Transcript for:
Webinar Insights on NanoEHS and Safety

TODD LUXTON: Good morning, good afternoon, or good evening to everyone. Thank you for joining today’s webinar. My name is Todd Luxton, I am a research chemist with the United States Environmental Protection Agency Office of Research and Development, and I will be the moderator for today’s webinar— What we Know about nanoEHS: Nanoinformatics and Modeling. The NNI’s nanoEHS webinar series focuses on sharing what we now know about environmental health and safety aspects of engineered nanomaterials. This webinar will feature experts from diverse disciplines to share the perspectives on key findings for the topics. Today we're going to be featuring nanoinformatics and modeling. Before introducing our excellent panel of speakers and providing a brief overview, I want to mention that that the NanoEHS webinar series is an important platform for agencies participating in the National Nanotechnology Initiative, the NNI, to share information on nanoEHS research progress and findings. Throughout the series experts will share the big take-home EHS messages with the broader nanotechnology community and highlight the NNI’s role in answering those questions. We've set aside time for answering the panel today. You can type your questions into the Q & A box on the bottom of your screen. We'll try to get through as many questions as we can. I look forward to a lively conversation. Let's briefly introduce our speakers for today. Our first speaker is Andrea Haase. She's the Head of the Fibre- and Nanotoxicology Unit, in the Department of Chemical and Product Safety of the German Institute of Risk Assessment. A biochemist and toxicologist by training, her work since her appointment as Unit Head in 2008 has addressed the integration of nanomaterials in different regulatory frameworks in the EU and conducting nanosafety research. Dr. Haase has been involved in several large national and European nanosafety and governance projects and is a coauthor of the EU-U.S. Roadmap Nanoinformatics 2030. Our next speaker today is Stacey Harper. She's the professor of environmental engineering at Oregon State University. There her lab has worked on developing and applying rapid testing strategies to investigate tools to determine the potential hazards of nanomaterials and nanoplastics and link those to the material properties. Dr. Harper has spearheaded the development of a knowledge base of Nanomaterial-Biological Interactions (NBI) between OSU and the Oregon Nanoscale and Microtechnologies Institute. Next we have Fred Klaessig. Fred is manager of Pennsylvania Bio Nano Systems and co-chair of the US-EU Databases and Computational Modeling for NanoEHS Community of Research. Prior to this, Dr. Klaessig was Technical Director and Business Director for the Aerosil Line at Evonik Degussa. This work led to his involvement in the international standards development organizations such as ASTM International and ISO as well as industry-led organizations. He is a co-editor of the EU-U.S. Roadmap Nanoinformatics

2030. Robert Rallo is Director of the Physical and Computational Sciences Division in the Advanced Computing, Mathematics, and Data Division at the Pacific Northwest National Laboratory. HIs research interests are in data-driven analysis and modelling of complex systems. Prior to joining PNNL, he was an Associate Professor in Computer Science and Artificial Intelligence and Director of the Advanced Technology Innovation Center (ATIC) at the Universitat Rovira i Virgili in Catalonia. Dr. Rallo served as chair for the Modeling WG in the EU NanoSafety Cluster (2013-2016) and as the EU co-chair of the US-EU NanoEHS Human Toxicity Community of Research. Just one final comment before I turn it over to the panel. I hope you'll join us for the other nano EHS webinar in the series and more information on all of the NNI public webinars can be found on nano.gov. You can follow us on Twitter @NNInanonews. With that, we'll turn it over to our first speaker, Andrea. >> ANDREA HAASE: Okay. Thank you, Todd. Thanks for the kind invitation and for the nice introduction. It is my pleasure now that I can provide an overview from the European perspective. Let me just see. I think now you should be able to see my slides. I entitled my presentation, “What do we know about nanoinformatics?” I intend to give you some insight from selected European projects. Of course, it is not a complete list, for time reasons. There's some logos depicted here. I will guide you through. Let me start with a general introduction. I entitled that “Nanosafety: Where are we?” And just starting from the application of nanoparticles or materials, we're all aware of the fact that nanomaterials are used nearly everywhere. There are plenty of applications already on the market and many more under research and development. That means nanomaterials are very complex. We have a lot of different chemistries. We have plenty of forms and variants. Moreover, not only that each of the nanoforms can behave differently, we also, need to consider complex changes of these materials. This picture here is just a simplification. And the reality might be even more complex. There might be dissolution, agglomeration, hetero-agglomeration, changes in the surface chemistry and other properties and reactivity. Bio-nanocoronas and so on. So, all of that renders the material characterization and also a proper dosimetry highly challenging. To sum that up, I think we urgently need modern data-driven approaches to deal with the complexity of these materials. It is not each and every variant and each and every modification can be fully tested for each and every endpoint. We have that modern data-driven approaches, be it AOPs, assessment strategies or integrated approaches to testing and assessment, or safe by design, safe and sustainable by design. All of these modern data-driven approaches they have one aspect in common. They need data, a lot of data. Of course, the knowledge has increased dramatically in the last decades. Knowledge is further increasing. Plenty of information is available. We have a couple of nano-specific database or nanosafety databases. We have a couple of other relevant database that partially store relevant data from the side of nanomaterials. There are other useful resources, for example, like Zenodo or figshare the authors made access to their datasets in parallel to the publication. That means from the perspective of the user, many challenges remain. How can you find relevant information for your material or your application? How can you evaluate the relevance and the reliability of that data and how can you bring together all of these different pieces of information? That means the solution -- in our perspective—that's not only my opinion but this is coming from a couple of collaborators from different European projects—we think we need to push forward towards the FAIR data infrastructure. What do these letters mean? Fair is the abbreviation for making data findable, accessible, interoperable, and reusable. Behind each of the aspects there are a couple of issues. For time reasons, I would like to emphasize the important issues. Findable: The data needs to be enriched with metadata. It needs to be assigned with a unique, globally unique, and persistent identifier. For making data accessible, the metadata needs to be accessible or in particular the metadata needs to be accessible even if the dataset is not accessible or no longer accessible, so that at least the information can be obtained that the particular dataset is available in a specific database such that then access permission can be checked out. For interoperability, it is important to use formal, accessible, shared, and broadly applicable languages ontologies and for making data reusable it is of the utmost importance to have clear and