Written by Christopher Kelly
Oct. 2, 2018
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Megan: Hello and welcome to the Nourish Balance Thrive podcast. My name is Megan Roberts. Today, I'm delighted to be joined by Dr. Elizabeth Nance. Hi Elizabeth.
Elizabeth: Hi Megan. How's it going?
Megan: I'm well. How are you today?
Elizabeth: Good.
Megan: Awesome. Well, thank you so much for joining me and I'm really looking forward to talking about your research. But before we start, I wanted to dive in a little bit and talk about your background in chemical engineering which I think is a field encompasses a lot more than one might think. Well, I certainly, myself don't have an engineering background. I think that it nicely sets you up for learning how to integrate and solve some complex problems. The engineers I know, they certainly bring a unique perspective to the table. Would you agree with us?
Elizabeth: Yeah, absolutely. Actually, I was just reading How to Change Your Mind which is a book by Michael Pollan and there's this fantastic quote in there from Peter Schwartz who's an aerospace engineer that describes problem solving and engineering is always involving these irreducible complexities where you're trying to balance complex variables that you're never going to get perfect while searching for patterns at the same time.
I think this really sums up nicely kind of the mindset I've always taken from chemical engineering and the approach I try to use with chemical engineering just because chemical engineering is a very, very broad field. The way I kind of always think about it, if I were to boil it down to one specific thing sort of as the core mentality is that the whole purpose of a chemical engineer is to build a quality product that can be produced at scale and can positively impact human life.
Chemists have mostly been thought of in the context of industries like oil and petroleum, and pulp and paper, and food, and chemical industries. Even in the computer industry, if you think about the scale down process of computer chips to the size that they are today, the IBM founder is actually a chemical engineer. In the process, he developed chemical vapor deposition was really one of the key processes that allowed us to kind of address this scaling issue. In another sense, like getting computer chips down to the size that you can use in your cell phones.
The role of chemist have played in terms of this concept of producing things at a wide variety of scales to kind of bring new technologies to humans I think has been a really valuable bridge for me to think about the role that chemist can play in health and the way that chemical engineering can be used to tackle with a very complex problems in health. I think about how heterogeneous humans are and how variable humans are.
A number of factors that influence that, like genetic and environmental, and diet and lifestyle, a lot of things that you guys like to focus on in Nourish Balance Thrive, and how that all influences and elevates the complexity of the human body and disease. This ability as an engineer to kind of think about that in this very complex way without getting caught up in the complexities. You're looking at a system that has all these components that maybe play a role, the role that they play, you don't quite know, but you know that there is a role to be played.
You don't know how important that role is, but you're trying to sort out how each component of that system actually impacts the overall operation of the system as a whole. The engineering mindset I think really helps you kind of bring into that realm of how complex this problem is. This idea of the fact that it's okay that you're never going to be able to exactly know the specific component or how that component might play a role, but what you're really looking for is patterns and connections between those components and how they influence a larger system.
I've really enjoyed that from an engineering mindset. Particularly, chemical engineering which really requires a lot of fundamental understanding of physics and math and chemistry which are all key components of underlying physiology, which I think is where there's a really nice transition between that degree and specifically at least health and medicine.
Megan: Wow, I love that. That's awesome. What in particular was it that got you interested in this field of-- You have the background in chemical engineering and then now you're working in neurology and nanoparticles.
Elizabeth: It was mostly, actually, a personal reason. When I was, I guess I was around ten years old, I realized that my grandmother was suffering from a disease, it seemed to impact her motor functions, so her ability to kind of use her lower limbs at least, her legs normally. This disease seemed to progress, in my mind, fairly rapidly throughout the remainder of her lifetime. The things that were most prominent to me at the time were that she was in a lot of pain. There was nothing that could be done really to help her other than some physical rehab and some basic steroid-type medications and anti-inflammatory medications.
But there wasn't really anything that could rid her pain or ease the majority of her symptoms on a day-to-day basis. The other interesting thing was that medicine didn't really know how to define the disease, so they named it something called hereditary spastic paraplegia which is basically naming symptoms. It doesn't really tell you anything about what the disease actually is, like how it occurs and how it progresses. It just says what the symptoms of that disease are.
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Going through my teenage years and sort of seeing this very significant impact on my grandmother who I spent a lot of time with, my family is very close, so this was very much in the forefront of my mind, I was really frustrated by the fact that medicine and science really weren't providing any answers. They couldn't provide treatments, but they also couldn't really seem to provide any basic understanding of the disease at that time. That was my view as a teenager.
But what it really kind of inspired me to do was to look into engineering as a field to go into because engineering to me was always training that allowed you to problem solve. Chemical engineering in my mind was just the most broadest engineering degree at that time because it requires an understanding of physics and math and chemistry and more so biology today. I went into that degree area because I wasn't really certain how I wanted to get from being an engineer that's a problem solver to actually figuring out how to better understand or do something that would immediately or eventually impact people who were suffering from diseases like what my grandmother and eventually my uncle ended up having. Those sort of fell into the realm of neurodegenerative diseases at least as I understood them at the time. That really kind of piqued my interest in finding ways to interface engineering with the neuro fields.
Nano technology I would say really found me more than I found it. I was not knowledgeable about that area or that field or that technology at all, prior to joining my PhD lab. It was only upon joining that lab and spending a few years in that lab that I really began to learn in what nanotechnology is, what the power of it can be and how it can actually be used in a biological setting. I really came to the point where I appreciated the fact that nanoparticles like most other things, I know we'll talk about what nanoparticles are a little bit later, but like everything else that goes into your body, they're very subject to the interactions with their environment.
That can occur through fluid flow like in blood or in the lymphatic system, anything that's going to carry the nanoparticle along with it. Those particles are subject to interactions with specific cells or proteins, both in terms of an active interaction type process or in terms of just being like if you think about a nanoparticle or something trying to move between two cells, it's got to fit through that space. Even just passive interactions with its environment still influence its behavior.
That kind of exposure when I was early on in my PhD to this world of nanotechnology really helped me realize the potential for it as probes, as basically indicators of the environment in which they were being used in. That sort of awareness in combination with my family background and wanting to be able to both understand, but also figure out better ways to treat these very complex diseases that were affecting my family really is what kind of motivated and drove my work from then on.
Megan: This neurological condition that your grandmother had, has there advances since then? Have you followed that field?
