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Sounds of Science is a monthly podcast about beginnings: how a molecule becomes a drug, how a rodent elucidates a disease pathway, how a horseshoe crab morphs into an infection fighter. The podcast is produced by Eureka, the scientific blog of Charles River, a contract research organization for drug discovery and development. Tune in and begin the journey.
Mary Parker:
I am Mary Parker, and welcome to this episode of Eureka's Sounds of Science. Today, we are looking ahead at the coming year. I am joined by several experts who will tell me all about what they think will be the next big thing in their field and offer their predictions for the industry at large. First up is Dr. Lauren Black, distinguished scientist for Charles River. Dr. Black is recognized as a leader in the rare disease community and can tell us what may be in store for the coming year on the rare disease front. Welcome, Lauren.
Lauren Black:
Good morning, Mary. How are you?
Mary Parker:
I'm not doing too bad. So what, if anything, do you think could change for the rare disease drug development in the coming year or so?
Lauren Black:
Wow, that's really hard to keep up with all the changes actually, so I can just scratch the surface. I'm involved in gene therapies and oligonucleotides and siRNAs, as well as other advanced medicines, including cell therapy. And what we're seeing is, of course, the continued development of CAR T-cell products for advanced cancers. Those have been rolling along for the last decade with a couple of setbacks recently. But generally speaking, amazing miraculous therapies for people that would otherwise die from their cancers. But in the oligonucleotides, in the siRNA fields, we've seen a sudden spurt of approvals for oligonucleotides, including tofersen for ALS or Lou Gehrig's disease recently, that is only the second intrathecally or direct CNS-delivered oligonucleotide that's been approved so far. The first one was nusinersen for spinal muscular atrophy, but that's our second intrathecal oligonucleotide. And then, we've got, I think, all total, there's been about 15 oligos approved so far, and several of those have been escalating in frequency in terms of approvals in the last two years.
Mary Parker:
So have you noticed a shift in the industry or in society, regarding rare diseases?
Lauren Black:
I think it's much more on people's mind. That one in five of us that's going to develop a rare disease is starting to resonate. If it isn't you, it's going to be someone in your family. If it's not in your immediate family, it's going to be one of your first cousins or aunts or uncles. And that just means that a rare disease is going to have a genetic basis. And if you're lucky, it has a diagnosis, and if you're extremely lucky, it has a drug. But it's going to touch all of our lives intimately. And I think that message has started to permeate societal awareness, and I hope it penetrates into Congress and all the other parties that have to help determine our public policies toward these things. But certainly, people in general are getting to be more aware of this.
Mary Parker:
Do you think there will be more action in gene therapies or in oligonucleotides in the next year or two? And also, which of those two is maybe more affordable, even if it's just by pennies?
Lauren Black:
It's really hard to know. It depends on what disease you're going after, how many patients there are, how many you can enroll in the trial. That bears a lot on the practicality of how long the clinical trials take to enroll and the statistics to be robust enough. The oligonucleotides, I think, are somewhat more practical from the potential of the manufacturing aspects are much more simplistic than trying to generate a gene therapy vector product. So I would say, from the perspective of that, oligonucleotides are a little bit more straightforward, but the penetration, and what I mean by that is how much you can really upend the disease and reach for the cure, is actually the big one is actually in a gene therapy that's replacing a gene that someone lacked and that is in a critical pathway. So they're different from the perspective of what the end game is. And you could say that the gene therapies are high risk, high reward. When they work, they're beautiful. And oligonucleotides sometimes have trouble getting to the site of action and sometimes only cause a partial efficacy, if you'll.
Mary Parker:
Yeah.
Lauren Black:
I think what we have to keep in mind with that is that all of the diseases that we're going after are severely debilitating for these products. They're not for your average pain in the knee kind of disease.
Mary Parker:
Right. Are there any specific changes you would hope to see on the regulatory side, that could better support innovation in rare disease research? I know you're also an expert at regulations.
Lauren Black:
Yeah, I've had to deal with a lot of it over the years, when I worked there. And subsequently, we work on a number of different INDs just working in the back room, behind our sponsors, to try to support them. So we hear about the FDA opinions over time as we read these letters. And what I can say is that Center for Biologics especially has been putting it out there that they're going to get on the rare disease bandwagon. Peter Marks, who's head of Center for Biologics, has recently announced a program, called SMART, is basically modeled after the idea with the success scene with... What do they call it? The full court press to try to develop COVID vaccines.
