 4h; CuO 3 24h; 24h; CuO 4h; TiO 2 WO3 4h; 24h; Co O 4h; 24h; 24h; TiO 3 4 2 Co O Fe O Fe O 3 4 3 4 3 4 4h; ZnO 24h; 4h; SiO2 24h; SiO2 4h; MgO ZnO 4h; 24h; 4h; 24h; 24h; MgO Sb O 2 3 Sb O Al O Al O 2 3 2 3 2 3 4h; Pd 24h; Pd 4h; Mn O 24h; Mn O 3 4 3 4 Slide by Monika Mortimer Villem Aruoja, NIPB, Estonia Table 1. Toxicity of metal oxide and Pd nanoparticles to protozoa ( Tetrahymena thermophila) and bacteria ( Vibrio fischeri, Escherichia coli, Staphylococcus aureus). The presented toxicity values are based on nominal initial exposure concentrations used in testing. 1 EC50 - half effective concentration; 2 MBC - minimum bactericidal concentration. The lowest tested concentration that completely inhibited the visible growth of bacteria on the agarized test m Ville e d mi u A m ru oja a t , 3 NI 0o PB C , E i s n to t ni h a e dark after 24-h of incubation to MOx NPs. Colour code: ≤0.1 mg/L ( ); >0.1–10 mg/L ( ); 10–50 mg/L ( ); 50–100 mg/L ( ); =100 ( ) mg/L; >100 mg/L ( ). Aruoja et al. Env Sci: Nano, 2015, DOI: 10.1039/C5EN00057B Mechanism: solubility, ROS Trophic level Primary Consumer Degrader producer Cell type Eukaryote Eukaryote Prokaryote Internalizes nanoparticles No? Yes No? Algal 72 h Protozoan Bacterium Nano- Solubility ROS ROS growth 24 h viability 30 min. lumi- Mechanism of † ¥ § $ + Classification particle (%) (HPF) (DCF) inhibition nescence toxicity OECD 201 ISO 21338 ZnO 56.1 - - < 1 . 0 1 …2 10 ... 50 Zn ions Pd <0.5 + ++ < 1 . 0 >100 10 ... 50 ROS Acute CuO < 1 . 0 5.14 +++* - 1 …2 1 …2 Cu ions & ROS aquatic hazard Co₃O₄ 1 …2 1.25 + + >100 >100 ROS 1 …2 TiO₂ 10 ... 50 >100 <0.83 +++ + ROS 1 …2 Mn₃O₄ >100 >100 11.1 - +++ ROS Acute 1 …2 10 ... 50 >100 Fe ₃O₄ <1.38 +* - ROS aquatic hazard? 10 ... 50 >100 >100 Al₂O₃ 0.40 + - 10 ... 50 >100 >100 SiO₂ NA - - 10 ... 50 >100 50...100 WO₃ 63.2 - - >100 >100 >100 MgO 38.1 - - >100 >100 50...100 Sb₂O₃ 56.3 + - Villem Aruoja, NIPB, Estonia Aruoja et al. Env Sci: Nano, 2015, DOI: 10.1039/C5EN00057B 31 Cell cultures, EC50 Villem Aruoja, NIPB, Estonia Ivask et al. Current Topics in Medicinal Chemistry [2015, 15(18):1914-1929] Endo- cytosis Ivask et al. Current Topics in Medicinal Chemistry [2015, 15(18):1914-1929] Villem Aruoja, NIPB, Estonia Anne Kahru, EUROTOX 2015, Porto, Sept Villem Aruoja, NIPB, Estonia 34 13-16 Monte Carlo Modelling of Interaction Processes between Nanoparticles and Biomacromolecules of Variable Hydrophobicity Fabrice Carnal October 3rd, 2015 CompinNano, Ljubljana Table of Contents I. Introduction II. Simulation method and model III. Results Simplified protein structure • pH • Chain hydrophobicity • Presence of nanoparticle BSA protein • Parametrization • pH • Nanoparticle surface charge density IV. Conclusions and perspectives I Introduction I. Introduction Polyelectrolytes Charged polymers  Functional groups are dissociated to charged monomers and counterions (weak polyelectrolyte charge varies in function of pH) Nanoparticles Objects constituted by tens or hundreds of atoms which have structure sizes comprised between 1 to 100 nm in at least one dimension  Spherical, tubes, needle-like, etc. Organic nanoparticles Inorganic nanoparticles 4 High specific surface  High reactivity I. Introduction Electrostatic interactions in polyelectrolyte systems Self-assembled complexes between polyelectrolytes and nanoparticles, dendrimers, flat surfaces, biomacromolecules, charged polymers Biology DNA packaging in eukaryote cells Biomedical Contrast improvement in Magnetic Resonance Imaging Cancer therapy by accumulation of active drug Environment Wastewater treatment 5 I. Introduction Goal How the chain hydrophobicity is playing a role on the conformational properties of biomacromolecules and on the formation of complexes with nanoparticles. R β m 2 0 Systematic investigations 3 0.3 4 1 5 3 Numerical simulations 6 II Simulation method and model II. Simulation method and model Polyelectrolytes  Several thousands of atoms !  