Elizabeth: I haven't followed it that closely, but I do know that there are certainly better treatments, earlier diagnosis as well, just being able to better define what symptoms onset early enough to get people into rehabilitative practices earlier on rather than waiting until maybe their muscles have atrophied or sort of degraded in such a way they're not functioning enough or further rehab can be more difficult. I think there have been advances, but as with most neurological disorders, increased understanding doesn't necessarily quite yet translate to improve therapeutic outcome.
That's something that I've been fascinated in is as we gather more information about these various diseases. Why are we not seeing a subsequent or a corresponding improvement in actual therapy? I think there's a lot of reasons for that. Some of them, definitely, you'll get me worked up about them as we talk through some other things. But I think that studying brain diseases in general is a really challenging area, not just because we can define symptoms we could potentially diagnose earlier.
That's actually what I think has led to any sort of improvement in survival and life expectancy of most people with most neurological diseases is because of improved detection and earlier diagnosis and maybe not so much better therapies, but also more awareness of the complications of it. Certainly, how things like lifestyle and environment can play a role in either prevention or to some extent, suppression of an ongoing disease.
I think there's an increasing amount of data out there, but the way in which that data all integrates and translates into actual better outcome is still not quite where it needs to be for us to really maximize what we know about these diseases or what we've learned from the basic science study of these diseases.
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Megan: Yeah. That just makes what you're doing, the work that you're doing now even more exciting, right?
Elizabeth: Yeah. Certainly, I think it can be overwhelming a lot of times, but I kind of always try and leverage overwhelming into excitement just because there is a lot of information, there's tons of fields working on this. Part of what really inspires and motivates our approach is really trying to be cognizant of the number of fields that are working on it and the amount of information that's coming out of those fields. But cognizant in a way that we're actively trying to integrate that information from different fields and different sources as well to help inform the role that we then play in that larger problem. I find it very exciting. I can totally get why it seems very overwhelming to a lot of people though.
Megan: I want to get into your current kind of research, but I also want to talk a little bit about your PhD research and how it set you up for the work you do now at University of Washington. If I understand correctly, and you can correct me where I'm wrong, before your work, it was largely thought that only relatively small particles could penetrate the blood brain barrier, but your work then showed that larger particles could actually penetrate when coated with a particular polyethylene glycol coating.
Then this ultimately opened the door, so to speak, for other potential molecules that were once thought to be too large to get into the brain. I'm guessing, they would also allow delivery of a larger amount of a given substance that might have downstream benefits, like kind of a controlled drug release or something like that.
Elizabeth: Right. When I first joined my PhD lab, which was with Justin Hanes at Johns Hopkins, his group at the time was actually largely focused on overcoming the mucosal barrier. It sounds probably kind of weird that my sort of path of studying things in the brain would originate from understanding mucus. But mucus is actually really fascinating and I think has a lot of what has been studied in the field of mucus coats pretty much, everything that is internal but external.
It coats your lungs. It coats your GI tract. It coats your eyes. It coats your nose. It coats the colon. It coats the vaginal tract. It's there for a reason because that's the interface. All of those cell layers that underlie that mucosal layer in all of those tracts are what are the barrier between the external world and your body. Whatever you swallow, whenever you're walking on the street and particularly in a city or behind a city bus that has whatever coming out of its exhaust pipe, all of that is stuff that could get internalized into your body if that mucous layer wasn't there. It is very exceptionally protective barrier.
What's fascinating about that and why it relates to the brain is that, as you can imagine, just even from knowing that when you get a cold, you get all gunked up and you blow your nose and the snot is all like stringy-- People are going to love this. Then mucus is very sticky, right? It's really good at trapping foreign entities from the environment like bacteria and air particulates and pathogens. But as you can imagine, that makes it really difficult to get a drug through.
My PhD lab had spent a lot of time trying to understand how things like viruses which are very good at getting across mucosal layers and infecting your body do that because everything else that comes into contact with mucosal layer, if they get stuck and they get cleared out. What they found is that by controlling the designs of systems like nanoparticles to basically be not adhesive or at least inert from interacting with that mucosal layer, you could actually increase the ability of a therapeutic to penetrate that layer and distribute in the underlying tissue.
When we were looking at clinical trial literature for disease in the brain, because I sort of came into this lab being fascinated by the problems they were working on, but really wanting to figure out how to take that into the brain, not with the expectation that there is mucus in the brain, but just that maybe some of these ideas might translate. What we found was that in clinical trial literature, most drugs have failed not because they were actually bad drugs, but really because they couldn't actually get to where they were going, even when they were directly injected into the brain itself.
That sort of opened up this idea that maybe it's not the drug itself that's ineffective, maybe it's the delivery method that's a problem. Especially because of the literature that showed that drugs that were directly administered didn't have great penetration, we begin to kind of take the mucus problem and say, "Okay. Well, what can we learn about how we approach overcoming mucus and translate that into what can we learn about this barrier that exists in the brain, including the blood brain barrier, but also beyond that that keeps things from being able to penetrate to the sites that they need to get to?"
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We kind of started very early on with basically controlling just some of the simpler properties of nanoparticle. Things like their size and their surface coating. We use nanoparticles as a model of a therapeutic system because a lot of therapeutics are quite large, they're not on the order of like oxygen and glucose and other small molecules which readily penetrate within these organs and across these barriers. We started playing around with these different physical and chemical properties of the nanoparticles and we found actually very similar ideas that if you make a nanoparticle basically non-adhesive with the brain environment, then you can actually increase the distribution of that nanoparticle within the brain.
Importantly though, we also show that if you do make this nanoparticle inert from its environment and you therefore increase the distribution within the brain, that by itself can actually result in a better therapeutic outcomes. That was kind of tackling the clinical challenge that they were seeing when you directly administer into the brain and you don't get good penetration, you get poor outcome or poor efficacy.
Then the last thing that we found ties into what you were saying where by making these nanoparticles basically inert from their environment, we could actually get much larger particles than what had previously been reported to penetrate within that environment. That certainly, like you mentioned, allows us to increase our therapeutic capacity. In nanotechnology, most of these particles are spherical in shape.