Mary Parker:
Oh, Project Moonshot, I want to say
Lauren Black:
Yeah, yeah, yeah, that's it. I knew it was one of those words.
Mary Parker:
Doesn't it seem like it was a million years ago?
Lauren Black:
That's why also it's not my short term memory. Good job, you.
Mary Parker:
Thank you.
Lauren Black:
Project Moonshot. So basically, they're restyling a rare disease full court press after that. And Peter Marks intends to hire a bunch of new reviewers to provide really advanced handholding for people that are in academics, for people that are in small companies, in collaborations, or foundations that are developing these drugs. That's why I love working in rare disease is you feel like you're part of that circle where you're all holding hands.
And instead of singing, you're doing science, you're doing science for the greater good. And it bears in mind that all of the advances that we're doing in these kinds of advanced diseases, every gene we fix gives us some learning about another disease that's possibly related to that genetic pathway, but not exactly that gene. So for every ALS drug that we learn something about from an oligonucleotide tells us something about a gene therapy for a different kind of ALS. You see what I mean?
Mary Parker:
Yeah.
Lauren Black:
You can cross over between the different products for the same diseases, and you can put together the learnings from both. Center for Biologics and Center for Drugs are two different entities, but the disease community is the same. They're product agnostic. They just want to fix it. And the academics that are learning about the pathways can look at either kind of trial and learn exactly what's going on in humans and how they should develop new drugs. So I do think that the efforts, although when I say four new drug approvals, maybe that's not very impressive to some people, but it's hugely impressive when you think that, the previous years, there was four across four years, and now, we've got four in one year.
You have to look at it relativistically as to the momentum that's starting to build. This technology of using AAV gene therapies, it's only been in use, count them, a double handful of years. Give us a break on the speed here. I just heard from somebody that works in genetics last weekend, that was talking to them over cookie baking, and they said that you can now get a whole genome sequencing for under a thousand dollars.
Mary Parker:
Wow.
Lauren Black:
Didn't it take us 20 years to sequence one person not so long ago?
Mary Parker:
Yeah.
Lauren Black:
And then, there was a huge foofaraw over it when we heard about Stanford and a bunch of other supercomputers that were networked together a year or two ago, and they were able to sequence one child who was in acute emergency, I think within the day, by linking up a bunch of different centers to do the data analysis on the genome super fast. That's what takes so much time, usually.
Mary Parker:
Yeah.
Lauren Black:
It's fascinating to know that, with the coupled efforts of so many different institutions trying to make genome sequencing more manageable, more accessible to people in remote vicinities, and then, trying to get the diagnoses earlier in patients' lives, in what's called their diagnostic odyssey. It takes 10 years or five years sometimes to get these diagnoses, when we move these calendars ahead and start to get more immediate screening, and of course, we look forward to a time in which we take a drop of blood from a newborn and just go ahead and sequence the kid. I would sign up for that immediately. God knows what's lurking. And if you could get ahead of it, why wouldn't you want to know?
Mary Parker:
Absolutely. Next up is Dr. Anjili Venkateswaran, Senior Director of Strategic Partnerships for Charles River. She is an expert on scouting new technologies and emerging trends that could be important for connecting Charles River with the next big advance in drug discovery. She's here to discuss the topic of antibody drug conjugates, which could be big for cancer treatment in the next year or two. Welcome, Anjili.
Anjli Venkateswaran:
Thank you, Mary. Thank you for having me.
Mary Parker:
Oh, thank you so much for being here, again, because this is your second time on the podcast.
Anjli Venkateswaran:
Always a pleasure.
Mary Parker:
So what exactly is an antibody drug conjugate?
Anjli Venkateswaran:
So antibody drug conjugates are a really interesting class of modalities, because they combine payloads, typically chemotherapeutics, with an antibody, and both of those modalities have been around for a long, long time. But what ADCs do is they kind of combine them and link them together. So here's a fun fact. While the drug development space has been really interested in ADCs recently, ADCs have been around for a long, long time. And if I recall, the first ADC to be approved was back in the 1980s. The first ADC clinical trial was in the eighties, and then, there's been almost, I think, over a dozen ADCs that have been approved since that timeframe. However, in the early days, ADCs kind of got a bad rap, because they're made up of three individual components. So you've got your targeting antibody, which is essentially analogous to an engine on a train, tells you where you need to go, drives the drug to your target, which is a cancer cell, and then, there's a linker, which basically links the antibody to your payload.