CPU consuming Coarse-grained models Atomistic details (bond length, vibrations, etc.) are omited Group of atoms  effective monomer 8 II. Simulation method and model Biomacromolecules Nanoparticles Neutral monomers Nanoparticle counterions Negatively charged monomers Chain counterions Positively charged monomers  Each object is represented by a hard sphere  The solvent (water) treated implicitly as a dielectric medium  Monomer charge varies depending on the solution pH 9 II. Simulation method and model Potentials 1) Electrostatic Each charged objects interact via a full Coulomb electrostatic potential and excluded volume potential  ,  r AAA  R  R U r   z z e el  ij  2 1 , ij i j i j AA r A  R  R 4 ij i j   r  r 0 ij e Elementary charge rij Distance between two particles  Permittivity of the vacuum R Particle radius 0 Water dielectric constant z Particle charge r 2) Lennard-Jones Lennard-Jones potential is used to take into account hydrophobic interactions between monomers 12 6     r   r  U r  vdW       ij  0 0 2 vdW  r   r     ij   ij    r 0 Usually Ri + Rj 10 vdW Minimum depth of the potential curve located at a distance r 0 II. Simulation method and model Minimum energy investigation Monte Carlo Metropolis  Random conformation  System energy calculation E   U initial ij ij  Elementary Movements  System energy calculation Efinal  Energy difference E   E  E final initial  Metropolis test Several thousands successive trials to achieve equilibrium state Recording of observables (macroscopic properties)  average values 11 II. Simulation method and model Elementary movements Biomacromolecules Counterions Internal movements End-bond Kink-jump Translation Global movements Reptation ‘Partially clothed’ Pivot 12 II. Simulation method and model pH dependency of biomacromolecules Protonation/deprotonation cycle 10’000 MC steps Chain relaxation E   E  E tot final initial Elementary movements Electrostatic, excluded volume and hydrophobic potential Protonation/deprotonation E   E   k T ln 10  pH-p K tot B    0  process Chemical potential N/4 monomers Several million MC steps to obtain a reasonable sampling of low energy conformations 13 C.E. Reed and W.F. Reed, Journal of Chemical Physics, vol. 96, 1992, p. 1609 II. Simulation method and model 2 models Simplified protein structure BSA protein 0 Neutral monomers 0 Neutral monomers -1 Negatively charged monomers -2 -1 Negatively charged monomers 1 Positively charged monomers 2 1 Positively charged monomers A: B: C: Hydrophobic carboxylic groups amino groups  100 monomers  583 amino acids  sequence of monomers randomly determined in  sequence of amino acids known (x-ray the beginning of the simulation structure)  p K A = 2.17 / p K B = 9.53  each amino acid can be neutral, positively and negatively charged  p K A, p K B and p K C (if present) different for each amino acid 14 III Results Simplified protein structure • pH • Chain hydrophobicity • Presence of nanoparticle III. Simplified protein structure Neutral monomers Negatively charged monomers Positively charged monomers A: B: C: Hydrophobic carboxylic groups amino groups highly hydrophobic chains folded conformation within the whole pH range hydrophobic/intermediate chains more folded conformations when the global chain charge is neutral 16 F. Carnal, A. Clavier and S. Stoll, Environmental Science: Nano, vol. 2, 2015, p. 327 III. Simplified protein structure Square radius of gyration 1 N R   r  r i cm 2 2 g N i 1  Extended conformations at low and high pH in the case of hydrophilic and intermediate chains  Strong repulsive monomer-monomer electrostatic interactions favorise less compact conformations 17 III. Simplified protein structure A p K  2.17 0 (carboxylic groups) B p K  9.53 (amino groups) 0 Chain globally neutral!  Charging process of carboxylic/amino groups promoted by charged amino/carboxylic groups  Attractive electrostatic interactions weaker for hydrophobic chains resulting in a less efficient charging process 18 III. Simplified protein structure RNP = 100Å σNP = -39.9 mC/m2 highly hydrophobic chains no elongated conformation no formation of complexes intermediate and hydrophilic chains stronger electrostatic interactions with the NP  adsorption at the surface both amino and carboxylic groups charged at physiological pH resulting in partial chain adsorption 19 F. Carnal, A. Clavier and S. Stoll, Environmental Science: Nano, vol. 2, 2015, p. 327 III. Simplified protein structure  Strong adsorption at low pH  high peak  At physiological pH, local adsorption resulting in a larger monomer distribution 20 III. Simplified protein