New Approach Methodologies: Redefining Animal Testing Alternatives

Hi, I’m Jennifer Smith Parker, Director of Insights at Biospace, and you’re listening to the Nature.

In this episode, you’ll be hearing from Stacy Adam, the Vice President of Science Partnerships at the Foundation for the National Institutes of Health, and Patrick Smith, Senior Vice President, Translational Scientists and says at Sotara.

0:20

In this conversation, we’ll dive into how momentum is building behind new approach methodologies or NAMS, that could potentially offer stronger human relevance than traditional animal testing.

The FDA proposed issued a proposed NAMS road map in April.

0:36

The Foundation for the National Institutes of Health has an initiative called the Validation and Qualification Network with dozens of partners from regulators at the FDA and the European Commission to big farmers and CRO’s.

I had a July meeting and Reuters reported in September that AI driven drug discovery picks up as FDA pushes to reduce animal testing.

0:58

We dive into what’s the latest regulatory news and the future for NUM development.

1:14

I’d love to start off and get a background and get an overview of your background, Patrick and Stacy.

1:21

Speaker 2

Sure, I can start.

I work for the Foundation for the National Institutes of Health.

What that means is that I work for a nonprofit associated with the National Institutes of Health, the federal government.

We are 5O1C3 recognized in the state of Maryland, but we were brought into existence by an act of Congress and so we have special authorization to work alongside of NIH to do public private partnerships.

1:45

I specifically oversee a number of portfolios there including cancer, metabolic, cardio, respiratory disorders, clinical innovation and our new approach methodologies work.

So my whole job is to build public private partnerships to get the government and the private sector to work together.

2:01

Speaker 1

Thanks, Stacey.

How about you, Patrick?

2:04

Speaker 3

Hi, so my name is Patrick Smith.

I’m a clinical pharmacologist by training.

Background includes a career in academia in large pharma and have been on the consulting side for the last 10 years or so.

2:19

And I work for a company called Certara and we focus on the application of bio simulation, both software and services to increase the efficiency of and predictability of drug development both in the clinical and preclinical space.

2:35

And so my role at Certara, one of my roles is to help organize how we are applying the bio simulation technology that we have to alternative methods for animal and and clinical testing.

2:52

Understanding New Approach Methodologies and FDA’s Acceptance

Thanks, Patrick.

Now, just to start off, can we have a definition of NAMS so our audience can better understand that acronym?

2:59

Speaker 3

So I would define Nams fairly broadly, which includes both in silico mathematical algorithms, AI, those kinds of things as well as in vitro systems that are all designed to evaluate the toxicology of drugs in outside of outside of animals.

3:25

So it’s, it’s a large, large array of things like organ on a chip systems, other more sophisticated assays, biomarkers.

So it’s it can be defined quite broadly.

3:38

Speaker 1

OK, understood.

And when did we really see the FDA starting to shift toward more acceptance of Nams versus edible testing?

3:48

Speaker 2

Obviously, everybody saw you talked a little bit in your intro about the road map that was announced when Doctor Mccary took office.

However, this is actually been ongoing for quite some time.

We called them new approach methodologies, but in all actuality, they’ve been in development for probably a decade or more, working up towards actually being able to be employed as replacement models for preclinical work.

4:13

What we’re actually seeing now, and you know, we’re at that edge where all of the technology and everything have developed to a point where we can actually start deploying them.

And so This is why we’re gaining so much traction right now both within the regulatory agencies as well as NIH and other scientific organizations around the globe.

4:35

And so I think FDA has really been using these various capacities for quite some time as far as them actually recognizing and qualifying, that’s really come up in the last kind of five to 10 years.

4:50

And really there’s only four new approach methodologies.

There isn’t a fully qualified one right now through the FDA, but there have 10 that are on their shortlist that are currently going through the process.

So it really, as I said, while it’s been building up over time, we’re really seeing it come to culmination now, OK.

5:11

Speaker 1

You mentioned Stacey, the 10 right now the FDA is has on that list.

Do those include the encilical methods that Patrick has noted before or along that scheme?

5:20

Speaker 2

I would have to go back and look at the list to see what’s on there.

If they would include that that they can be there.

I don’t I know that the ones that I am most familiar with are some of the in vitro systems the company emulate I know is probably one of the farthest along with their micro physiologic system through the qualification process.

5:39

But again, any of those types of methodologies that Patrick noted, the insilico, which is the mathematical, I mean up until very recently, FDA had a whole group within their Center for Device and Radiologic Health that was just dedicated to AI and how they were going to handle AI in drug and device development.

6:02

So definitely that is part of it.

I think some of the in vitro systems just might be a little further along in the actual qualification processes.

6:12

Why Big Pharma Will Lead NAM Adoption in Drug Development

Patrick, any words for yourself?

6:13

Speaker 3

Yeah.

So it’s, it’s quite interesting, right?

