E83: NAMs: The Future of Research

As researchers look for ways to reduce the need for animals in their studies, new alternative methods (NAMs) are an intriguing route to capture crucial safety data and help advance drug discovery and development - specifically, virtual control groups (VCGs).

Join Steve Bulera, CVP and Chief Scientific Officer for Safety Assessment and Toxicology at Charles River, as he discusses how VCGs can be a viable alternative to research models, what it would take for the industry to widely accept NAMs, and how they can revolutionize research for the future.

Show Notes

• Virtual Control Groups
• E73: Virtual Control Groups
• Modernizing Drug Development with Virtual Control Groups
• Virtual Control Groups: A Data-Driven Novel Method
• Revolutionizing the Future of Safety Studies
• Charles River Laboratories Announces Development of Nonclinical Virtual Control Groups with Sanofi to Reduce the Use of Animals in Research
• Alternative Methods Advancement Project

• Podcast Transcript

  Steve Bulera:
  It's about animal welfare and reducing animal usage. That's really the big goal there is rather than using say, 10 males and 10 female control rats on a study, you may reduce it to five and five. You may, in larger species that we work with, it's maybe cutting them in half. So, we could reduce at least. And since those control groups are 25% of the animals on a study, typically you could reduce the animals on a study by about probably somewhere between 10 and 20%.

  Mary Parker:
  I'm Mary Parker, and welcome to this episode of Sounds of Science. Virtual control groups or VCGs are an established concept in clinical trials, but the idea of replacing concurrent live animals with virtual data sets has not been introduced into the design of regulatory nonclinical studies. With more researchers and regulatory bodies expressing interest in reducing the number of animals needed to get a drug to market, it is time to discuss where the industry stands on VCGs and other new alternative methods. Here to lend his expertise is Steve Bulera, CVP and Chief Scientific Officer for safety Assessment and Toxicology at Charles River. Welcome, Steve.

  Steve Bulera (00:55):
  Thanks. Thanks for having me.

  Mary Parker (00:56):
  Well, thank you for being here. We're honored to have you. So, can you tell me a little bit about your role at Charles River and how you got to this point in your career?

  Steve Bulera (01:05):
  I've been with Charles River now for 18 years. First, I started off as the head of toxicology when Shrewsbury facility was first opened. Then I was asked to move to Reno to take over the head of toxicology there and then moved into this role. And prior to even Charles River, I was in pharma for 10 years at a number of major pharmaceutical companies. Kind of done everything in toxicology that you can think of. Everything from running studies to investigational, toxicology to what we used to call the Panomic technologies. Kind of done it all.

  Mary Parker (02:55):
  So, in what ways are VCGs a promising tool for reducing animal use in research?

  Steve Bulera (03:03):
  Well, as we all know, there's increasing pressure on reducing the use of animals in research. It is going to take a long time, but we do need to take steps to do this. And VCGs is one way to start doing this on a study- by-study program-by-program basis. And it's not only for pharmaceuticals, but also for using VCGs in the chemical industry and the agrochemical industry and all the other industries we test the safety of compounds for.

  Mary Parker (03:31):
  And just so that everybody who's listening, in case they've never heard this term before, basically a virtual controlled group does the same thing that a normal physical biologic control group would do. It's just based on decades of data, correct?

  Steve Bulera (03:45):
  Yes. In the simplest terms, it's basically taking all the data from control animals that we've collected over the years and putting them into a huge database. And then in simplest terms, picking animals that match the criteria of the study and using them in place of live animals basic, creating a virtual animal.

  Mary Parker (04:08):
  Like the same food, same bedding, same everything that they would've used in virtual

  Steve Bulera (04:12):
  Control group growers, same vehicles, that they receive as much as close as we can. We align it to the study that we're running in real time.

  Mary Parker (04:20):
  Makes perfect sense. So how do VCGs enhance drug development in nonclinical studies?