The way you would think about therapeutic capacity is thinking about like if you have something on the order of 100 nanometers in size, if you were to double the diameter of the particle alone, you increase the drug loading capacity in a volume metric sense, right? You cube the amount of drug that you can put in, not just double the amount of drug you put in. Even just slightly increasing the size of a nanoparticle that can penetrate within an environment significantly increases the amount of drug that you could give in the same volume of nanoparticles. That can decrease your total dose, but also allow a higher local dose.
It's this really nice interplay of taking advantage of some of the unique properties of nanotechnology, but also trying to maximize that therapeutic capacity. Maybe you don't have to administer as much drug or as much of volume, maybe you don't have to administer as much total mass, maybe you don't have to administer as many times. It can actually help give you a little bit more flexibility in terms of the therapeutic capability than what had previously been thought to be possible.
Megan: That's really interesting. What is it about this polyethylene glycol that allows that to happen? Because if I understand correctly, that's the coating, right?
Elizabeth: Yeah.
Megan: Is it the charge of it? Is it the total surface area? What is allowing it to act in the way that it does?
Elizabeth: Yeah. Polyethylene glycol basically imparts a stealth-like property on a nanoparticle. It protects it in a couple of ways. Polyethylene glycol itself is just basically repeating units of carbon and nitrogen and oxygen. What those units do is they provide a net neutral charge on the surface of a nanoparticle, so that reduces any sort of electrostatic interactions. When you sort of have positive-negative interaction you could have with the environment.
Proteins and cells are charged in most cases, whether they're negatively charged or they have pockets of positive charge that kind of varies cell to cell and protein to protein. But those charges can basically stick a nanoparticle or a drug if the nanoparticle or the drug also is charged. That net neutral charge helps reduce any sort of those electrostatic interactions that can occur in the environment.
But the other thing that polyethylene glycol does is it's a relatively hydrophilic compounds. Most people would describe it as amphiphilic, but it's relatively hydrophilic. What you don't get when you have a polyethylene glycol coating is not just hydrogen bonding, but also hydrophobic interactions. That's another type of interaction that is very present or the possibility for that interaction to occur is very present in most tissues because a lot of cells and a lot of proteins have hydrophobic domains on them. That's another way that a nanoparticle or drug, for instance, could easily adhere or stick to a local environment that it's not intended to necessarily target.
We found that the molecular weight of the polyethylene glycol and the density of the polyethylene glycol on the surface of the particle certainly plays a role. But we've also found that there's other coatings that provides similar properties other than polyethylene glycol that can also be used to impart those same sort of properties on a particle which can be beneficial for a variety of reasons. Not only does it give you more coatings to potentially pull from, but polyethylene glycol has also been shown to decrease cell uptake with that coating because it's now non-interactive with its environment, so it's kind of a balancing act.
If you want to get a nanoparticle into a specific cell but it's coated with this peg layer, then your chances of getting into that cell are lower, by the fact that you have this peg layer on the surface of the particle. There's balance between at what step you're looking at delivering a therapeutic, how much that peg coating is actually beneficial versus actually hindering your capabilities. We've explored other coatings as well that can achieve the same thing. If, for instance, we have a scenario where we want to get cell specific uptake and we know that that polyethylene glycol coating is going to minimize that.
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Megan: Okay. Thanks for explaining that. I probably should have done this before we even delve into your PhD work, but I do you want to back up just a little bit and talk about both diffusion and convection. In my mind, when I hear those terms, I immediately go back to physics and chemistry courses, and things like concentration gradients and thermodynamics and the idea of gas law which I don't think, I could forget if I tried these days.
Elizabeth: I don't think anybody can.
Megan: Yeah. But you're probably thinking about these things on a much more sophisticated level, but I would like you to describe what diffusion and convection is. Why do we care about them in biology? Then if you can also give us an everyday examples of where we would encounter diffusion and convection.
Elizabeth: Yeah. I don't think I get a lot of grief from a lot of people about the fact that I say diffusion, I was jokingly say like diffusion is what keeps me up at night which is also is physiologically true, but also just from the process of thinking about it all the time. When we're thinking about diffusion at the very, very basic level, diffusion is just the random movement of any sort of particle that is basically experiencing thermal fluctuations.
These thermal fluctuations are driven by the local temperature which then causes water molecules to bombard whatever the object is and the bombardment of those water molecules on that object is then what causes that object to basically jiggle around. It's random because if you have a completely uniformed solution that you're in, the water is going to bombard that object from all directions equally.
There's no reason to think it wouldn't be equally. You can get diffusion driven by concentration gradients, like what you were saying, because you now have, for instance, different types of molecules bombarding that object that are going to drive it to become more uniformed. Because that's what thermodynamics tells us we have to do, is to increase entropy, minimize free energy and so getting uniformity
Basically, easiest way to think about this is if you take food coloring and drop it into a glass of water, that's concentration gradient-driven diffusion. Because over time, you'll see that food coloring both decrease in intensity as it spreads out throughout the entire glass of water, but you also see that entire glass of water become a more uniformed shade of that color. That's probably the easiest example to think of concentration gradient-driven diffusion.
Now, just basic Brownian motion is what you call kind of basic simple diffusion where it's just the random movement of an object and then concentration gradient diffusion. Those can all occur in the context of bulk flow which is kind of what we think of in terms of convection. I like to think about convection only because having to live with somebody who drinks coffee nonstop, is when you have a cup of coffee and you blow across the top of that coffee to cool it down, you're inducing convection to drive a faster diffusion of temperature, of heat molecules from the coffee into the air.
By adding a convective flow, you can increase the change in the system that you're trying to get. For instance, in that case, a cooler coffee. If somebody added sugar into your coffee, it's a similar idea. If you just relied on concentration gradient diffusion, you would put the cube of sugar into your coffee and not stir it all. It would take a long time for that sugar to dissolve and to be fully solubilized in the cup of coffee. But what most people do is they stir it, so they're adding convection into the system to increase the rate of diffusion and the rate of dissolution of that sugar into that coffee.
In physiology, we've actually evolved to take advantage of these processes in the most efficient way possible. What's interesting about diffusion in physiology is that diffusion is the basic transport process that occurs for all tissues. Getting oxygen and glucose to yourselves is a diffusive process, once they get to the organ of interest from blood flow.
Blood flow provides that sort of convective process of getting oxygen and glucose and other nutrients to organs in the body, but actually getting those nutrients and oxygen from the blood into a cell is a diffusive process. This is why our blood vessels are structured in such a way to provide the most efficient method of getting oxygen and glucose and other nutrients to all of ourselves.