If you look at the, yeah, we’re all of this started and I think there’s some insights that can actually be gained from the clinical development side where ironically we’ve made a lot more progress than we have on the the animal testing side.

6:32

So if you you go back and look, we’ve actually been able to replace clinical trials.

And a good example of that is what we call the thorough QT study.

So this used to be a mandatory expensive trial for all drugs to look for cardiac induced arrhythmias.

6:50

Today because of the advances in in silicon methods, those trials are generally not done anymore.

So they’re that’s a human safety study that’s waived and has been replaced by mathematical modeling where we we’ve successfully been able to do that.

7:06

And there are other examples of being able to waive drug, drug interaction trials and other sort of safety related studies.

So it’s a bit a bit ironic that we’ve been able to to waive studies in humans and we haven’t made as much progress in animals.

7:24

So I’m very optimistic that we can begin applying those same principles in the in the non clinical side going forward.

7:32

Speaker 1

So in a sense of the companies that are employing these names, I know beforehand, Patrick, we had been, we had a discussion you mentioned about companies are looking into each other kind of saying who’s going to make the first move to be employing this.

So do you think that will be big pharma that makes the first move on this or they’re going to be more conservative than biotechs?

7:53

Speaker 3

So great question.

I think the conventional wisdom, right, is that biotechs are small, they’re agile, they’re they’re risk taking.

And pharma tends to be the opposite of that to me.

And in this matter one, we have pharma who who probably is better positioned to be able to pave the way.

8:14

They’re sitting on loads of data, right and many compounds where they can actually be a part of the validation process and, and work with Stacy and other groups to be able to to learn and validate.

And then they also have a, a treasure chest of compounds, right, where they can afford to take some risks to pave a regulatory path through to the adoption of these methods, even if it if it means running program simultaneously where they’re doing animal studies in parallel with some of these Nam technologies to try and move the ball forward.

8:50

That may be different from a biotech that may be a little bit more risk adverse because if you get a clinical hold because your tox program wasn’t accepted by regulators, that could potentially sink the company.

So I think it’s going to be a combination of of those going forward.

9:09

But but my view is big pharma is probably in a better position to leave the way away.

But once the once there’s a path and the door is open, I would I would foresee biotechs flooding into to go down that pathway.

9:25

How the Validation and Qualification Network Accelerates NAMs

So lots to look forward to then going forward, Stacey, let me ask you about the validation and qualification network.

Can you walk us through how this network works and what are the next steps going forward?

9:37

Speaker 2

Sure.

So the Validation Qualification Network or the VQN is actually part of a larger effort that launched late last year from the NIH called the Complementary program or the complement Animal Research and Experimentation.

9:52

There are three pillars to that program.

The NIH will be overseeing the technical development centers and the Data hub and coordinating Center.

And then we will be helping to facilitate and oversee the Validation Qualification Network.

And you can look at this as a spectrum of the development of Nams.

10:10

The technical development centers are really meant to focus on developing new Nams, especially in areas of unmet need, really to be centers of excellence to develop and, and get through the early validation of the work of these, the data hub and courting center, you can sort of see that as the center of all of the spokes.

10:28

Data will come in from the technical development centers, data will come in from the VQN.

We’ll also be recruiting data in from the community.

And that’ll be critically important because as Patrick said, you know, while companies and others have started to build up databases of clinical data, we don’t have a lot of databases and a few others have some, but not a lot of databases around preclinical data.

10:48

And where those databases, you can also merge the clinical data.

Those methodologies that Patrick was talking about, like in silico can actually work and train, train the models as part of that database as well as the coordinating center.

There’s also going to be a searchable database for Nams, what level of development they’re at, who has them, like, you know, how would you get access to them to use and that sort of thing.

11:12

And then the late stage is the validation qualification network.

So as those methodologies work their way through, as the technical development centers develop them or other people in the community, we won’t just be working with the tech dev centers.

And you know, people are have been able to kind of work and validate, you know, either the in silicone methods through data or the in vitro methods for experimentation.

11:32

We can then work to help them standardize their outputs, standardize the data that they’re collecting, standardize the reporting and really take it through the final stages of validation through qualification with the regulators and get those accepted by the regulators sort of universally for use in preclinical packages.

11:52

And so it’s a, it’s a really big picture overall.

It’s also meant to complement other efforts.

Sorry, no pun intended.

Other efforts at the NIH, they just announced a very large multimillion dollar organoid system that they’re going to be setting up along with Frederick National Lab.

And you know, they announced at the beginning of the year their office to basically work towards animal reduction overall in experimentation.

12:14

And so all of this comes together, you know, working with the other U.S. government agencies, FDA is very much at the table for the BQN.

So the validation Qualification Network, as you mentioned in your intro, we have 15 U.S. government agencies.

We’ve got probably just a little shy of 10 international governments and agencies working with us.