  Steve Bulera (04:26):
  It's about animal welfare and reducing animal usage. That's really the big goal there is rather than using say, 10 males and 10 female control rats on a study, you may reduce it to five and five. You may, in larger species that we work with, it's maybe cutting them in half. So, we could reduce at least. And since those control groups are 25% of the animals on a study, typically you could reduce the animals on a study by about probably somewhere between 10 and 20%.

  Mary Parker (04:58):
  I mean, that's a pretty great gain and definitely feeds into the 3Rs; Reduction, Replacement, and Refinement.

  Steve Bulera (05:02):
  Yes.

  Mary Parker (05:03):
  So without getting into the weeds of the difference between artificial intelligence and machine learning, can you explain how those are used to help make VCGs possible?

  Steve Bulera (05:14):
  So VCG really isn't artificial intelligence or machine learning. To me, machine learning, artificial intelligence is the computer system learns about what they're doing learns from it, makes changes, tries, another attempt learns from it until they get it right. This isn't really machine learning. This is using advanced algorithms to go into a database of animals and saying, Hey, which one of these matches my study parameters? So, there's not really learnings from it. They're not going to learn that the next time I'm going to pick a better animal. It's more about always picking the right animals. So it is a bit different than machine learning and ai.

  Mary Parker (05:52):
  It's not learning, it's not intuiting as maybe some robots are thought to do. It's more just, it's really good at analyzing big data sets and pulling up the information that you.

  Steve Bulera (06:06):
  Need. That's what it is. It's Laura Lotfi who has really spearheaded this for us, has created algorithms and selection criteria and along with her team tested which ones were important to be able to pick the best animals for that specific study that we're going to use the virtual control on.

  Mary Parker (06:23):
  So, what would the implementation of VCGs include? What would it look like?

  Steve Bulera (06:53):
  So, what we've done is taken a study from that we've previously run before that are done archived. There's no impact on those studies, is we've taken a virtual controlled data set of animals, used our selection criteria to match those, I'll say old studies, and then reanalyze those studies. So, we had one of our study directors do this and went back and as I said, retrospectively analyzed 20 old studies and showed that there was no impact on the outcomes and conclusions of the study. So, we have clients who are interested in doing that now. So, we have a number of major pharmaceutical companies that are working and partnering with us in addition to Sanofi. We have other clients who, like Sanofi, who are going to what I call the next step. The next step is not taking an old study but taking a study. They're running today and doing the same and re-analyzing the study with a set of virtual control animals that match their study.

  (07:56):
  So the study director who's running their study as a separate activity will reanalyze the study using virtual controls. This, so people might get concerned that this has an impact on the study. This is completely an academic exercise done outside of the main objectives and main direction of the study. But they're going to do this as an added academic exercise. That's where we're going eventually. Where we see virtual controls going is we probably for many years will not replace the entire group of animals. So for example, if you have 10 male control animals and 10 female control animals, what you're likely going to do is we're probably going to go to five live animals and five virtual animals. And this is for a variety of reasons. One, the data in our database is probably good for about five years. We need a way to replenish that data.

  (08:55):
  So if after five years we've replaced all the animals, our database will be --not valid anymore. So, by having at least half the animals be live animals, you've reduced by half. But the other half are allowed to repopulate the database to keep it fresh. And that accounts for our processes or procedures or animal handling, the food, the environment, all those things that could have an influence on the outcome of the data in a study also, it doesn't happen a lot, but there's always a concern about genetic drift by continually repopulating the database with animals we can control for that as well. So those are two reasons. The last reason is our clients are doing really outstanding science and they will always have a biomarker that we probably don't have in a database. So, we can match the study almost completely separate, let's say one biomarker. And we'd be lacking control animals for that biomarker by having at least half the animals be live animals. We can use their blood, their serum, their plasma at least from five animals rather than 10 to be used in the statistical and the data analysis.

  (10:20):
  We think this is the way that it will go in the future. It's much safer, it reduces risk, and our clients are very, they really understand that it reduces the risk, and it gives them the opportunity to have some live animals to control for, I'll say environmental factors. And they understand the reasons for genetic drift and for repopulating the database. So, it seems to be the least risky to our clients for them to accept.