accessible data usage licenses. And this is not a complete list. These are just the important issues. Also we need to keep in mind that making data FAIR means that the data is open. And that each and everybody can just use it for any purpose. So this needs to be clarified and emphasized as well because otherwise people may be reluctant to publicly share the datasets. And this is an issue that has been initiated in a project where I had the pleasure to participate, NanoREG2. This is the publication that came out at the end of the project. We put together our perspective on how the nanosafety data infrastructure can become more FAIR and we shared the experience that we made in the project where we reached out to other finished projects and collected the complete data sets of the project and incorporated them into the nanosafety data interface based on eNanoMapper. As you can see below, this is the current view of the interface. Meanwhile many European projects are using this interface so we think we can say it is one of the largest nanosafety databases that we have worldwide. You can access it via this link here. Specifically looking at the NanoREG2 dataset, you can see the overview here. This is only the tiny fraction of that whole interface. This is only the NanoREG2 database. You can see here the number of data points and the number of methods being represented in the database for particular forms of nanomaterials. You can see here which of the nanomaterials are mostly populated in the database. This is titanium dioxide, for instance; silicon dioxide, multiwalled carbon nanotubes, zinc oxide, silver nanoparticles, to mention some of them. We experienced particular issues for some of the data. It is very often omics data is stored in specific repositories that are not nano-specific. And even so these omics data overall they have a very high FAIR index since the nano-specific descriptors are missing, this is hindering the reuse of this data for the nanosafety community. This is something we initiated already in NanoREG2 by linking transcriptomics datasets, and this is currently ongoing in other European projects where we try to link the datasets and specific omics repositories to eNanoMapper. This is something that we address also in our reply to the publication. You can see below actually the NanoREG2 database all has a very high FAIR score. In the project Gov4nano, this is something that has been taken on and pushed to another level and a FAIR implementation network was established. I will not talk you through the complex slide, but the idea is that we would like to develop the FAIR data ecosystem that allows the various stakeholders, be it citizens, policy makers, industry, researchers, and civil society to get access to high quality trustable data. And this once the nana data has been established and initiative and we also released a video that's available here at the link that explains the idea behind the initiative. And a couple of case studies are currently ongoing. Some of them initiated by Gov4Nano and some of them initiated by an infrastructure project, Nanocommons. And to name some of them —there is work ongoing on persistent identifiers, use on the use electronic laboratory notebooks to upload data more elegantly and easily. We experience the basically the biggest challenge here on our way forward is how to stipulate [that] the researchers deposit their data in such infrastructure. The largest bottleneck is not on the side of the technology, but rather how can we change the mindset and the community? I would like to emphasize two other aspects before I end that I find highly important. This is the standardization of metadata. This is also important to make the relevant information findable and to make data that stored in different databases also interoperable. This is just giving you the general idea. If you have an experiment ongoing; there is a user and operator; there is some detection or measurement ongoing. But before there is some sample pre-processing equipment needs to be described- maybe there is a calibration, and raw data and then you have the property that's measured and there's data analysis and post processing of the data. And for all of that you need metadata that truly describes what has happened to that specimen or to that nanomaterial. This is highly challenging to make it in the standardized manner. This is an initiative that was currently ongoing in the project Nanocommons. For time reasons I will not talk you through the very complex figure. But the idea is -- may be just going one step back. Basically, we work archetypes. We have archetypes for the instrument, chemicals, and all of that should contribute to a standardized metadata and description. What is also needed from the perspective of the user, you don't only need the data, but you need tools to analyze the data. This is another example from NanoCommons. Tools are developed that are then directly linked to the data infrastructure. In this example, it is the NanoXtract: Nanomaterials Image Analysis Tool. I will not talk you through. If you have questions, please feel free to ask me later on. More work is ongoing in other project. Also in NanoCommons they have other tools- NanoSolveIT and NanoinformaTix That brings me to the end. I hope that I could convince you first of all the digitization is highly important and that this will enable and advance data-driven approaches that are urgently needed for the hazard and risk assessment of nanomaterials but also to ensure safe innovation. I tried to give you some insights from selected European projects. I would like to emphasize here on the slide at the AdvancedNano Implementation Network. You need the different stakeholders to buy in to the idea and we need to foster a FAIR data landscape. Also, there are challenges as more material types need to be represented. This is an example. So currently ongoing project like HARMLESS focuses on multi-component materials, PolyRisk focuses on micro and nanoplastics. We need to integrate information from different database like I have shown you with the omics data. We need tools supporting the digitization, the easy upload and the reuse of data. The examples here is the GRACIOUS template wizard that has been established from GRACIOUS or the NanoCommons electronic laboratory notebook case studies. Of course, I would like to thank many collaborators coming from all of these projects for their valuable input. Later on b I'm happy to answer your question. You can paste them now in the Q & A box. Thank you at this point. >> STACEY HARPER: It's okay. I think I'm on now. I didn't know if you were going to introduce me or not, Todd. >> TODD LUXTON: I thought I had but I was on mute. >> STACEY HARPER: Okay. Here I am. For those of you I haven't seen for a couple of years, howdy. I'm still here. I really appreciate you guys bringing back at least getting the gang back together for the panel. I think it is fun just working through how we were going to do this. I'm going to focus my remarks today on what we can draw from the wealth of nanoEHS research that has been conducted over the past decade and a half, the past two decades, and what we can leverage to inform these new materials that Andrea was just talking about and specifically in my world, nanoplastics. When we started this over a decade ago when we were writing the nanoinformatics road map, the U.S.