Now, in the previous iterations of ADCs, the linker technology was not where it should have been. There were instances where the payload, which is a toxic drug, a chemotherapy, would fall off and cause a lot of off-target effects. So ADCs kind of got a bit of a bad reputation there. But thanks to developments in antibodied engineering, in linker chemistry, and a creative design of payload, ADCs are really experiencing a renaissance of sorts. I really do believe that this is a modality that's only going to continue growing. And when you see the number of ADCs that are being approved and the buzz around them, it's pretty clear that I think ADCs are going to be here to stay.
Mary Parker:
And the most basic terms, an ADC or antibody drug conjugate, will take something like a chemotherapy and be able to target it to the cancer cells and theoretically bypass all the other healthy cells, yeah?
Anjli Venkateswaran:
Yes. That's exactly right. So ADCs, the most important element is that targeting concept. And so, that's where the target of interest comes in and the antibody specificity comes in. But there has been quite a bit of success in specific cancer types, like ovarian, triple negative breast cancer, and most importantly, in hematological malignancies, in other words, leukemias, different types of lymphomas, that's where there's been a ton of success. So essentially, we're looking at leukemias, lymphomas, and gynecological malignancies is where we've seen the most success to date.
Mary Parker:
That's really great, because correct me if I'm wrong, but things like the blood cancers can be a little harder to treat, because they can't be touched physically. It's the whole body, if it affects all of the blood.
Anjli Venkateswaran:
Exactly.
Mary Parker:
Yeah.
Anjli Venkateswaran:
Exactly.
Mary Parker:
And you could also, in theory, deliver higher doses of chemotherapy, since it won't be affecting the healthy cells in theory, so it won't cause as many side effects, if you deliver higher, more toxic doses.
Anjli Venkateswaran:
Absolutely. That's absolutely true. The other really cool thing about ADCs is you are able to tweak multiple components. You're able to tweak the antibody to engineer an antibody with a maximum specificity. You are able to really tweak and improve the linker, so that the payload doesn't fall off prematurely or inappropriately. And you're also able to creatively design the payload. In other words, you don't have to only use chemotherapy. In fact, there is a recently approved ADC, the brand name is Lumoxiti, that targets hairy cell leukemia. And its payload is actually a bacterial toxin, but at the end of the day, it does the same thing. It goes in, and it basically kills the tumor cells from the inside out.
Mary Parker:
So what does it look like on the regulatory side? Are they seeing more INDs for this type of technology? Are they getting excited about it?
Anjli Venkateswaran:
Yeah. There's a few things that point to a really exciting regulatory landscape. First off, there's over a dozen ADCs that have been approved, and the number changes by a few depending on... Because keep in mind, some of the early ADCs, there was a couple of them that were discontinued, and then, one was re-approved. So the number shifts a little bit, but definitely over a dozen FDA-approved ADCs that are available today. And there are well over 150 ADCs in clinical trial, which basically means all those INDs were approved and they're now in the clinic. And the preclinical pipeline is just looking bigger and better. The reason for this is because, again, with the improvement of these individual components, and for some of the parts, there are a lot of oncology drug development companies who are looking at ADCs going, "Wow, this is a modality that works, if the right components are put together." And in fact, we're seeing a lot of pharma companies, like Pfizer, AstraZeneca, who have publicly announced acquisition or asset deals for ADCS.
And so, that to me is a big indication, when big pharma is interested in developing and commercializing ADCs, that to me suggests that they definitely have the knowledge on how to get a therapeutic asset from preclinical through IND into the clinic and into approval. So very, very positive trends, and definitely, the data is really, really compelling. I'll give you an example. There was one of the ADCs that caused a lot of buzz was an ADC for HER-2-positive metastatic breast cancer called ENHERTU. And the phase three clinical data for this particular ADC showed a pretty amazing 72% reduction in disease progression. That's kind of amazing. And so, again, when you look at these kind of data, you're like, "Wow, these ADCs really work."
Mary Parker:
Yeah. And can you go into more detail on some of the more recent technological advances in antibody engineering that have led to this kind of boom?