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We can think about this in the context of the brain which is the organ at least that-- Where are we most interested in for my research where you look at things like magnetic resonance imaging or functional MRI and you're looking at oxygen and glucose transport in those cases. Those two imaging techniques really rely on seeing the diffusion or the change in diffusion of oxygen and glucose based on a change in a local cellular demand, which can come in the case of sleep, can come in the case of exercise, can come of the presence of injury, or even in the presence of just different functional demands of the brain for any sort of normal day-to-day execution of task.
This concept of diffusion I actually think is a really important underlying basic physiological mechanism. Where I feel like we haven't really thought about how that connects, what that might mean in terms of drug delivery is how therapeutics behave in the context of diffusion. That's where we're really interested in saying, "Okay. If diffusion is this basic physiological process that evolution has maximized to be as efficient as possible, then how can we actually use an understanding of diffusion or leverage diffusion of therapeutics to improve delivery or to at least maximize our chances for delivery in the body?"
I think the kind of cool thing that's may be relevant to your listeners as well is that if you think about diffusion from a more philosophical aspect, it's something that occurs on these very, very short length scales, but it has these long-reaching effects. If you think about the brain, the diffusion of a neurotransmitter to neurons induces a cascade of cell-to-cell communication events that then elicits a response that we see through a function or an action of the human body.
That's a pretty broad acting scale for a simple physiological process to act on. This concept of seeing that a very basic aspect of physiology has significant and often complex consequences, I think is a concept that really appeals to the NBT listeners because this is something that you think about all the time when you're seeing what in your environment, what in your life, what in your diet, what nutrition, what in all these areas actually influences the performance outcomes or the health outcomes that we're trying to achieve.
Megan: Right. That was a great explanation. The next level up or down I guess from the basic physiological processes of diffusion and convection, it would be these nanoparticles that you're using. Can you kind of explain what they are and also help us conceptualize the relative size of the particles that we're talking about?
Elizabeth: Yeah. The technical definition of a nanoparticle is an object that behaves as a whole unit with respect to its properties and has a dimension that's less than 100 nanometers. That 100 nanometers size is really kind of the scale aspect that we're always thinking about when we're talking about nano. In terms of scale, what you can think about is that 100 nanometer particle is about a thousand times smaller than a single human hair. I like to kind of put it into context of relative size difference.
The way that I always explain it to students is that if you look at a nanoparticle and a soccer ball, the difference between those two is the same as the difference in size between the soccer ball and the Earth. We're talking about a really, really tiny scale. But what's really great about that scale is that that's the same order of magnitude at which most biology operates. DNA is on the order of a couple of nanometers. Viruses are on the order of 50 to 100 nanometers. Proteins are on the order of tens to hundreds of nanometers. Most of biology actually operates at this scale. Even though this is a pretty small scale, what we get with nanotechnology is very similar to what biology has in terms of control.
We can actually manipulate the composition of a nanoparticle at the atomic level, much like biology can control the composition and structure of, say, a protein at the atomic level. There's a lot of flexibility both in terms of being able to design a nanoparticle literally from the atom up, to being able to have it behave in a more complex system and be influenced by that more complex system that kind of span scales. That sort of spanning of scales is what's always really fascinated me about nanotechnology.
Just to give a few examples of ones that maybe people have heard of, there's organic and inorganic nanoparticles. Organic nanoparticles or things like lipids and proteins and peptides and micelles and polymers. Then inorganic nanoparticles are probably much more commonly known to most people because these are things like gold and silver and iron oxide and aluminum and silica particles, and then quantum dots which is kind of always a term that comes up when you dig a little bit into the nanotechnology literature.
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These are all particle systems that are actually in use in humans in a variety of different ways. Maybe not all of them, but a good majority of them are actually in use in humans in a variety of different ways, and have been in our food or in sunscreens is a good example with certain types of nanoparticles that are intended to help block UVA and UVB rays. There's a lot of cosmetics have nanoparticles in them. Foods have nanoparticles in them to help keep things stable and dispersed.
There's lots of places that these particles have actually been used and it's because they have these really unique properties, even at this really small size scale. In medicine, that sort of unique properties are tied in to the concept of the surface area, the volume ratio of a nanoparticle. A nanoparticle has quite a large surface area per mass and that surface area allows you places or space for therapeutic to be encapsulated or conjugated.
If you can put a therapeutic into these spaces by maximizing how it distributes within a nanoparticle itself, then you not only usually increase the therapeutic solubility, which is often a problem with a lot of drugs that are being used and one of the reasons why they have a delivery problem, but also you're protecting that drug from any sort of degradation or clearance from the body because you're basically shielding it from the body's ability to degrade it down or get it cleared out.
That allows the therapeutic to have an increased circulation time. It also improves the bioavailability of that drug. In many cases, you can take advantage of the fact that this therapeutic is encapsulated into this nanoparticle to get things like facilitated delivery into a target organ or a target cell. That also helps increase its specificity as well.
Megan: You almost answered one of my other questions which was basically how can understanding this nanoparticle behavior ultimately inform new treatments for neurological and other health conditions. Do you have any other thoughts on that?
Elizabeth: I think I can tie it back into the diffusion question a little bit--
Megan: Perfect.
Elizabeth: -- Just to sort of provide a bit more of a holistic view on it. We are taking and translating the ideas around diffusion of oxygen and glucose and other nutrients and small molecules to the context of therapeutics. One of the questions that we like to ask is how does the design of a therapeutic actually impact its ability to diffuse within the brain? Does that diffusion change in the presence of a disease because that's going to influence how effective that drug then is in treating that disease.
One of the nice things about thinking about therapeutic delivery is that we can use nanoparticles as probes to answer those questions. In using them as probes, we can actually get a sense of how they respond to their local environment. For instance, in some brain injuries, you get an increase in cellular debris in the brain auxiliary space or in the brain tissue that's caused by a cell death and that's actually going to alter the spacing in which a therapeutic can fit.
It also increases the viscosity of the fluid in that space as well which can slow down the penetration or the diffusion of a substance in that space. Really kind of using the particles and measuring their diffusion within the tissue environment allows us to get a better sense of how do the fundamental physiological processes actually scale and apply to therapeutics or therapeutic scale entities. Then if we understand that, can we then engineer around it to increase our therapeutic capacity.