-based one, we really envisioned a path in which we could advance nanoEHS research by leveraging the nanoinformatics. None of us are nanoinformatics people, per se, but we're researchers who saw the need for nanoinformatics to really advance our understanding of nanomaterials and getting to that Holy Grail of “can we predict a materials’ behavior based on the physical-chemical properties of it?” This was like the wild west. This is when we were collecting a few studies that were collected and reported information on things like the agglomeration state, which we now know is critically important. There wasn't much consideration given to instances of characterization. Thinking about, you know, when in the material’s life cycle the data were collected. That's important because we know that transformations can occur. This is what our data pipeline looks like. It was literally a pipeline where we would collect data. Everyone doing their own independent research: collecting data, processing it, and analyzing it, writing up your publication, and then, curators on the backside would have to go in and extract that information back out of publications. You know a lot of negative data is missed that way and not reported on. And wouldn't be considered in the final risk assessment. It is quite biased in some ways. Then the computational analysis of the curated data could happen. But what we did back then, we envisioned what it could be. In Andrea’s overview of the EU’s efforts, these data repositories offer a real “pie in the sky” idealism, a set of interoperable system of Federated databases that could share information and making sure that information was the same. We still have the same old pipeline here where we collect the data and publish it. But, by putting the raw data into– raw data annotated so people can understand it and understand the error and variability in the data and be able to share like materials with other like materials. In this way drive hopefully some predictable models that Robert will be talking about and even inform the next round of study designs. In order to do this and support this effort, the National Cancer Institute Nanotechnology Working Group lead effort for several years. We're trying to adapt the standardized tab- delimited format that was developed by the European Bioinformatics Institute. We're trying to basically adapt it so it could describe nanomaterials. Just the complexity around describing the nanomaterial is immensely complex that we really needed some assistance in thinking about what that was and working through it for, all of the different nanomaterial classes. We basically added a materials file extension on to this ISO tab format. That's the basis for most of the EU efforts. It was at least the starting point back in 20 -- mid 2015 or something like that. This allowed us to catch all the critical physical-chemical properties about the nanomaterials, not just the surface chemistry, but how that surface chemistry is actually attached. We moved this forward to an ASTM [International] standard to make it useful for other people. For informing nanoplastics risk, many of the engineered nanomaterials on the nanoEHS research have been and are commercially available or can be synthesized in small batches to tweak the specific physical or chemical property you want to. I think my last look at nanocomposites web site for ordering materials, they have 23 pages of options. That's not adding in any customization that you could do. A lot is available. Nanomaterials themselves can be precisely engineered for the shape, for the size, or the service chemistry. They are often times available in homogeneous suspensions. We can study these to determine what are the factors that are driving their fate in the environment and also their impact on living systems. Most engineered nanoparticles are produced at the nanoscale. Therefore, the primary particle size is within that nanoscale. Whereas only a few nanoplastics are generated as primary particles. Those are limited primarily to polystyrene and PMMA [polymethyl methacrylate]. Most of the nanoplastics that we're dealing with in the environment are going to be a result of breakdown to macroplastics to microplastics to nanoplastics. From the microplastics research we can say something about the occurrence of these materials. If you look at breakdown from macroscale to microscale plastics, the same trend of the ratio of the different types of plastic that are more prominent should hold at the nanoscale as well. A good hypothesis, nonetheless. So those materials that are commercially available, the polystyrene and PMMA are only available in spherical forms, which is clearly not what we see or will see in the environment. In addition, the surface chemistries are pretty limited to, you know, we can get a positive charge by adding some amine groups, carboxyl groups give it a negative charge, or it could be left undecorated and just be neutral. But this really limits the information that we have on nanoplastics effects, certainly we can't be doing the comparative types of studies that we've been doing in nanoEHS. What can we draw from that nanoEHS to inform plastics risk assessment? I would say all of these environmental transformations that were described eloquently by Greg Lowry in his 2012 paper should hold for nanoplastics; right? I guess here where we have dissolution that would not necessary be the case for nanoplastics because they are persistent. They don't dissolve. They can transport through the environment will likely depend on the agglomeration state like it does for nanomaterials. Both of the processes should be affected by things like salinity or pH, organic matter in the system, So, we know a lot that we can draw from. The formation of the organic matter layer in the environment, or the protein corona in living systems, should also be the same, if the outer most surface chemistry is the same or similar enough. Transport then we can think about that could be similar to nanomaterials, if they had the same density. If you think about nanocellulose or nanolignin, both of those might have a density that's similar enough to nanoplastics, to be useful and informative. On the environmental fate and transport both in terrestrial and aquatic systems, these should be comparable. Again, if they are in the aqueous system, the density is going to be a big driver. As they agglomerate, plastic particles even at the nanoscale may float after agglomerate as opposed to falling into the sediment like you would expect from metal nanoparticles. And if they are un-agglomerated, they can remain suspended in water bodies indefinitely and lead to exposure to organisms that live in or traverse through the water column. Again, there would be no dissolution of the plastics. There have been some concerns about leeching and some of the additives and the stabilizers from the plastic particles. I think a good example of this is the 6PPD [p-Phenylenediamine]-quinone that's released by the tire wear particles. For environmental sampling and quantification, I think we have the same issues in environmental sampling that exist for nanomaterials and nanoplastics. This would be things like the difficulty in, collecting them. For microplastics research, they are not even trying to get down to the nanoscale at this point. Also concentrating the samples. There's interference from the colloids in the system. It is really challenging to try to locate them once they are in a complex matrix. Now dynamic light scattering and nanoparticle tracking analysis can be used for the nanoplastics and nanomaterials. Since both instruments rely on the idea that the particles are spherical, the vast majority of nanoplastics are going to violate this assumption while many of the engineered nanomaterials are engineered to be spheres. One other thing to note is that some of the clear plastics evade the detection system for the nanoparticle tracking. Considerations. Thinking about uptake and translocation what can we glean from nanoEHS? I think the mechanisms of uptake would be very similar. Particularly when you think of this mechanism being a function of size. The smallest nanoparticles here can directly penetrate through the cell membranes. We have particles that are around 100 that can get in through the