  Mary Parker (10:42):
  And of course, the more of these studies that are done, the more times it can show over and over again that it works without sacrificing an ounce of safety, the more trusted it'll be in the future. Yeah.

  Steve Bulera (10:55):
  Yes. It'll also give regulators a, so they'll be able to still look at the live animal data as well as the virtual control data. And we are hoping that our clients who are doing that, what we call that prospective analysis, where they're running the virtual control in parallel, we hope that our clients will submit that along with it again as an academic exercise and to show that, look, this does work. It does have a future. So that's what we're working with our clients, that they will submit this to the regulatory agencies and as the regulatory agencies get more and more comfortable with it, when we go to what we call that hybrid model, let's say five live animals, five virtual animals, they'll be much more comfortable seeing it in the future.

  Mary Parker (11:35):
  So, you mentioned our collaboration with Sanofi. So, we're exploring the use of VCGs in nonclinical toxicology. Can you tell us a little more about this initiative?

  Steve Bulera (11:45):
  Yeah, I mean, it's kind of what I was talking about. Sanofi is probably the one who's the most vocal about it, where we have already picked some studies where we will do that parallel analysis with them and then hopefully, they will submit it with their regulatory package. They'll submit it along with that dataset.

  Mary Parker (12:03):
  So, all this work we're doing for VCGs, might it help industry adoption of other new alternative methods?

  Steve Bulera (12:12):
  I think it'll open their eyes to see, show things that work. So, here's a success. It's all about getting the regulators comfortable. I've been to many seminars that talk about this. One of the biggest hurdles is familiarity. The regulations are currently written for animal testing. We're hoping that those regulations get, I'll say if I don't want to use the word loosened but modified enough that they're open to the use of these alternate methods. We jokingly talk about is if you're brought into the public forum and have to defend why you put this drug on the market and it was all alternate technologies or things of that nature, right now we get the feeling from the agencies is that to be safe rather than, sorry, it's the fallback. I followed the regs. The regs, they use animals. So, we need to break that mindset and start showing them data that this is a good way to reduce the amount of animals and it doesn't compromise the evaluation of the safety of a drug, a chemical or whatever entity we're testing.

  Mary Parker (13:17):
  Efficiency, safety, efficacy, and accessibility are critical to the drug development journey. So how can VCGs and other new alternative methods be expedited and accepted within the industry and among regulatory bodies?

  Steve Bulera (13:51):
  I think it's getting back to what I was talking about earlier is that familiarity. We have to show some successes. We have to show some wins. We have to convince the public; we have to convince we regulators that we are not compromising the evaluation of the safety of the test article by using something else than an animal model.

  (14:11):
  And that's just going to take time. People willing to submit in toxicology, people don't like to give the FDA or regulatory agency something that they don't know the answer for already. So, if you provide a NAM that let's just say it just doesn't work well, they're probably not going to submit it because they don't want to create a false sense of or give the FDA string to pull on to see what that data means. Maybe it does mean something, maybe it doesn't. So, they're only going to submit success stories. But we have to be able to talk about the failures, talk about what doesn't work. It's getting everybody comfortable to understand the limits and the boundaries of what these new alternates can do.

  Mary Parker (14:56):
  That's a really good point. And what you mentioned earlier about them not having these new regulations for these non-animal-based systems, it makes sense because they just, like in the last year passed the FDA modernization act. So they say, okay, you don't have to use animals anymore. That is no longer required, but they didn't change everything else downstream yet. So, from your perspective, what's that going to look like? How long is it going to take for the effects of the modernization act to work its way down through these different levels?

  Steve Bulera (15:32):
  So, they didn't say that you don't have to use the animals. They said they were open to alternate methods, which means, again, you have to be able to show that if you're going to replace an animal study with, I'll say an alternative model, be it in vitro organ on-a-chip, spheroids, any of those new things that are coming out, you have to show they are good at predicting what you want it to do.