We've been able to show that just by increasing diffusive capability of a therapeutic on its own through probing that question with nanoparticles that we can improve the efficacy of drugs that have otherwise failed clinical trials in cancer models neonatal hypoxic ischemia models and cerebral palsy and in a lot of other injury models. That's just by kind of thinking about how do we take advantage of a process that already naturally exist for other important entities at least within the brain.
Megan: Then I'm assuming that this using nanoparticle technology isn't just for pharmaceutical drugs. I know that there was a recent study that you co-authored actually with Tommy that showed that curcumin can be loaded on to nanoparticle for enhanced uptake into the brain. I know curcumin has poor bioavailability on its own. Are there other examples of other compounds other than the pharmaceutical drugs that have been shown to have increased bioavailability and target ability when combined with this nanoparticle technology that you're using in your lab?
Elizabeth: Yeah. There's certainly things like antibodies that don't have great efficacy or bioavailability on their own or just don't have site-specific uptake. If you think about pretty much all cancer drugs, all cancer drugs are generally insoluble and they're also incredibly toxic when they're applied to a site of interest. One of the benefits of sticking them into nanoparticle is not just to increase their solubility, but also to better control where they go.
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For instance, a good example of this is just if you're looking at maybe cancers that would affect the kidney or the liver, just by tailoring the size and the shape of the nanoparticle alone, nothing else but the nanoparticle, you can actually control where it accumulates because you're taking advantage of the natural cut-offs for diameter molecular weight of the filtration of those organs. You can better direct just by kind of controlling some of these physical properties of a nanoparticle.
You're not just improving the solubility of the drug, you're also, in many cases, better to direct the localization of that drug by altering basically the vehicle in which that drug is being carried. One of the reasons that nanotechnology has been so heavily applied in cancer is for that exact purpose because those drugs are-- They have poor solubility, but they also are so toxic when they don't go to the site of interest, that even just increasing their site specificity, even just to some extent to reduce a toxicity, even if you're not necessarily making the drug on the whole more effective, you're still doing something because you're reducing that drug going to all other cells where it's going to act indiscriminately the same way it would act on a cancer cell and kill those cells or calls other damage. There's actually benefit in multiple ways I think to using a lot of these nanotechnology platforms to get otherwise hard to deliver drugs or poorly soluble drugs into the body in a safer way.
Megan: Oh wow, that is fascinating. Are there clinical applications that are being used right now or is this largely kind of theory-based in a laboratory setting in the context of cancer and that kind of thing?
Elizabeth: I think right now, there are 51 different nanoparticles that are FDA approved and in use in humans and almost all of those are for cancer applications. Now, all of those are not for therapy alone, many of them are for diagnostics. Thinking about iron oxide nanoparticles are great contrast agents, gold nanoparticles provide contrast. There's definitely nanoparticles that are being used in a diagnostic setting, not just in a therapeutic setting.
But the majority of these systems, at least the last I checked, I think this is data from 2016, so it might be the most recent that we have, there's about 51 unique nanoparticle materials or platforms out there that are being used and the majority of those are for cancer. Even if you just look at the National Cancer Institute's current number of clinical trials with anything nano-based just for cancer, I think there's 91 active clinical trials. That's just looking at the cancer field, there's certainly others in disease areas like in arthritis and atherosclerosis and heart disease, certainly in Alzheimer's and Parkinson's.
There's certainly a growing amount of promise of nanotechnology in being used to treat or better diagnose a lot of these diseases. One of I think the limitations is that these are still, these are very complex systems we're putting these nanoparticles into and we don't always well understand their fate and how long they actually stay or reside in a tissue space. There's certainly longitudinal questions I think that a lot of people are still trying to explore and sort out. There's this challenge of designing nanoparticles for something specific that we don't fully understand yet.
There's a lot of really good research around that, but there's still enough unknowns that we can't quite always predict. How these particles are going to behave or if they're going to retain their efficacy when you go from small animal models up to the complexity and heterogeneity of humans. Particularly, even within one human disease, you have a huge range of how that disease progresses. There's still a lot of work to be done. I think there's ways to kind of address that work, but it does require intentional effort and a lot of stubbornness I think to do.
Megan: Yeah, for sure. That kind of leads me into one of the other things I wanted to talk about which was this idea that one of the problems was studying some biological processes is that some of the science just can't be done in humans, whether it's for logistical purposes or practical reasons or ethical reasons. We have animal models which are great, but they're certainly not without their downsides. On that topic, I have two questions for you in the context of your research. One is, is the fundamental process of diffusion the same in animals and humans? Then secondly, how do we know that what is done a small scale in the lab is mirroring real-time diffusion in human being or do we even know that?
Elizabeth: Yeah. Those are both great questions. I think really important ones that we always have to keep in mind because relevance I think when we're engineering things and we're trying to develop technologies to be used in human health, we have to keep the end goal in mind and we have to keep the end goal in context.
Fortunately, diffusion is conserved across all species. The main limitation of diffusion, even in the fact that it's independent over the organism or environment, is that it is length limited. It's only efficient over short length scales. But considering that all cells require oxygen and nutrients for basic functioning, diffusion is still conserved between animal models and humans.
[0:40:05]
However, when you take that concept and apply it to therapeutics, therapeutics unlike oxygen don't need to act on every cell and if that shouldn't act on every call. While diffusion is a critical component of their transport to a target cell, therapeutics often have to act more specifically in larger volumes. If you think about the difference between a mouse and a human brain, the adult mouse brain is about .4 centimeters cubed and the adult human brain is 1,200 centimeters cube. This is like a 3,000-fold increase in volume alone.
That's not an insignificant factor to account for, but it also is in line with increasing complexity of the brain as well. Even though diffusion is evolutionarily there to ensure that all of these cells in both the mouse and the human, it can efficiently get oxygen. When you're kind of thinking about cell specificity and site specificity with their therapeutics, you start to kind of reach the upper limits of diffusion. That's where I think we have to be really considerate and thoughtful about how we're taking the information we get about the way that therapeutics diffuse and translating it to larger scales.
One of the things that we've tried to do in our own research, to really account for this kind of gets to your other question about how do we know what's done in a small scale actually translates to a human being. I think that there's a couple of important things to note first that I should just qualify this with. We know that there's no single animal model that replicates human disease at all. While there are animal models that represent a single aspect of human disease, we know that humans are highly variable.