clathrin-mediated endocytosis. We have 50 to 80 nanometer ones that can get in using caveolar receptors and endocytosis. Those that are larger ones will be mostly taken up but phagocytosis. Now one other consideration is that the biodistribution should be similar if the particles have the same size, shape, and charge; or close enough- we need to know what that distinction is so we can start reading across. The accumulation of nanoplastics could occur in lysosomes because they will not break down even at the very, very low pH environment that's found in the lysosome, so that could be problematic. Lastly, I wanted to touch on what we know about the toxicity relative to nanoEHS. Reactive oxygen species generation is a predominant finding for both nanomaterials and nanoplastics. Although right here where the nanoparticles, some nanoparticles themselves, especially the transition metals can generate ROS on their own. It would be my hypothesis that the nanoplastics would do the oxidative stress through the cellar interactions. Particle specific effects would be expected for both the nanomaterials and nanoplastics. The three main things that have been indicated for both nanoplastics and nanomaterials toxicity are inflammation, oxidative stress, and metabolic disruption. Those are the highlights. Lastly, I want to end with my slide on nanoparticle safety testing to share with you all. With that, I will hand it over to you, Fred. >> FRED KLAESSIG: Thank you, Stacey. I would like to thank Todd for the introduction earlier. I would like to thank the audience for participating or being here. I'm going to be describing the role of dissolution in the current risk assessment models for oral ingestion. Dissolution is an intriguing phenomenon in that dissolution will limit the amount of particles that are present and for toxicology, the dose makes the poison. That can simplify testing considerably. In 2011 when the nanoEHS plan came out, the concern was a particle effect. What I would say is there was the toxicity observed the particle? Was it a dissolution product? And when one goes through the plan, you'll see there are milestones for test method development, applying those test methods to silver which was the case study, and there's a chapter on informatics which combined all of the results into a coherent dataset. This continued when Stacey and I and others were working on the first nanoinformatics roadmap. The situation changed when we got to 2016. Can I have the next slide? When Andrea Haase and I become our work in what eventually become the EU-U.S. Roadmap. In the intervening years there's been more reports on the nanoscale particles dissolution but there are divergent regulatory opinions. I'm using here hydroxyapatite [HAP] as the case study. It is the mineral form of calcium phosphate found in bones and teeth. Paul Westerhoff of ASU [Arizona State University] had documented that nano-HAP was present in baby formula for sale in Australia, which led to a review by the local authority Food Standards Australia New Zealand who took the position it would dissolve in the stomach, therefore no exposure and no action. In Europe at the same time around 2016 the Scientific Committee for Consumer Safety was concerned-- also addressed nano-HAP in toothpaste and mouthwash. They expressed concern about oral safety but did not address dissolution as an effect that should be considered. For Andrea and me, there was an opportunity, progress in literature, divergent opinions–could we bring them altogether? We put this together in the pilot project and roadmap. I've repeated here what the objectives are there. They really come down to identifying stakeholders, bringing them together, and then seeing if we can make advances accordingly. Next slide please. There were, of course, technical reasons that justified such a project. In the background I would say we are dealing with the fact that it was a topic of an unlikely raised issues regarding confidential business information. That means that we could focus on what I call the fourth point here. How do you do test methods that simulate physiological conditions, with solution characteristics, compartment identity, and residence time such that the resulting data are useful from a toxicology standpoint. Certainly, that's the area where I've been engaged with and I've learned from in my interactions with NSF international, who are a public health standard development organization for testing product, certifying them, and auditing them from the use in the water system. Next slide please. Let me kind of give a concrete example. Lead pipes are used in a number of municipalities in the United States. You are supposed to feed phosphate to maintain a phosphate barrier layer. In Flint, the budget was cut, they stopped feeding phosphate. Corrosion occurred. It is a question of whether or not people are being exposed to particles or soluble lead ions. I have here a chart from dissolution study of galena, which lead sulfide, an insoluble form of lead. You can see as it dissolves in gastric fluid, these two curves reach the same plateau. We can assume equilibrium to the right of that vertical line. To the left you can see the approach to equilibrium. Smaller particles reaching equilibrium faster than the larger particles. In essence, the equilibrium is to the right and the kinetics to the left. If you ask me my opinion on one or two PPM of particles in water, I would say they will probably dissolve in the stomach and therefore your exposure is to soluble lead salts. Of course. that has to be demonstrated. Next slide please. That's not always the case when it comes to nanoscale particles. What I have here is cupric oxide, tenorite. You see on the left the plateaus for this material change with the shape. Spindle at the bottom, rods are the red line. Slightly higher, spheres are the higher line. The plateaus do not align with equilibrium. You have the effects of the particle shape. On the right I have concentration. Same particle and different concentrations. I've provided on the right-hand side what the solution concentration was. These profiles are completely kinetic, there's no equilibrium branch. You can go to references for nanosilver, for example where the same is true for those particles; the size of the different plateaus, the dose levels lead to different plateaus. This raises the question of what is a possible explanation. Go to the next slide please. I'm now into the world of geochemistry. This is one of the reasons I have been saying mineralogical names. This is albite which is a sodium aluminum silicate. I'll draw your attention to the picture at the right. The relatively smooth surface. There are some lines that represent ledges. And when you do a dissolution study you are monitoring or measuring the retreat of the ledges, their movement across the field of view. On the left-hand side is a similar material but it is rougher. There are etch pits present. Dissolution is now on ledge movement plus the formation of etch pits, the number of etch pits, and whether the etch pits actually grow. Or how fast they grow. These authors have aligned this with the two different mechanisms, based on the level of under-saturation. Under-saturation is the solubility product re-expressed for activity. What happens in a static system or batch mode test, you start severely under- saturated as the particle dissolves? It starts moving to mechanism one and where it lands starts depending on how it was manufactured or as we would say “engineered” when it comes to nanoscale materials. Did the manufacturing process introduced line defects, point defects, screw dislocations, and the other sorts of elements that come with the crystal growth. I would point out this was for a flow-through system. The previous work was for a batch system. Can I go to the next slide please? This is a