Anjli Venkateswaran:
Yeah, therapeutic antibodies have been around for a long, long time, but increasingly, we are seeing just more sophisticated screening methodologies. We've come a long way from the traditional hybridomas and the instability of hybridomas and things like that. Now, we are looking at extremely sophisticated [inaudible 00:17:37] methods, which is basically a simple way of saying you're screening for high affinity antibody binders, you're looking at individual cell screening, and you're able to really zoom in. There's a lot of publications and a lot of platforms out there that allow you to zoom in at the individual B-cell level to find those diamonds, find those antibodies, that really have high affinity and high specificity. So I anticipate, again, as these methods move beyond R&D into commercial applications and with collaborations between engineers and antibody scientists and bioinformatics people to analyze the data, we are just going to see a boom, I believe, in the next generation of antibodies that are really going to surpass anything that we've ever seen.
Mary Parker:
David Clark is a senior research leader in Charles River's computer-aided drug design group in the UK, also known as CADD. He has spoken with me before about how artificial intelligence and machine learning can help the drug development process. With AI making more headlines every day, he's here to predict what could be hot for 2024 in the world of AI and CADD in general. Welcome, David.
David Clark:
Thank you, Mary.
Mary Parker:
Thanks for joining me. So what are some of the ways that AI can speed up drug research in 2024?
David Clark:
Well, I think that we potentially numerous ways, and I'm sure I won't enumerate all of them, but I have a few, it's sort of at the front of my mind. And the first of which is the AI-based techniques for protein structure prediction. So I'm talking about things like AlphaFold and RoseTTAFold. They've already had a marked impact on numerous areas of structural biology and drug design. I think there's only one way forward to that, and that's upwards, because they haven't sat still, those people developing those methods. There were some criticisms of sort of the early versions, but now, they're coming back with enhancements that are due to, I believe, be released in 2024. So that will include things like protein models, that include water molecules and metal ions, and even positioning ligands within protein structures, and also, being able to predict the structures of protein complexes, so molecular entities that contain more than one protein. And that's, again, something that was a bit of a limitation in the early versions .
Mary Parker:
Now, correct me if I'm wrong, but just to give a very thumbnail sketch version of the problem, proteins are folded in numerous different ways, making them quite complex and kind of hard for us to visualize, but it's important for us to know the shape, so that we can find drugs that can attach to them?
David Clark:
Yes, that's right.
Mary Parker:
Okay.
David Clark:
So protein function is determined by protein structure, and as you say, it's a hugely complex problem. So starting from, essentially, just the sequence, the amino acid sequence of a protein, to try to predict how that sequence will fold into its three-dimensional shape, which will enable it to have its function in the body, and this is something that, obviously, nature has solved very efficiently, because proteins fold very rapidly. But until really the advent of AlphaFold, maybe I think two or three years ago now, it was still a big challenge.
Mary Parker:
So what other aspects of computer-aided drug design do you hope to see progress in in the next year?
David Clark:
There are a few things still sort of under the AI area that I'd like to see progress in. So there's been a lot of development in these techniques, so-called generative AI for drug design. Now, generative AI has kind of entered a bit more into the public consciousness, with things like ChatGPT, which are generative AI for text, which we're all very familiar with. But in the field of drug design, the generative AIs are used for actually generating chemical structures, so new ideas for drug molecules. So you can train a neural network to have some understanding of what a drug molecule looks like, and then, present it with a specific task and ask it to invent, essentially, some molecules that look something like what you've shown it, but that are still novel. So that's, I think, an area with great promise, great potential, because we know that chemical space is just vast.
People estimate that 10 to the 60 molecules of drug-like size and composition could exist in theory. And even our largest collections, even virtual collections, are still in the billions or trillions. So there's a vast area of space that's untapped still. So the prospect of having computer algorithms that can invent novel but sensible compounds is very, very exciting. It's been something we've been wanting to do for 30 years or so, I think, really, all of my career. And it looks like we're starting to get to the point now where that is beginning to be a realistic prospect.
Mary Parker:
What other things do you think are going to be important in the coming year?
David Clark:
Yes. Well, perhaps switching away from AI, at least for the moment, there's still a lot of interesting work going on in the broader CADD field. Something that we're having to increasingly deal with is what are known as newer modalities. Historically, computer-aided drug design has really concerned itself largely with small molecules, perhaps the small molecules, like aspirin or other drugs that we're perhaps historically familiar with. But when you take a look at the kind of molecules that the FDA is approving now, those types of what we would historically have considered drugs probably only make up about 50% of the approvals.