The heterogeneity of human disease is much higher than it is in animal models, even though it is also heterogeneous in animal models. One of the reasons for that is that humans are highly variable themselves. They're in largely uncontrolled environments, so you have people in different geographical regions, you have people with different-- That are exposed to different toxins, they're exposed to different levels of sunlight, they are exposed to different vitamins and nutrients. All these things are going to affect how that human operates and potentially how disease occurs in that human.
In a lab setting, your animals in an experiment are all exposed to the exact same thing, so they're in very controlled environment. That's not something we can really account for in animal models is that kind of uncontrolled environmental exposure, at least not in an unstructured or unsystematic way. The thing that we try to do is to work in multiple models that have common disease aspects. When we're thinking about diffusion, we know that is conserved. Then we want to look at diffusion in the context of a disease process that underlies multiple types of neurological disease.
A good example for this is looking at the neonatal brain. Children can develop something like cerebral palsy from a wide variety of factors that can occur before, during or after birth. There's a high correlation between infection, between a hypoxic ischemic event, between an asphyxia event that can lead to increased risk cerebral palsy. The way you would model those in a human would have to be you model infection, you model hypoxic ischemia, you model asphyxia.
But the way you would model these in an animal is through each one of those individually. The way that occurs in a human is much harder to pin down. One of the reasons that we like to try and study the questions we're asking and the resulting therapeutics were trying to deliver in multiple models is to try and account for the fact that we need to replicate, at least to some extent, the heterogeneity of disease. But we want to do it with some lower hanging fruit so we're not just exploring all things at all times because that gets really costly.
One of the things that we have done in working in multiple models have a common disease aspect. I'm thinking, when I say common disease aspects, things like inflammation and oxidative stress and cell death. We know that these things occur, these processes occur in almost all neurological diseases. We want to work in models that also replicate those disease processes. Then we want to work in a couple of different species of those diseases-- That have those disease processes because we want to approach the level of heterogeneity that our human would have.
Then sort of drawing these conclusions if we study something in these different models and in these different species, then it allows us to identify commonalities across those models, which might be able to give us better educated guesses for things. It might be able to narrow down how much of a drug we would dose, when we would dose, how to dose it.
For instance, if we're applying a therapeutic platform to a new model because we can correlate two aspects or common aspects that we saw in the multiple models we studied before to say, "Okay. We know that if we really want to get a drug to attenuate inflammation in the brain, we absolutely have to have the blood brain barrier impaired and we have to have certain cell types activated that can uptake these particles." Those both need to be at their peak. If they're not at their peak, we're not going to get maximal efficacy.
[0:45:18]
That gives us at least a starting point in a new model to make it more efficient to say we can at least give an educated guess of where to start with the dosing and the timing of the dosing and the administration of that dose, based on these sort of biological factors that we have identified are common in all the other models that we looked at. It's risky, but I think there's ways to minimize that risk to better be able to replicate the complexity that we see in humans.
Megan: You're basically, you're using the clues that you get from studying these different animal models and putting those together to kind of allow yourself a bigger picture of what's going on?
Elizabeth: Right. With the assumption that no one model is going to give us a clear enough picture on its own. We've actually, this has been an effective approach for us. This approach sort of came to being for me when I was a postdoc. In my postdoc, I worked on eight different animal models that had neuroinflammation as a hallmark of the disease and they were all pediatric brain injury-focus models.
The work that I did as well as the work that many others in my postdoc labs did and my postdoc advisors did eventually led to approval for one of our nanoparticle platforms to go into clinical trials for an orphan disease. It's childhood cerebral adrenoleukodystrophy which is a fatal disease. By the end of the kind of however many years, it's probably close to ten years of study that my postdoc advisors did with those eight models that I got to work on, as well as I think they did a total of 15 different models in five or six different species.
They were able to sort of verify that with different ideologies, different disease progressions, different ways of kind of capturing the complexity of something like cerebral palsy or something like neonatal hypoxic ischemia, we can actually move a therapeutic from the bench into a very difficult population of people to treat. Clinical trials in kids is very, very challenging. This was kind of a proof concept. Yes, there is actually quite a bit of benefit in being intentional about working in multiple different species and multiple different models that have common disease aspects that are a key part of a specific type of human disease.
That shows that there's a lot of promise to doing this approach, but it also shows the necessity of being highly collaborative and being highly interdisciplinary. Because we were not doing-- At least I wasn't doing all those eight animal models in our own postdoc lab, like five of those were models that were through collaborators and who had an interest, a sort of shared interest in the end goal, which was to provide better therapy for these kids with these diseases.
It definitely requires collaboration and inter-discipline. It cannot be done by any single person. But that intentional approach of saying, "Look, we've got all these different models of disease, let's find what's common between them and let's see how that impacts our ability to develop and deliver a more efficacious drug," I think is an important approach that we need to be doing more of, at least in the context of developing therapies for brain injury.
Megan: Yeah, for sure. I completely agree. That's actually a really good segue into your TED Talk that you gave a while back and it was titled Specializing in Not Specializing. I have to say, it was one of my favorite TED Talks that I've ever listened to. I'm not just saying that because I'm talking to you, it was truly resonated with me. A big take-home message that I got out of it was that the sum of integrating multiple systems and schools of thought is much greater than what each individual system or school of thought has to offer on its own. Can you kind of talk about what you mean by specializing in not specializing?
Elizabeth: Yeah. This sort of spawned out of the challenges I faced in the approach that I wanted to take, even from very early on in my PhD and then finding a postdoc, but it was a very non-traditional postdoc for an engineer to go into and in even searching for faculty positions. When I was proposing work that really set at the interfaces of so many different fields and I had a few faculty tell me that I was just a glorified matchmaker.
This is something I'm very passionate about because I believe that the idea of this specializing and not specializing is actually important for performing interdisciplinary research in a successful way, and for tackling the magnitude and complexity of the challenges we face in health. To me, this means that we have to look at problems that we're trying to tackle from different and many times contradictory views than our own.
[0:49:52]
To do this, it requires us to be able to step outside of the walls in which we exist, the field that we've been trained in, to get away from the language, even the jargon that we use at conferences, to go to people that we might see mainly on paper have no shared interest or shared knowledge with to get input from. This requires an actual active and intentional decision-making process that I think should pervade pretty much every aspect of your scientific life.