series of data from flow through testing on silica particles. Everything on the page is a silica particle and surface treatments may differ. Orange is a lung simulant fluid, Gamble’s solution around pH 7.4. Black is a lysosomal acidic solution around pH 4.5. You can see the shape of the curves differ. On the left, the orange is more likely to have curvature. On the upper right quadrant relatively linear reactions there. This is somewhat confirmed. I you look at where I drew the circle, those are the three materials that did not have any surface treatment, they are somewhat together when it comes to the pH 7 neutral material, but they separate when it comes to the acidic material. Obviously, the kinetics have changed. When you combine these two conditions, you simulate the lung. That's one of the reasons the work was done. What's missing is any plateauing or any asymptote formation indicating a limit. That somewhat makes sense. This is flow through. The under-saturation is controlled and therefore, you can go to completion more readily. I have the next slide please. I would just like to somewhat recap. In 2011 the concern was the particle effect. The actions taken were appropriate. 2016 people are used to the fact that there will be mixtures and those mixtures will change in their relative composition based upon the physiological compartment that you are considering. When you do batch mode, you are probably seeing the under saturation decreasing with time. Asymptotes are more likely to be visible and they may be sample-specific in terms of shape and size. The testing is probably better attuned to compartments, the stomach, or phagocytes. When it comes to flow-through testing on the other hand, under-saturation is fixed over time, the solution is more likely to go to completion. It simulates open systems where fluid is refreshed such as lung lining. The blood stream is also another possibility. In all of this I've talked about the current use of kinetic reaction rate laws that describe the data is disconnected from the degree of under-saturation, thermodynamics. That represents the different interests of the disciplines, geochemistry versus what we're doing in nanoEHS. The next slide. I'll finish just by saying what the current regulatory climate is. Australia has not reconsidered things. That's a historical number comment. In Europe and the United States, we're seeing a movement towards dissolution in the stomach if it is complete and demonstrated, there's no systemic exposure. Localized genotoxicity remains required. The European Food Safety Authority has provided test conditions. Those are batch conditions, 30 minutes in stomach acid, less than 12% remaining at the end. In the United States — NSF International, they have extraction tests to determine the drinking water concentration of a nanoform that comes from a product that is being used. The second step would be dissolution in the stomach using the EFSA conditions, and finally in vivo genotoxicity. So. there has been progress, but at the same time, there have been challenges. Again, I would like to thank the audience for watching. Have a good day. >> TODD LUXTON: Thank you so much, Fred. We have our final speaker now. Robert. >> ROBERT RALLO: Thanks Todd for the introduction. Thanks to the organizers. I would like to focus in on the last part of this webinar on an aspect which is complementary to what my colleagues have focused on before, which is essentially looking at the nanoinformatics ecosystem and targeting the right side of this diagram that you see. Which is, we can generate the data, we can use characterization capabilities to generate this data. And then we have computational approaches to model or analyze this data, which is what at the end of the day helps researchers to get new insights from this data in this specific field. Since 2010 up until now, a lot of things have changed in the modeling field. And starting from the pioneering efforts in the U.S., for instance, in channels like the UCLA CEIN [Center for the Environmental Implications for nanotechnology]. Or through different projects funded by the European Union 2020 within FP7 and Horizon 20220 programs. We started to look at the translation of structure activity relationships for chemicals to the nanoparticle world, right? And in this pioneering effort, we realized that the translation is not easy and not direct. Essentially, often times the interactions with the biology when we are talking about nanomaterials, is much stronger and much more complex than what we have with regular chemicals. Also, the data that we're able to generate in the nano-space is more difficult to generate. Although by leveraging high-throughput screening facilities, we can generate larger volumes of data. But it is still -- data continues to be a limitation. So, what I am going to focus on in this part of the talk is essentially on what has changed since then. What are the challenges that still remain and what are the opportunities that new modeling and new computational approaches offer us? I wanted to focus this in five different areas. The first one is we have focused a lot in the past reproducibility of the data, but we need to focus on the reproducibility of the models that we generate. The second one is data alone is not enough to create these models. We need to find ways to provide some domain awareness and to leverage this domain awareness within the modeling workflows. Then we need to make sure these models can be used at the end of the day. So, we need to make sure that these models are robust. That we can trust the predictions that we get from these models. Essentially that we can deploy these models in an operational setting. Finally, the last task I wanted to cover is the idea of how we can advance, in terms of having a much more tight coupling of the modeling, the characterization instruments, and the whole scientific discovery process through automation and autonomy. In terms of reproducibility, it is important and still a challenge to track the providence of the model in the same way we're tracking the providence of the data. This becomes more and more important, especially now with all of the machine learning approaches in which we have this huge scans of large hyper-parameter spaces to tune the models. If we are not able to keep track of all of these processes, we may have the situation in which we can have models or we can introduce, inadvertently, biases in the model development process. Essentially as we're moving towards the idea of using the autoML [machine learning] using automatic methods to generate or approve the quality of the machine learning or data-driven models in general, we have to keep an eye in the same way we've done in the past developing the ontologies, developing curation strategies for data; we have to keep an eye on the reproducibility aspects of the models. The second element is this idea of the domain awareness. In the most cases, we still have limitations on the data. Most of the modern approaches are really data hungry. They need lots of data to really have reliable predictions. We need to advance in developing models which can operate in idea space in which we have data but we don't have perhaps sufficient data to develop a model that we can really trust. So, in this space again, and this has been addressed in the past when we were developing QSARS [quantitative structure-activity relationships] for chemicals, the validation of these models is essential. And understanding when the model is interpolating versus extrapolating; when we're operating within the application domain of the model versus when we're not, is going to be extremely important. This information has to be provided in and has to be linked to all of the modeling approaches that we implement. The real knowledge embedding with the modeling architectures, we