And so, there are larger numbers of things like antibodies and other types of therapeutic compound or molecule that are increasingly being important in drug discovery and in therapeutic treatments. Increasingly, RNA, in its various forms, is coming to the fore as a class of drug target. And again, historically, the sort of modeling techniques that we've grown up with have really been directed towards normal proteins, not DNA and RNA. So there's a whole area of development and proof of new tools that will enable us to hopefully address those kinds of challenges when they come our way.
Mary Parker:
So what are some challenges you predict will come in the next year? And how can we maybe overcome them?
David Clark:
Well, I think, coming back to AI, we have this, I think, fundamental challenge still that whatever algorithm or computer methods you're doing, the old rule of garbage in and garbage out still applies. There's no way around that. And I think people are growing increasingly aware that AIs are very dependent on the kind of training set that they learn from. And people are very worried about this in some areas. In particular, things like recruitment, you might get bias, because you've only trained the AI on certain types of candidates. And it's the same in any field. And in drug design, it will be the same. So we need to make sure that our training sets are as large as possible, as high quality as possible. And of course, that takes a vast amount of effort to assemble and curate those kind of data sets. But hopefully, over time, that careful effort will pay off.
Mary Parker:
This might be a personal rant, but in my opinion, a job category that is perhaps being overlooked and underused by people building these AI databases is librarians. They know all about how to curate data in ways that are going to be readable and unbiased and good for the computer to be able to process. Digital, there are whole master's degrees in digital librarianship or whatever they call it. So get out there and hire some librarians. That's my advice.
David Clark:
Well, that's right. You need those kind of people who have that attention to detail really, I think, when you're doing these kind of tasks, because it's not necessarily the most glamorous of activity. But it's fundamental to success in this area.
Mary Parker:
Absolutely, absolutely. Well, thank you so much for talking with me, David, and I hope that all of these predictions come true in this year or two.
David Clark:
It's been my pleasure. Thank you very much, Mary.
Mary Parker:
Liz Nunamaker is Charles River's Director of Animal Welfare, and also the outgoing President of the 3Rs Collaborative. She is tuned in to what will be important in the research animal field in the coming year. Welcome, Liz.
Liz Nunamaker:
Thank you.
Mary Parker:
So glad to have you. So just as a quick intro, can you tell us about the 3Rs and the 3Rs Collaborative?
Liz Nunamaker:
Yeah, so the 3Rs are refine, reduce, and replace. And very briefly, in case you haven't heard of these before, refine means that we are critically looking at our methods that we use for working with animals, and we're figuring out ways to minimize the impact on them. Usually, people think of this as providing pain medications to animals, and that is important, but also, looking at potential stressors or distressors for our animals. And this could be as simple as looking at how we house these animals, how we handle them. And so, using things like group housing for social animals, making sure that we use low stress handling methods are all really important refinements. Reduction or reduce are ways of optimizing the number of animals that we use in research. And so, this might be getting more data from fewer animals or just using smart research design strategies, so that we can use the right number of animals to ensure scientific validity.
And then, lastly, replace is finding ways to not use animals at all. These methods are generally revolve around the use of cell culture techniques and computer simulations. So the 3Rs Collaborative is a growing nonprofit organization that's really committed to identifying and promoting practical and impactful 3Rs initiatives. They currently have a few different focuses. So one of these is environmental health monitoring for the ultimate replacement of live animal sentinels with environmental testing, using refined or low stress handling methods with mice, the use of microphysiological systems to ultimately decrease, potentially even replace, the use of animals in primarily drug discovery, but in other areas. Then we have things like translational digital biomarkers, and these will help reduce the number of animals needed, because we're able to gather a lot more information, a lot more data on a given study, but it also improves the lives of the research animals. Because it's minimizing how much we're handling them, because the smart caging techniques allow us to gather this data without even interacting with the animals.
Mary Parker:
So we, as in Charles River, came up with the idea of the 4th R last year, responsibility. So looking ahead, what are some of the pressures on the industry in general to approach animal models responsibly?
Liz Nunamaker:
So I see it as two primary responsibilities. We have a responsibility to the animals that we're using in research, that we are making the best choices possible to care for them, that we minimize the use of animals wherever we can, replacing them with inanimate objects, whether it be cell culture or computer modeling. And we also have a responsibility to the public. The public needs to know that Charles River is truly committed to improving the lives of the research animals and minimizing the number of animals that we're using in research. And these are two areas where Charles River has put a ton of effort into improving the lives of the animals that we are using in research. And this is a really nice application of that 4th R, responsibility.