structure  Lost of symmetry at low pH  intersection with the ideal curve  Modification of the apparent pKa hence promoting the protonation of carboxylic groups 21 III Results BSA protein • Parametrization • pH • Nanoparticle surface charge density III. BSA protein Parameters  length and contour size of the protein  amino acid distribution  pKa, pKb and pKc values BSA x-ray structure 12 6     r   r  U r  vdW       ij  0 0 2 vdW  r   r     ij   ij     hydrophobic interactions between amino acids not too strong to observe conformational changes (denaturation)  vdW = 3.5 K B T  hydrophobic amino acids: Ala, Met, Leu, Val, Ile, Phe 23 III. BSA protein 0 Neutral monomers -2 -1 Negatively charged monomers 2 1 Positively charged monomers NAA = 583 RAA = 2Å physiological pH more folded conformations and no adsorption with only electrostatic interactions protein positively charged at low pH adsorption at the surface of the nanoparticle change of the protein conformation and 24 modification of the charge of the nanoparticle (reactivity) III. BSA protein  Global charge of the protein is slightly negative at physiological pH  Presence of the nanoparticle significantly influences the charging process of the protein 25 III. BSA protein R +R  Ads  R +3R NP AA L NP AA Adsorption criterion One amino acid situated in the adsorption layer (AdsL) more than 50% of Monte Carlo steps  Adsorption domain increases with the nanoparticle surface charge density Prediction of the reactivity of nanoparticles! 26 IV Conclusions and perspectives IV. Conclusions et perspectives • Structure of strong hydrophobic chains not dependent on pH  Denaturation and reactivity limited • The nanoparticle modifies the acid/base properties of chains, and thus the charging process BSA protein • BSA conformations strongly modified at extrem pH and with the adsorption at the nanoparticle surface  Denaturation • No BSA adsorption at physiological pH considering only electrostatic interactions  Importance of hydrophobic and structural interactions between the nanoparticle and BSA Work in progress • Improvement of the interactions between the protein and nanoparticle • Parametrization and validation of the model based on experimental data provided 28 by the University of Ljubljana hank yo for your attention ! Computational Approaches in Nanosciences Workshop Maja Sopotnik October 3rd, 2015 › Adsorption of biological molecules on the surface of the NM › Protein corona – depends on the properites of the NM and the proteins › Change in NM characteristics and behaviour › The need for biological characterisation of NM › Surface adsorption index, 5 nanodescriptors Xia et al. 2011 › Acetylcholinesterase Neurotransmitter acetylcholine degradation › Butyrylcholinesterase – Backup system in the blood › Convenient optical method of measuring the amount of the reaction product – Ellman‘s method Jemec et al., in press › First level – Second level › Third level – Fourth level › Fifth level Jemec et al., in press › First level – Second level › Third level – Fourth level › Fifth level Jemec et al., in press › Graphene oxide › Carbon black › C60 – fullerenes › Multi-walled carbon nanotubes Mesarič et al. 2013 Mesarič et al. 2013 Atomic contacts Hydrogen bonds Mesarič et al. 2013 › Pre-coating with BSA Sopotnik et al. 2015 › Pre-coating with BSA Sopotnik et al. 2015 Sopotnik et al. 2015 › 0,4 % human serum › Intrinsic BChE Sopotnik et al. 2015 › Transport proteins – Serum albumin – Seroransferrin – Apolipoprotein A-I – Apolipoprotein E › Immune system – Complement C3 – Complement C4-A – Copmlement C1q – Immunoglobulins Sopotnik et al. 2015 › AChE inhibition: CB>GO>MWCNT>C60 › GO : AChE adsorption >> AChE inhibition › Binding affinity of NMs for serum albumin was similar in pure albumin solution and in the whole serum › CB and MWCNT have a strong affinity for serum albumin › GO has a weaker affinity for serum albumin but stronger for other serum proteins, including BChE; is less specific › Carbon NM corona is enriched with complement factors and apolipoproteins › Xia, X. R., Monteiro-Riviere, N A., Mathur, S., Song, X., Xiao, L., Oldenberg, S. J., Fadeel, B. & J. E. Riviere, 2011. Mapping the Surface Adsorption Forces of Nanomaterials in Biological Systems. ACS Nano 5(11): 