I think that the reason for that it's not done that commonly is because it's really difficult to do. I mean, if you look-- You go to a scientific talk, within the first few minutes, what you get inundated with its jargon. Language alone as we know, just from life in general can easily be one of the biggest roadblocks to being able to effectively communicate with somebody. If you can't effectively communicate with somebody, it's really then difficult to find where there are points of overlap or where there are ideas or tools that could actually be tailored or adapted in a new way.
When I think about this sort of concept of not specializing or actually becoming good at not specializing, part of that is being able to have ownership over language and ideas and research areas that you can make amenable to a wide variety of audiences, independent of what their background is. Part of that requires that you don't make any assumptions about their background, but part of that also requires that you're kind of coming to the table, so to say, to kind of learn something from them.
To say that, "Okay, they have something of value to me, I might not know what that value is, but I'm going to be as open minded as possible so that I can identify where there's common points of connection or where maybe there's something that they're doing that I can sort of take and adapt and apply in a new way. Maybe there's something that I'm doing that they could take and adapt and apply in a new way based on the information that we both have learned from our own fields."
My dream for science is that there's a lot more intentional effort put into this, unless lip service given to it. Because I think it's really easy to say that you're interdisciplinary, but that just implies that you're collaborating with a lot of different people. It doesn't actually imply that you're learning language of those fields, you're trying to understand the approach of those fields to your problem that you're trying to solve.
Even if that problem is the same problem, they're trying to solve the way that you would go about solving that problem is naturally going to be very different based on the backgrounds that you have. Trying to have some appreciation for that and see where there's resources or tools that could help you really be able to bridge those fields even further and really actually integrate them is a very, very valuable thing for us to be focusing on. But it's very counter to how most science typically runs.
Megan: Yeah. I think it's not only counter to how most science runs, but it's also almost counter to--
Elizabeth: Life.
Megan: Yeah, to life and into our intuition because taking chances and being wrong in the context of interdisciplinary collaboration which certainly happens. Like you can't be an expert in everything and you kind of have to set the ego aside. That certainly goes counter to just the human state. I think this is kind of all part of what is-- Kind of notice the growth mindset in a noble way. We can link to that book and our show notes, but it certainly is-- I think that it's going to be this kind of crossing disciplines. Collaboration is going to be the thing that allows us to make probably the most progress in the future.
Elizabeth: The collaboration of component is important, but to really integrate those collaborations, I think we have to kind of own the complexity of the space that we're in and not shy away from that, not knowing things. Like what you were saying, you have to put ego aside. I realize every day like I know very little and every day, I pretty much learn more about what I don't know then about what I do know.
A lot of how I go about working-- We have I think 15 different collaborators in a huge variety of departments and they're all very valuable I think to the efforts that we're doing. Because we can understand something like diffusion in the brain very well, but as soon as we start to perturb or tweak this system, we want to understand it in the context of that perturbation, but also without losing the side of the whole system.
Our ability to keep both the very, very delved in like specific perturbation that we're doing in mind with the whole system in mind requires us to bring in knowledge and to get input and insights. To really understand how somebody else use that system or how somebody else sees that perturbation and what the limitations of those are, and what the potential of those are, and where there's big unknowns in both of them.
It requires a continual two-way relationship that I think is really easy for us to lose sight of because things are so complex. Because there's these language barriers to a lot of fields and mindset approach barriers like the way that an engineer approaches a problem is that different than the way the clinician approach is a problem and it's different than the way to basic scientist approaches a problem.
[0:55:03]
That's totally fine. I think that's all actually, very valuable. They're just has to then be intentional effort to say, "Okay yeah, we do approach all these things differently, but we have the same shared goal." There is a lot I think of benefit to putting effort into finding how and where those three different ways of approaching something actually marry together or could marry together if they're not already married together.
Megan: Right. It's the integration part that becomes key right there.
Elizabeth: Right. Yeah.
Megan: I know you're a mentor to a lot of future scientists, so how do you teach students to communicate and collaborate and integrate between disciplines, and also keep an open mind and ask the right questions? Because that's not something that's easy to learn or easy to teach, I would assume, so how do you do that?
Elizabeth: You're right. I don't know if I have the perfect ways or even good ways of teaching it. I do know that I try to model it as best as possible. But I think that in modeling it, there's an aspect that's really easily overlooked and that's actually communicating what you're modeling. One of the things that I try to do as much as possible is be transparent about what I'm doing. I think it's really easy in academia to feel like you need to be an expert and that you need to know everything at all times.
That's true in the lab, it's true in the classroom, it's true when you're giving seminars, it's true in your writing grants. I think it's actually very freeing to tell students and show them that you feel similarly that this stuff is really, really difficult to do and we know very, very little about it. But we're going to ask questions that give us insights. Maybe they don't have a right or wrong answer and that's okay, but we're trying to just gather information about how to better understand the various pieces of the puzzles that are there and then how they fit together.
Part of that is actually acknowledging and being transparent about the fact that you are acknowledging that you really don't know everything and that you have times when you're very uncertain about how all this is going to fit together and what the resulting outcome is going to be. But you kind of see that as an opportunity to say, "Okay. Well, what is it about this that's making us uncertain? What do we not know?" Who are going to be the best people for us to talk to to say, "Yeah, this is something you don't know."
When I actually think that's a step that's easily overlooked, a lot of our collaborators, I'll go to them and I'm saying, I'll say like, "Okay. We have this problem, we can't make sense of this data, but I think it's just because we don't know this. Is that true?" They're like, "Yeah, it'd be helpful if you did this and this because we don't know the answer to this question right now." You can't really interpret your data until you know the answer to this question, but they are interested in the data because we have data for them.
To me, it's very much about not just modeling the sort of ways of communicating and collaborating, but also being transparent about what you are actually modeling. That part of being transparent is being open to the fact that this is very difficult spaces that we're working in that we don't know necessarily what's going to be successful, but we'll try and mitigate our risk as much as possible. That we really don't know a lot of things and that's okay because there are ways to frame questions that are in the context of just gathering information, not so much in the context of saying, "We have to have this exact answer to move forward with."
Megan: Science, based in my experience, it often opens more doors than it closes as far as questions are concerns. I think that this communication and collaboration and transparency certainly goes beyond just the science and that kind of seeps into our everyday lives and relationships and how we see the world, and whether we're able or not able to kind of see this perspectives of others. I think that modeling that for your students definitely goes beyond just the lab.