can start thinking on complementing the data on what we know about the biology, about the chemistry and the material science aspects of the systems that we're trying to model. This can help us partially alleviate the situation of limited data, but more importantly it can increase the confidence that we have in the models, because we have some sort of physical-chemical constraints that really control the response of the model when we're especially operating outside of the applicability domain of the model. Approaches like transfer learning, reinforcement learning. We have seen the advances especially in reinforcement learning with all of the developments of Google with AlphaFold and other techniques have shown recently. These are really promising approaches that can help us to advance the field. Causality is another other important element that links with data It is not enough, in which we need to really find the proper causal structure within the model to make sure that everything within the model makes sense. Robustness is again an important element. One of the things we're not taking into consideration is the bias that we may introduce into design the experiments of experiments to capture the data and generate the data. Bias, together with interpretability of the models is something that is going to be key. This is something which is a really important area of research which will have huge impacts when we're modeling the behavior of nanoparticles. And then with respect to the scalability, of course with the advances in computing environments, we are in a better situation to start doing things which up to now were much more difficult to implement. Which is coupling the data with simulation capabilities. We can couple data-driven models, with molecular dynamic simulations or quantum chemistry simulations, for instance, to really drive all of this process. This can be done by leveraging the specific types of hardware, and this can be done in situ. So we can have operando characterization techniques, which are able to respond in real time and adapt in real time to the type of measure and the quality of the measure that we are obtaining. This leads to the last part of this presentation, to the last challenge and this is how do we use these techniques or combination of all these techniques to develop smart instruments; to use the idea of AI as the driving element in the design and execution of experiments that will end up generating these kind of self-driving laboratories that will be able to go beyond high-throughput and do the real intelligent generation on-demand of data that we need in order to improve the models and get new insights into the system or systems that we're trying to model. Overall, I think that we are exciting -- this is an exciting time. There's a lot of opportunities in the area, especially when you are looking at this from the viewpoint of modeling and how models can help to advance research and the new tools that we have for our hands to really develop these new modeling approaches. With this, I'm going to finish. Thanks. I'm getting the control back to you, Todd. >> TODD LUXTON: Thank you very much, Robert. Thank you to all of our speakers today. That was really very informative. We covered a broad range of topics here. Again, if you look at the bottom of your screen, you'll see the Q & A box. That's where you can type in a question that we can pose to our speakers today. I would like to start off and ask a question of all our speakers. Covering a little bit of what each of you have offered and provided insights today. I think we’ve really progressed in a great way. We started off with sort of an understanding of what the rationale and reason behind why we're collecting the data and developing the framework from which we can continue to pull and integrate. Then we went on to think about how the data we currently have can be applied to nano and microplastics. How do we avoid having to redo or go through the entire process again? Launching then into a discussion on how to build some of the mechanistic data inputs. It is more than just collecting data. It is understanding the mechanisms and the dynamics of those situations. Finally, how do we pull all of the data together and learn something from it? When I look at this, I look at the immense weight that it would take to get all of this into a single format. My question to the panel is what do you think is our bottle neck at this point for being able to achieve this goal of getting all of this information into a system that can be easily accessible. And then on the other side of that, where are we really succeeding at this? How can we use those results to push us forward? If any of you would like to comment on it, that would be wonderful. >> STACEY HARPER: I think Andrea should speak because Europe is actually implementing some of this at a much rapid pace than we are in the U.S. >> ANDREA HAASE: Thank you so much, Stacey. I was just thinking about a clever answer. Because I think there's not a single bottleneck. There are several bottlenecks. Maybe my answer is not even complete. I think one aspect is currently the understanding of data is still hampered by the fact that we're still lacking data. Not that they are not generated but they are simply not accessible or available to do some metanalysis or evaluation. Clearly, we need a change in mindset through which the datasets are released. The data-base landscapes fragmented. We need more standardized approaches to interlink these data, to standardize the metadata and so on. Maybe we have a couple of bottlenecks that still need to be worked on in order to make a full understanding even on the wealth of information that we have today and not only -- not only to touch like the quality criteria, completeness scores, so on and so forth. I think modelers would appropriate or have a quality tech associated with the data. Many bottlenecks, I would say. >> FRED KLAESSIG: This is Fred. I think that we have to move from the worry of nanotoxicology to more active nanosafety format. I see that in what's happening in the dissolution area. Not every test on the official list has to be done because dissolution means you don't have to do it. I would extent that to the type of predictive toxicology that Andre Nel and colleagues at CEIN are pursuing and currently, I think if I am correct, Andrea, IATA [integrated assessment and testing approaches] is a version of that is in the GRACIOUS framework. So those are somewhat flexible responses to what is needed for the testing for safety purposes and less the inflexible response that you have to do a 90-day inhalation study because it is a particle, darn it. >> STACEY HARPER: We've talked over the past, you know, decade or so about having the journals be one of those key gateways that when you publish, you have to make your data actually available in a format that's usable instead of just a PDF of your raw data or something and it is not annotated. You know that we've talked about the logistics of that; it is probably not feasible. I think, you know, of the Miami standard for, you know, putting in Juno, genomics data, that's expected. You can't publish without putting your data in there. I think we have to move to a structure like that to really get the amount of data. I think one of the key things to that is there's a lot of negative data that's never published. There's a lot of it. And we're not taking them when we do a risk assessment. We're looking at the published papers and then try to extract from that. It just is flipped backwards, I think. >> ROBERT RALLO: Yes. I think we have a lot of advances. We are seeing a lot of advances, for example, in natural language processing that can help in doing some of the extraction of the data, in some cases. I would agree with what’s been said; It is imperative to have access