9074-9081. › Jemec, A., Mesarič, T., Sopotnik, M., Sepčić, K. & d. Drobne. Biological characterization of nanomaterials. In press. › Mesarič, T., Baweja L., Drašler, B., Drobne, D., Makovec, D., Dušak, P., Dhawan, A. & K. Sepčić, 2013. Effects of surface curvature and surface characteristics of carbon-based nanomaterials on the adsorption and activity of acetylcholinesterase. Carbon 62: 222-232. › Sopotnik, M., Leonardi, A., Križaj, I., Dušak, P., Makovec, D., Mesarič, T., Poklar Ulrih, N., Junkar, I., Sepčić, K. & D. Drobne, 2015. Comparative study of serum protein binding to three different carbon-based nanomaterials. Carbon 95: 560-572. Date Event title Developing Reference Methods for Nanomaterials www.nanovalid.eu Quality of nanotoxicity data and the importance of harmonization Dr. Anita Jemec University of Ljubljana, Biotechnical Faculty, Department of Biology, Večna pot 111, 1000 Ljubljana, Slovenia 1 This project has received funding from the European Union’s Seventh Programme for research, technological development and demonstration under grant agreement No 263147 What is quality of nanotoxicity data and why we need quality data? Developing Reference Methods for Nanomaterials Krug et al., 2014. Screen the nanotoxicity literature of the last 10-15 years >10 000 publications. Conclusion: „ Most of these studies, do not offer any kind of clear statement on the safety of nanomaterials. On the contrary, most of them are either self- contradictory or arrive at completely erroneous conclusions…„ 2 Multiplicity of variables- an example for in vitro testing Developing Reference Methods for Nanomaterials 3 Developing Reference Methods for Nanomaterials CRITERIA FOR quality of nanotoxicity data NanoValid aimed to develop criteria for good quality nanotoxicity data to be used for RA and LCA - In order to provide a list of criteria three web-based initiatives, one authority document, one list developed with NanoValid, and two scientific papers (Stefaniak et al., 2013, using various sources, Mills et al., 2014) were used a basis. 1. MinChar initiative, providing minimal material characterization recommendations for nanotoxicology studies (http://characterizationmatters.org/parameters/), see Annex 1 2. DaNa critera checklist, providing a list to evaluate nanotoxicity studies regarding their scientific value (http://www.nanopartikel.info/cms/lang/en/Wissensbasis/kriterienkatalog), (Kühnel et al., 2014)) 3. The Nanomaterial Registry's Minimal Information About Nanomaterials (MIAN), https://www.nanomaterialregistry.org/about/MinimalInformationStandards.aspx (Mills et al., 2014), see Annex 4 4. Standard information required for Nanomaterials manufactured or imported (REACH) “Nanomaterials and REACH – Background paper on the position of the German competent authorities”, (UBA, 2013) 5. List of issues and parameters to be specified for ENPs used in toxicological tests under NanoValid, see Annex 3 6. List of relevant nanomaterials properties provided by Stefaniak et al. (2013, see Table 2, page 1329), see Annex 5 UNIFICATION OF CRITERIA NEEDED! NANOVALID CRITERIA FOR quality data Developing Reference Methods for Nanomaterials Physical and chemical Sample preparation properties • Adequate characterization of sample: Dissolution for soluble (ion releasing) nanomaterials • Name of substance (or CAS-No) • Suitable preparation of the sample: Detailed description of • Aggregation / Agglomeration State the dispersing procedure • Shape • Determine size in processed sample, do not rely on producer • Particle Size / Size Distribution (including type of information dispersion medium and additives) • Aggregation / Agglomeration in respective media • form of delivery (powder, suspension) • Dispersibility • Composition (including chemical composition, • Consider age / storage periods of NM powders / elements, element distribution and crystal structure) suspensions for subsequent testing • Purity (including levels of impurities) • Surface Chemistry (including functionalization, reactivity, hydrophobicity) Toxicity testing • Solubility • Surface Area • Determination of exposure concentration (real vs. nominal) • Porosity • Stability—how do material properties change with time, • Density storage, handling, preparation, delivery (aging) • Defect