  (16:02):
  For instance, how do you predict a convulsion in a Petri dish? It's kind of hard to do that. There are companies that are working on looking at biomarkers and things, but again, you have to have that correlation from the animal model, from the animal model to the dish to the human to the, you have to link it all together to show that what's happening in this alternative model is what's going to be in the human. And right now, the agency is very open to, and they don't really have an opinion on if you want to prioritize a compound before going into animal testing. They don't have any regulations, anything. It's like if you want to use this assay to test whether this compound is good enough to develop, that's on you. But it's that kind of data that'll be helpful for later on. And we have to start now. This is going to take decades. People think that just because they wrote it into the FDA or the modernization Act that it happened tomorrow, it's not going to happen. I mean, we are battling 50 to 75 years of animal toxicology data. It'll take a long time and we hope to expedite it by getting people more familiar with it and things of that nature.

  Steve Bulera (17:31):
  What I think a lot of people are concerned about is if you expedite things too fast and the safety of the patient, the population is compromised. I don't think anybody wants to do that.

  Mary Parker (17:45):
  I mean, I think historically the FDA and European regulatory bodies have been extremely conservative on things like this, which is for the best. I mean, their most important thing in their mind is patient safety and public health, if it's a chemical for crops or whatever. So, it does take a long time, but I think that should be reassuring to people that when these things happen, I do think they will happen. A lot of work has gone into them, and a lot of safety data is there already.

  Steve Bulera (18:16):
  Yeah, I mean we go back, and we look, have these discussions in the toxicology circles. If you go back to the days of thalidomide, we never used to do reproductive toxicology testing before that. And we had those incidents where children were born with limb deficiencies, and that was one of the impetuses for creating a program looking at reproductive toxicology, we're going to have to do the same thing with models. It's come up with models that are as good at predicting human outcomes and protecting, again, patients in the population.

  Mary Parker (18:55):
  As a contract research organization like Charles River, our focus is on the clients and getting their work to where they want it to be. But what are ways that we can help evolve the next generation of drug development technology and help with the acceptance of BCGs and NAMS and all of these other new technologies?

  Steve Bulera (19:21):
  So, we're working with clients on a number of fronts, either directly with talking to them. VCG is a perfect example. We're working with them directly and trying to get them to work with us, analyze their data in parallel, and then convincing them to submit it to the agency. So direct interactions. We're also involved in industry consortiums. There is one on VCG, which just started last week as a matter of fact. And we're starting to work with them. And we already had one client asking us to be able to release our data to them so they could submit it to the I, they call it the IHI. So that's a couple of ways we can help work with clients is participate and also offer to be leaders. We work with almost everybody in the industry, so we are a great focal point.

  (20:14):
  It's kind of like my role. I get to see across everything. We also get to see across all the companies out there, what their concerns are. They all have the similar concerns and asking questions and also bringing them together. So, we have something, we did it last November, it was called the Drug Development Leadership Forum, where we had the heads of toxicology from all the major pharmaceutical companies in one room, which is only the second time they've ever done this with anybody. Second time we did it with them the first time and the second time. And we talked a lot about NAMs about how we can help them, and they can help us drive these things forward. And again, VCG is a perfect example of that.

  Mary Parker (20:57):
  Are there any other NAMs that you'd like to mention that are kind of on the same path as BCGs where there's so close to being relatively close to being adopted?

  Steve Bulera (21:07):
  So, there is one that we are working on in toxicology. One of the required genetic toxicology tests for submitting a drug eventually to for an NDA for a new drug application is something called the in vivo micronucleus. So, our gene tox group in Senneville and Skokie are working on two approaches, one using spheroids, one using human hepatocytes on a chip to see if we can come up with an assay that could replace that in vivo assay. And we've made great progress. We presented posters, had publications. The group, the leader in Senneville, Annie Hamel, is getting a lot of requests to come talk to companies, to societies about that assay because it does show promise. And there is again, an industry consortium that we're part of to help harmonize standardize it to see if we can start driving this forward.

  Mary Parker (22:07):
  That would be amazing. I mean, speaking of driving it forward, so investments are being made in new alternative methods to improve the development and design of clinical and nonclinical trials. So we want to reduce our reliance on animal testing. How will VCGs advance drug discovery and development?