Mary Parker:
Okay. So you mentioned some of these earlier, but we can get into more detail. What are some of the newest trends in refining animal research?
Liz Nunamaker:
Yeah, so there are a couple that are really important to me, but are also, I think, up-and-coming trends. So low stress handling is something that has been around primarily in the UK for the last 10, 15 years, but it's really starting to catch on in the US and throughout the rest of Europe. And we are seeing a huge increase in uptake of low stress handling, which is really exciting, since mice are the predominant animal used in research. Another big upcoming trend is in the use of micro sampling. And micro sampling is essentially just using really tiny volumes of biologic samples, primarily blood, from research animals.
And this is really important, because it's easier on the animal when you can just use a toe stick or a tail stick and collect a drop of blood, rather than trying to restrain them and get a blood sample. But it also translates very nicely into the clinic, because it's actually easier for people as well, especially when we start talking about in-home drug monitoring, clinical trials, it's much, much easier on the human patient as well. So it's a really nice system that improves the lives of both research animals and human patients.
Mary Parker:
That's wonderful. I love that.
Liz Nunamaker:
And then, some other areas that are up and coming is the use of smart caging and translational digital biomarkers. The cost of this technology is slowly but surely coming down, and the applications are doing nothing but increase. And I think, as we continue to look for smart ways to use this technology, it'll allow us to have really nice refinements for our animals, while gathering even more data that is scientifically relevant.
Mary Parker:
That's fantastic. I imagine this also has a positive impact on the veterinarians and the animal technicians who care for the animals. Obviously, they only got into this sort of work because they do love animals and want to make them as happy as possible. So all these other low stress things that keep the animals happy would also keep the people happy.
Liz Nunamaker:
Oh, absolutely. And it goes a long way to minimizing compassion fatigue in those individuals that are so dedicated to this field.
Mary Parker:
So finally, what will likely be some of the trends in the coming years for replacing animals entirely? That's the goal, obviously.
Liz Nunamaker:
Yeah. One area that I'm really excited about is the use of environmental sampling for health monitoring rodents. This is something that has been slowly but surely gaining momentum, and it's really impressive. Because it allows us to monitor the health status of the billions of mice that are used around the globe in research, replacing them with environmental health monitor, rather than using live animal sentinels for the sole purpose of disease detection. Another important area that's continuing to grow is the use of microphysiological systems or MPS systems. These systems currently exist for every single organ system, but their use has been a little bit hit and miss, partially because there's not been widespread regulatory support for these systems. But as that data continues to grow, there's a number of individuals that are working to collect all of this data, so that they can start putting pressure on the regulatory bodies for data acceptance.
And this will be great, because it'll ultimately lead to a reduction initially in the use of animals, but it could ultimately replace the use of animals in drug development. And going hand in hand with this is the use of organoids, which are essentially tiny self-organized 3D tissue cultures that can be used either alone or in conjunction with MPS systems. And they allow you to look at cells and how they interact together in an organ structure, how they're going to respond to the environment, and how they might respond to drugs. So they are a very powerful ally in the drug development process, that doesn't use live animals. You can do a lot of initial screening in the drug development process with these organoids, rather than using mice and rats that are traditionally used.
Mary Parker:
Yeah, that'd be great. I know a lot of them are used in these early stages, in drugs that might never get past that first stage, which can seem like kind of a waste if you have another method of weeding those drugs out. So I think that's a really important step.
Liz Nunamaker:
And exactly, because it's not an all or none. I think a lot of people think, "Oh, we have to 100% eliminate the use of animals," but these technologies, organoids and microphysiological systems, they're very synergistic with the animal models that we're using. And so, you can answer some questions better either in the vitro system or in the vivo system, but ultimately, it leads to a more scientifically sound set of data, so that we can accelerate the drug development process and make sure that drugs are adequately screened and that they're safe using the fewest number of animals possible.
Mary Parker:
Yeah, exactly. Even if it's not total replacement, it's still definitely reduction.
Liz Nunamaker:
Exactly.
Mary Parker:
That's an important point. Well, thank you so much, Liz. This has been really interesting, and I hope that all these predictions come true in the coming year.
Liz Nunamaker:
Thank you so much for having me. It's great to be here.
Mary Parker:
All right. Bye.
Liz Nunamaker:
Bye.