Elizabeth: Yeah, that's what I really hope. One of the things that at least I try and focus on a lot in my group is the fact that it is the research setting, this lab that we're in, is very much like a second home and like an extended family. That ensures that we are thinking about each other as an actual community, that it's not just something that people are kind of touching and going with in their life. That's a natural space for people to grow and to explore, not just the science we're involved with, but also who they are and the context that science and who they are and where they're going with their career.
Part of modeling that transparency and that desire to be willing to communicate even when it's difficult I think is to try and get students to see that there are ways to apply this to all of life and that there's no point-- Science to me is a really good space to kind of explore some of the more difficulties of life which is there's a lot of things you can't predict. You're going to do stuff and it's going to fail a lot of the times.
Hindsight is almost always 20/20. There's so many lessons that you can learn from the scientific space, but what I think is necessary to really enable true interdisciplinary research is to enable freedom for people to fail, enable freedom for people to explore who they are within that space. Because role identification is important in interdisciplinary research, knowing what each person brings to the table I think is an important component of actually moving interdisciplinary research forward. But coming to the table of assumptions about what that role is going to be I think limits the potential impact of where that interdisciplinary research could go.
[1:00:24]
Part of giving a space for people to explore who they are is also giving them a space to figure out what role do they want to play and what role are they best at playing, and is it a role that they then want to pursue not just in the lab, but in their next steps or in their career beyond what they're doing in at least our lab setting. That to me also accounts for a lot of things that are really important like having diverse views, having diverse backgrounds, being inclusive about those views.
These are all things that we try and enable as much as possible because I truly believe that's the best way to get interdisciplinary science moving forward. That's of course with the assumption that you have to have interdisciplinary science, to have impact in health. But given how complicated humans are and human diseases, I don't see how we can avoid having many, many different fields involved.
Megan: Yeah. Absolutely. I could not agree more. We'll certainly link to your TED Talk in the show notes and I would encourage everybody to go get that and listen. This conversation and your TED Talk has kind of made me want to go back and get my PhD, but then I'm also realizing what you're talking about is not like we've just said, combined to the academic research setting.
I think that we're already kind of specializing and not specializing at Nourish Balance Thrive and trying to bridge the gap between disciplines that the Blood Chemistry Calculator that Dr. Tommy Wood, Bryan Walsh and myself have worked on is kind of an example of this and are hoping that we can do that, integrating of different disciplines more of that in the future.
Elizabeth: Right. Yeah. Actually, I think it's a great example of ways in which you're not making a lot of assumptions of things both in the tools and the technologies you're using like machine learning and data science, as well as in the assumptions about what the data is going to show based on your own personal experiences. I think that one of the things we're most excited about, at least in my research area, is how we can also bring in tools that are largely on biased, like these machine learning algorithms, to really elevate the information we're getting out of the data.
Because as scientists ourselves, we're going to naturally have bias to what we think the data might show or what the data could mean. Bringing in some of these tools that are kind of cross-cutting and interdisciplinary to really check ourselves and hold ourselves accountable that we're not introducing too much bias, but also potentially getting more useful and more relevant information out of it by being able to pull in data sets from many different places, many different people in many different forms, to actually get more useful information, even about something like diffusion in the brain, I think is a really kind of exciting area and hopefully one that will keep growing with a number of people that kind of become aware of the power of these data science tools and machine learning tools. I think that's a really neat area that you all are modeling. I'm also partly inspired what we got into with some of the machine learning and data science tools with analyzing our diffusion data. I think it's something that can easily be translated to a lot of different areas.
Megan: Yeah, absolutely. I've been pleasantly surprised at how using the machine learning algorithms in the context of the Blood Chemistry Calculator has provided us further insight, and I'm sure it will do the same for your lab and in other disciplines as well.
Elizabeth: Right. Yeah.
Megan: Is there anything that we haven't covered that you think that our listeners should know about, either your research or your TED Talk or anything else?
Elizabeth: That's a great question. I'm trying to think. There's definitely scientific aspects that I think that are interesting to kind of tie into this context of how-- Like what you were just talking about with the machine learning and data science, that I think one of the biggest challenges that our field, and by our field, I mean both the nanotechnology field and those that are trying to treat brain injury, need to focus on is ways of pulling in data from multiple sources and that covers multiple scales, and actually finding ways to integrate that data together and produce useful information.
One of the things that like I was mentioning, we're really excited about is how we can use things like data science tools and machine learning to take something like diffusion data, integrate it with biological data, looking at RNA and protein expression profiles, and cell number and cell density and cell morphology. Then applying that to a machine learning algorithm in such a way that we could then predict physiological data.
That requires us integrating information from our way of assessing nanoparticle diffusion to these biological datasets, to even some functional datasets where you look at how changes in a local environment that we see based on how a particle diffuses, actually correlates to a change in how plastic neurons are. Kind of pulling all that information together which a lot of it exists out in the world right now and actually integrating it to look at something like structure function in a tissue, in the brain for instance, and how small changes and structure could lead to a large changes in function.
If there's a way we can predict that, I think is a really interesting area. I know that some of these data science and machine learning tools are really becoming cornerstones of a lot of people's approaches to try and do things like that to integrate multi-scale information. I'm really excited to see kind of where that goes, both within our own work, but also within the field in general.
[1:05:50]
Megan: Yeah, that's amazing. I'm really excited to follow your work and see how you guys are able to integrate the machine learning algorithms and data sets that we don't even know are out there yet to further your research. That's awesome. Last question for you, where can people learn more about you and follow your research that you're doing at University of Washington?
Elizabeth: Yeah. We have a couple of different ways. Probably the easiest way to get to all of them is through our lab website. It's just nancelab.com. From there, we have a blog that we post on about both scientific stuff, but also our approaches to everything from teaching some of our work to high school students and elementary school students, to how we work on building an inclusive community, to featuring some of the things that each individual student in the lab is doing.
We also have, well, up and running as of today, Facebook account, but then we have a longer standing Instagram account, but all of those are accessible through our website at nancelab.com. That's probably the best way to stay on top of what's going on in our-- At least in our world.
Megan: Awesome. That is perfect. Well, this has been great. I've really, really enjoyed our conversation. Thank you so much for your time and I will definitely have you back on the podcast in the near future.
Elizabeth: Thank you very much. I appreciate it, Megan.
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