  Steve Bulera (22:34):
  I think advance is the wrong word. How do you make it going? How do you not interrupt? How do you improve the drug development process? I think things like new approach, methodologies, VCG, things of that nature will reduce costs, will decrease timelines. And that's how, if you define advance that way, that's what it'll do. It'll make it easier, faster as well as cheaper. And we know such a focus on drug development and drug development costs and drug costs that any way we can reduce any of that, that'll be a benefit for everybody.

  Mary Parker (23:25):
  And what disease areas can this approach impact? Basically, all of them.

  Steve Bulera (23:29):
  So, we talk a lot about safety testing. So right now, we talk about replacing it on a toxicology study of VCG. We're already having conversations with our discovery colleagues. Can we use virtual controls in when our discovery colleagues are running and discovery? So rather than constantly running controls over and over, can they use a VCG or something of that nature and reduce animal usage in their studies as well. And if discovery can adopt this as well and we think they can, it'll impact every disease area because rather than using control animals, they can use virtual animals in their studies as well.

  Mary Parker (24:09):
  That would be pretty great. So, in your opinion, what does the future hold for VCGs and other alternative methods?

  Steve Bulera (24:17):
  I think it's bright. I mean, we have to do this. The technologies are advancing. We're looking for things. It's just going to take time. And I think people, we've had people call us when the FDA modernization came out and it's like, you're going to stop using animals tomorrow. It's like, no. Again, as I said earlier, this is going to take a long time, but we must start, we must put effort. We must put resources behind it to start doing it. And as I think we are, I think we're doing a good job internally within Charles River, but also externally being knowledge leaders, leaders in the industry, joining consortium, having a seat at the table. I think we're doing a really great job at doing that. And I think the future is bright. It's just going to take time.

  (25:23):
  Some of us more experienced people in Charles River may not be here to see all of this implemented in the future. So, we need more people getting involved in this volunteering, committing time, which may be on top of their daily workload, but to get involved in these things because they're the ones who are going to carry this forward. I may be retired, a lot of us may be retired. We need, again, the people who haven't been here long, who have a real interest in this kind of technology, this kind of advancement, these kinds of projects to get involved.

  Mary Parker (25:59):
  When you were at the beginning of your career and you're looking at what's going on now, is there anything that your younger self would've been very surprised by that you would think is just really cool and almost sounds like science fiction?

  Steve Bulera (26:15):
  Yeah, there's a lot of things. We've had a lot of conversations on next generation sequencing,

  (26:22):
  Which is an instrument now that takes a sample and can sequence A-D-N-A-R-N-A strand very quickly. Very, and I was talking to somebody just the other day, remembering the days where we used to pour gels that were two feet by three feet, and they were these giant gels that we had to pour using acrylamide. And it was more art than science to get these gels to know bubbles and things. And I'm like, oh my goodness. Had I had a machine, I could just squirt the sample in and get a sequence out in a short matter of time versus these other things. Because with those gels you used have to take, they were radioactive. You'd put a piece of film on it, you'd throw it in the freezer for a month, and then you'd pull it out and then you'd look across, okay, A TCG. And you'd go through the whole thing all the way down to get your sequence. And sometimes the lines weren't clear. And so it was kind of a guesstimate versus today's technology, it's just like, wow, this is so different than what we had back then.

  Mary Parker (27:17):
  Yeah, definitely that. That's a good example.

  Steve Bulera (27:20):
  It was just, we were just laughing about, wow, remember the days of dinosaurs.

  Mary Parker (27:25):
  Well, thank you so much, Steve, for being a part of Sound of Science. It's been really great having you here.

  Steve Bulera (27:30):
  My pleasure. I enjoyed it completely.

  Mary Parker:
  Steve Bulera is CVP and Chief Scientific Officer for Safety Assessment and Toxicology at Charles River. Stay tuned for the next episode of Sounds of Science. Until then, you can subscribe to Sounds of Science on Apple Podcasts, Spotify, Stitcher, or wherever you get your podcasts. Thanks for listening.