density • Behaviour in exposure media over test duration solubility, • Surface Charge and the rate of material release through dissolution • Stability • Aggregation / Agglomeration in respective media • Conductivity • Dose metrics used (mass, surface-area and number • Magnetic properties concentration in µg/ml, µg/cm2 ; N (particle)/cell or pg/cell) • Surface Reactivity • Controls (positive and negative controls) • Consideration of surface modification: exact • Interferences with test system characterization • Appropriate methods / endpoints 5 • Use of reference material NANOVALID CRITERIA quality of nanotoxicity data Developing Reference Methods for Nanomaterials General aspects Appropiate data evaluation / statistics Standardisation criteria (SOPs used, OECD guidelines, decison trees) Further questions regarding the scientific validity of the data: 1) Are raw data provided by the data source? 2) Were proper controls used and reported? 3) Was the instrument within calibration? 4) How many replicates were performed? 5) Was the measurement protocol reported? 6) Was there a citation to the protocol? 7) Were there modifications made to the cited protocol? 8) Are there specifications regarding the age of the NM? 6 NANOVALID harmonisation efforts Developing Reference Methods for Nanomaterials STEPS in Establishing reference methods CRITERIA for reference method: • sensitive to demonstrate exposure to NPs • the signal to noise ratio have to be high enough • the exposure should be linked to effects, • the test should exhibit good reproducibility, ease of performance, and robustness. CHOICE: Daphnia magna Artemia franciscana Porcellio scaber 7 VALIDATION Developing Reference Methods for Nanomaterials waterflea Daphnia magna Organization of inter-laboratory comparison study University of Ljubljana (UNILJ, Biotechnical Faculty, Slovenia) (reference laboratory), National Institute of Chemical Physics and Biophysics (NICPB, Estonia), The Helmholtz Centre for Environmental Research (UFZ, Germany) Technical University of Denmark 8 (DTU, Denmark) Developing Reference Methods for Nanomaterials RESULTS: Daphnia magna Exactly after SOP developed by NICPB (OECD 2004: No. 202) PARTNER 48h 48h EC50, EC50, AgNPs Ag+ µg/L µg/L 1: NICPB reference 2.50 1.00 laboratory 2: UNILJ 5.37 3.69 3: UFZ 2.39 1.28 4: DTU 4.081 3.21 5: UTA 18.85 2.02 • Good inter-laboratory repeatability VALIDATION Developing Reference Methods for Nanomaterials brineshrimp Artemia franciscana Organization of inter-laboratory comparison study University of Ljubljana (UNILJ, Biotechnical Faculty, Slovenia) (reference laboratory), National Institute of Chemical Physics and 10 Biophysics (NICPB, Estonia), The Centre for Cellular & Molecular Biology (CCMB, India), The Helmholtz Centre for Environmental Research (UFZ, Germany) and Faculty of Chemistry and Chemical Technology (FKKT, UNILJ Slovenia) Developing Reference Methods for Nanomaterials First the harmonised protocol was developed- literature review and experimentation Developing Reference Methods for Nanomaterials Experimental set-up. Negative control: ASW medium Test compounds AgNPs (mg/L): 25 50 75 100 125 Positive control; K Cr O 2 2 7 (mg/L) 15 30 60 Developing Reference Methods for Nanomaterials RESULTS: POTASIUM DICHROMATE Data of the intercalibration study on K Cr O . 2 2 7 a.) CONCLUSIONS: 100 • 1 partner invalid results-high control mortality 90 an (inappropriate cysts storage, prolonged hatching) 80 scaci 70 • LOW Intra-laboratory variation (UNILJ) n fra 60 (relative coefficient of variation): 9.6 %. A. h ile 8 50 • LOW Inter-laboratory variation: 17% b 4r 40 ftea f immo 30 Lab 1 o Lab 2 re 20 Lab 3-1th trial a Lab 3-2nd trial sh 10 Lab 4 e Lab 4 hT 0 Lab 4 Lab 4 -10 Lab 4 GOOD REPEATABILITY WITH CHROMIUM 0 10 20 30 40 50 60 Concentration of K Cr O (mg/L) 2 2 7 Each data point represents mean ± SD (10 repetitions were done for each concentration). Developing Reference Methods for Nanomaterials RESULTS: SILVER NANOPARTICLES Data of the intercalibration study on AgNPs b.) 100 CONCLUSIONS: 90 a • 1 partner invalid results-high control mortality n 80 sca (inappropriate cysts storage, prolonged hatching) ci 70 n • High Inter-laboratory variation fra 60 A. h (relative coefficient of variation): 37 % ile 8 50 b 4r 48h EC50 value was 36,48 mg/L 40 ftea f immo 30 ore 20 a Lab 1 Lab 2 sh 10 e Lab 3-1th trial h Lab 3-2nd trial T 0 Lab 4-1th trial Lab 4-2nd trial -10 Control 25 50 75 100 125 Concentration of AgNPs (mg/L) NOT REPEATABLE. SOURCES OF VARIABILITY? Each data point represents mean ± SD (10 repetitions were done for each concentration). Developing Reference Methods for Nanomaterials IDENTIFICATION OF SOURCES OF VARIABILTY Source of variability Actions taken to diminish the Previous practises in nanotoxicity variability assays Species of Artemia Defined species, A. franciscana Commonly undefined, mostly Artemia sp. or A. salina Origin of Artemia  Producer, which supplies A. Very variable sources, commonly cysts franciscana, Great Salt Lake, undefined USA  Further suggestion: use of certified cysts Hatching medium Defined composition, artificial salt Undefined composition: sea water or PHASE water (SSW)* Instant Ocean®, Red Sea Salt®,  hatching conditions Synthetica Sea salts®, Tropic Marin® – a synthetic sea salt mix CHING  test plate incubation Illumination Continuous light Continuous light, or not reported Temperature Defined temperature: 25 °C 25-30 °C, often not reported HAT Duration Defined duration: 24 h 24 h or 48 h  illumination regime Aeration Defined rate: < 200 bubbles/min, Not defined partners reported that hatching was  Special AgNPs properties unsuccessful at very high rates Hydratation/bleaching Not applied, hatching was successful Various practises, but ussulally not -light dependent Ag+ speciation without this step applied Test medium Defined composition, synthetic salt Undefined composition: sea water or water (SSW)* Instant Ocean®, Red Sea Salt®, TS Synthetica Sea salts®, Tropic Marin® – a synthetic sea salt mix Y TE Age of cysts Additional experiments done, 24 h Various practises: old nauplii (stage I) ensure sufficient 24 h, 30 h, 48 h, not reported survival of controls during 48 h exposure OXICITT Illumination Additional experiments done, Often not reported, Various illumination is extremely important practises: dark, 16 h/8 15 h light/dark in nanotoxicity studies, we suggest regime VALIDATION Developing Reference Methods for Nanomaterials isopods Porcellio scaber Organization of inter-laboratory comparison study Development of protocol UNILJ Concentration tested (µg/g leaf) No. of animals CuO NPS and Cu2+ STOCK Control 10-12 2000 Cu (from CuO NPs) 10-12 total Cu measurement 5000 Cu (from CuO NPs) 10-12 UNILJ Dispatch of 5000 Cu2+ (Cu-salt, Cu(NO ) ·3H O) 10-12 3 2 2 the test -Three rounds: November 2014 UNILJ January 2015 February 2015 TOXICITY TESTING UNILJ UFZ NICPB MCGU EAWAG BAM FKKT October November November November January February 2014 February 2014 2014 2014 2015 2015 2014 ANIMAL AND LEAF CU CONTENT 16 UNILJ Developing Reference Methods for Nanomaterials RESULTS: Porcellio scaber Detailed instructions, video/audio material Animal Cu content- Bioaccumulation Leaf Cu content Conclusions Developing Reference Methods for Nanomaterials • Internationally recognized toxicity test assay the “Feeding assay with Daphnia magna” fulfils the criteria for the reference nanomaterial exposure and effect method. • The assay with Artemia franciscana has a number of advantages as a test organism and fulfils a number of criteria as a reference method. The reproducibility of the assay with the reference chemical K Cr O was good, but this was not the case with 2 2 7 AgNPs. We attribute this to specific properties if these NPs. • The “Feeding assay with isopod Porcellio scaber” has proved to be a reliable and reproducible assay and we therefore suggest it as reference method for terrestrial nanomaterial exposure and effect. We therefore suggest further steps to standardise the protocol. 18 Developing Reference Methods for Nanomaterials Data harmonisation quality sharing GOOD PRACTISE EXAMPLE: http://www.nanoobjects.info/en/ Developing Reference Methods for Nanomaterials ACKNOWLEDGEMENTS To All NanoValid partners involved in round robin. Monika Kos for Artemia assay validation All Bionanoteam for support in analytic and toxicity testing EU FP7 project NanoValid (Development of reference methods for hazard identification, risk assessment and LCA of engineered nanomaterials; grant no. 263147)