to the data and perhaps having a common format is not that important. The important element is to make sure the data well documented and we understand what the data represents and then we can derive whatever interfaces to capture the data in the right way. >> TODD LUXTON: Thank you all for that. We do have a couple of questions. The first one here is for Dr. Haase. What are your thoughts on FDA and international counterparts in regulating nanomaterials which may affect clinical adoption/use of nanomaterials? >> ANDREA HAASE: My answer is I don't have any particular upon on what the FDA is doing. I would like to approach the question from a more general perspective since I also work in a regulatory institute. I think regulators also appreciate to have access to the wealth of data from the scientific domain and to look also at the data that is available from the research. Also in the medical field, it is highly relevant in the preclinical state to have all of these evaluations coming from cell culture and coming from other maybe potentially biochemical and acellular testing. I think the way forward has already been paved in the TOX21 initiative. I think what we need here for the nano or innovative materials domain is a very similar approach. We need modern and data- driven approaches that are reliable. So that not each and every material variant needs to be fully evaluated. Again, we can rely on what we know already and make an extrapolations or predictions that are relevant. >> TODD LUXTON: Thank you. Our next question here is playing devil’s advocate, this approach—gathering data with too little understanding of the difficulties, evaluating each nanomaterial for toxicity, — seems to be committing the sin that was committed in biotechnology of drowning in data. How do you think this will material the commercialization of the new materials? >> ANDREA HAASE: Maybe I can go first. The others can add. Currently I do not see the risk that we're drowning in data. Rather I currently see the situation where the problem is elsewhere. Currently not the full data sets are released. Also the metadata description is poor. That means currently, I would rather see it is highly challenging to fully and truly evaluated what has been done, how it has been done, and how it has been evaluated. That contributes to the situation that we have today where it is not really clear. Is that really -- genotoxic. Is that really showing some other adverse effect? Because it is not really clear if two experiments have been conducted under comparable conditions. Maybe the protocols were just different. All of that matters. I think currently we would benefit from a universe where more datasets are fully released and we have a rich description in metadata that we can truly understand what has been done. I think then Robert will come in with all of this artificial intelligence, so you can easily sort out the garbage by using the computational approaches and in parallel we need to really push forward for quality standards, for completeness standards, and then, I would say it like that. >> FRED KLAEESIG: This is Fred. I have a different perspective, shall we say. The regulators are not scientists. I think Andre asked the question. When you get an PMN [pre-manufacture notice] and you have 90 days, to act upon it, you go by analogy. You pick the closest particle you have worked by analogy, and you move from that as opposed to, say, I'm going to change how I evaluate the material because of nanoinformatics. It’s just a difference in the regulatory nature. That's why I put it more in the nanotoxicology versus nanosafety perspective. We have to ask that people move over to the more dynamic setting how they evaluate things. I see that Andre has his hand up. >> TODD LUXTON: I don't know quite sure how we do that If it is possible. >> NNCO WEBIINARS: It is not possible. I'm sorry about that. >> FRED KLAESSIG: You can accept everything I said Andre agrees with unless he lowers his hand. There's a question from Paul Harten on ISA-TAB-NANO. ISA-TAB-NANO is a standard in ASTM International. It and other elements were brought together in what's called eNanoMapper. ISA-TAB was a flat file, Excel-spreadsheet type. Everything has moved forward, with movement in languages. ENanomapper right now in my mind is an excellent tool. There is also the MENDNano database that came out of CEIN. >> ROBERT RALLO: To add to what my colleague said, nanoinformatics in general is not some magic set of capabilities that are going to help us answer whatever we have. Nanoinformatics, in general, from data modeling, to data analysis are the set of tools that provide scientists with additional capabilities that can help them to advance and to look at the data in a different way. I think this is the real focus that we have right now. How this translated, later on, to use in a regulatory environment- this is a different question. This links with some of the things that I've said before. If we want to use a model in a regulatory environment, we need to make sure the model really works. We need to understand -- we need to understand how the model has been developed. We need to make sure the model has been validated. We need to know where we can trust the model and where we cannot. There's still a lot of work to do in the space. But I think that the building blocks which is in part what we've been doing in all of these past years are there. We are now in a position to start really advancing in the field towards this direction. >> ANDREA HAASE: Maybe if you allow I can add one more aspect, because -- I don't know, I think it is understanding who is the regulator and who is the risk assessor. What we frequently see is that we are approached, there some journalist out there that read something in the paper. Then they make some conclusions on that. There's suddenly a huge public interest in one of the topics. It is really difficult to explain what the science behind it is. And I think from that aspect, I truly believe that also for this, we need a more open access to all of the data. Including also the negative data that never will become published as Stacey just said. And also, if you develop or want to develop these modern approaches to get rid of these animal tests trends that we still need today, agreed. But maybe we don't want to do animal tests forever. We need the information coming from all of the research project to make use of the power of big data in order to develop AOPs or IATA-based approaches that can truly replace animal tests in the reliable manner. This is also something that we need to consider here. >> TODD LUXTON: Kind of moving along the lines to this question. As an environmental health practitioner, I appreciate the reduction in the “sky is falling” message as most of us do. Do any of you have any insights into disposable aspects of engineered nanomaterials in issues that we might face from that standpoint? And how -- I guess maybe how some of the current information that we do have might enable us to make better predictions about ultimate fate? >> FRED KLAESSIG: If I might take that on. I come from the industry. I recommend looking to carbon black in the silica industries. Carbon black is ten million metric tons a year production. Silica is about 4 million metric tons. They've been in commerce. They have disposal requirements. They would be a starting point for addressing that sort of a question. Second, I believe that our colleagues in Europe are more complete. They connect the safety data sheet disposal to the safety data sheet in terms of toxicity. Andrea is aware of CLP [Classification, Labelling and Packaging] over in Europe. There's a lot of activity there making certain that the supplier tells the distributor, tells the customer the same information, so that it is disposed of properly. I defer to Andrea at that point.

I'm not sure if I need to answer. Because CLP is just the European reality of the GHS [Globally Harmonized System of Classification and Labelling of Chemicals] system that's globally active. I think you described it pretty well, Fred. >> TODD LUXTON: All right. We have a question here that's probably directed more towards Fred. Are we really documenting our measurements of dissolution, absorption, and toxicity data well enough we can assess their quality? I think this is -- you know, a really important question that can be expanded beyond this. One of the challenges that I faced as a Federal researcher is the ability to utilize other published data and having to have a very well documented QA/ QC procedure that goes along with it. This kind of touches on a number of different topics that we've talked about today. How do we make sure that we're meeting those QA/ QC practices when we're doing the big data collection? >> FRED KLAESSIG: I think we're in the middle problem. I don't think that we're getting the right metadata fully. I don't think regulators need the mechanistic answers from the physical chemist on dissolution I think the need there is more directional; does it dissolve a lot? Does it dissolve a little? Does it persist? Then that would allow someone from the regulatory side would say What is from my menu? So many from column A and so many from column B are the appropriate test to apply. As long as eventually you do back it up with mechanisms. I think, in response to John’s question, the question is what is your test method -open or batch? Are you seeing a possible apparent solubility limit in one system and not the other? First I think it comes down to methodology. We haven't really clearly established that. I think everyone is doing a lot of work. But I think there will be some consolidation. I bring everyone's attention the NanoHarmony group over in Europe is working on this as a revision of TG 105 from OECD. I think they are looking at both batch- and flow-through systems. They may be able the place for more detailed answers to that question. >> STACEY HARPER: Yes, I would say even on the topside when we think about studying silver nanoparticles or copper nanoparticles, we do the dissolution studies. But we do it at one concentration and then we expose our animals over a wide range of concentrations. We don't really do dissolution measurements that way. Your thoughts, Fred? >> FRED KLAESSIG: I think there's limits. I know that -- I think Keld Alstrup Jensen and Jutta Tentschert are the two people in NanoHarmony. They are very much involved in what is the dose. What is the concentration? And the answer is you want to find that apparent kinetic solubility limit that will tell you that the particle exists. And to make sure everyone realizes- In the GRACIOUS framework, persistence of the product is what leads to accumulation, which leads to a certain category of tests being done. You have to identify with persistent or the apparent solubility to be able to make those sorts of decisions. They are all interconnected. I don't think the dissolution is going to solve all of the problems. But it will help you, inform you in terms of what toxicity testing you should do. >> TODDD Thank you. There's been several questions or calls for the making the slides available. The overall presentation will be made available. So that's what we can offer at this point in time. Another question here. Have any rules of thumb evolved as to what constitutes a significant exposure of nanoparticles? The example here is by inhalation and trying to determine if we are measuring what is noise versus signal. But in the broader sense, what constitute significant exposure? That would be to all of our panel members. >> FRED KLAESSIG: I think dissolution may give you a floor in the sense that -- oral ingestion -- as I used with the lead example. It might be that anything below 2 PPM lead particles is not really a particle exposure. It is an exposure to lead salts. There's a long tradition of information on that. There may be a dissolution floor. I don't know what's a trigger though or what would be a threshold. I assume that's what the regulators do when they do a recommended exposure limit or something of that -- OEL. They put that on one of the safety data sheets. I think that's a very complicated topic. Therefore, it comes much later than the current group is being exposed to. You'll be told, “oh, gee, you shouldn't have used benzine when you were a sophomore in organic chemistry.” You are going to be told after the fact that some exposures were not particularly helpful. >>ANDREA HAASE: I think it is correct. But we need to ask “does the particle persist? “Can it biopersist? Can it accumulate in issue? Then the answer would be different compared to the soluble particle that will eventually become cleared or it something that accumulates over the lifetime? Then you may end up with the significant exposure even if the initial event or the individual event can be pretty low. Yes . STACEY HARPER: This would all be informative on the toxicology side too. That would have to be taken into account. How seriously toxic a particular nanomaterial is. >> FRED KLAESSIG: Correct, Stacey. I'm making the difference between the particle effect and whether the toxicology of the soluble is acceptable. You might not like the toxicology of the soluble ion. But it may tell you it is a measurable rate. >> STACEY HARPER: Yes. >> TODD LUXTON Okay. Unfortunately, we've reached the end of our time for today. In closing, I would like to thank our speakers one more time. Stacey, Robert, Andrea, and Fred for sharing their perspectives and for their wonderful presentations. There was a great learning for me. Thank you for being part of today's event in asking questions. We hope you'll join us for future webinars. Follow us on Twitter @NNInanonews and check out nano.gov where you'll be able to find copies of the presentations. Once again, thank you very much to all of our presenters. I hope that everybody has a wonderful morning, afternoon, and evening. Thank you so much for joining us today. >> FRED KLAESSIG: Thank you. >> STACEY HARPER: Thank you.