12:34

And then just a little over 40 private sector partners, including nonprofits, small methods developers, large pharma, chemical companies sitting at the table with us, helping to work through the design of what this qualification network should be doing.

What should it look like long term?

How do we make ourselves sustainable?

12:50

And so, you know, we’re really happy to have that.

And one of those key quintessential government partners to us is the FDA, as well as our other regulatory colleagues in the EPA.

And so, you know, we’re, we’re just very happy to sort of sit at the Nexus and try and sort out what all of the different players need in order to move this forward and really start to change the face of how we get new approach methodologies through qualification faster.

13:16

Federal Groundwork and the VQN’s Long-Term Implementation Plan

Gown That’s a great point.

Patrick, do you have a comment to make?

You’re nodding your head.

13:21

Speaker 3

No, I was just agreeing with Stacy.

Incredibly important effort that will be critical for for this to be successful.

13:29

Speaker 1

Stacy, I know when we talked beforehand you mentioned about the before this robot position, the the IKVAM support or the IKVAM really had a nice template for that.

Can you just tell the audience a little bit more about that and how that essentially was was kind of a nice baseline for the FDA?

13:44

Speaker 2

Yeah.

So IKVAM is essentially the cross coordinating agency for the federal government on new approach methodology.

So it’s interagency coordination, validation of approach methodologies I think and this committee has actually been meeting for quite some time.

14:01

I’d have to go back and look at officially when they were formed, but it’s at least been over the last five plus years and they have been working towards developing a lot of the groundwork and foundation of what became the FDA report of what became the complementary program at NIH.

14:19

They issued a a strong report about kind of what the direction should be for federal work in the new approach methodology space in 2024.

And so a lot of pieces from the road map and other pieces that the federal government have been working on came from that report that was issued.

14:37

Speaker 1

OK, understood.

Now with with all these different agencies and partners working together, it seems to be a quite a massive effort to bring a lot of information.

I said you choose to develop a template or develop standards going forward.

Is there is there a goal for to have essentially a sort of guideline the next 5 to 10 years, something along those lines or any sort of time period that’s given to this?

15:02

Speaker 2

So we are in the design phase for this project right now.

We have been working on this since the July meeting.

So you mentioned the July meeting.

The July meeting was really our kickoff.

That’s where we kind of came, came to the world and said we’re going to do this and we’ve got a bunch of partners behind us to do this.

15:18

We’ve continued to bring on additional partners past that meeting, but that was really sort of the big reveal.

What we are going to be trying to do through about May of 2026 is really just locked down one that overarching infrastructure that I was talking about for the VQN, but also our proof of concept pilot projects.

15:37

And so that’s really, we’re going to, you know, it’s, it’s all great if you can kind of put together these big infrastructures and everything.

But if you don’t actually know how they’re going to work through the process and you don’t know how things are actually going to come to being, it’s really hard to know whether you did what you’ve designed is actually useful.

15:52

So we really like to not only put in place some basic overarching infrastructure for our programs as we go into implementation, but we like to have proof of concept projects that come with it.

And so we are in the process now of finalizing our selection of the 1st 4:00.

So we issued an RFI earlier this year.

16:10

Our first set of responses to the RFI were due on August 31st.

We’ve been going through that review.

We had had very high hopes that we were going to be able to announce by the end of October.

Things that happened with our our federal colleagues slowed that down a bit.

So we are targeting that just shortly after the Thanksgiving holiday, we should be able to be announcing the the first groups that we’ll move forward with were selecting concepts.

16:33

And so we’ve had people submit concepts and we will be working not only with the submitters, but then working to add to those submission teams to build pre competitive projects in this area.

And so by doing this proof of concept projects and I can give an example.

16:48

So let’s if we had, you know, somebody brought forward brain on a chip.

So we talked a little bit about organ on a chip and we felt that, you know, neuro is an area of unmet need.

We could take that submitter with their idea, bring in others who have similar technologies, work with them to get them to work together, not so much to change their technology, but to work and actually say, you know, these are the standard readouts that your technology would need if it’s going to replace whatever, you know, kind of gold standard we currently have for the work.

17:20

Although in many cases, especially in the neuro space, we don’t have a gold standard.

There isn’t something there.

And so work to standardize these, work to standardize the report outs that would go to the to the regulators.

And we’re going to be documenting that whole process because there’s no way, I mean, even with all of these partners at the table, there’s no way the VQN is ever going to be able to validate all the Nams out there.

17:41

So we want to use these to not only push very key areas of priority for our regulatory colleagues and our private sector colleagues forward, but documenting the process so anybody else could come along behind us and do the same thing for their Nam of interest.

17:58

And we hope that that will help to kind of increase the number that are getting to the regulators and increase the speed at which the process can be done.

18:07

Speaker 1

OK.

So lots of work planned ahead, very, very busy going towards the end of the year and let’s look forward to the holiday season.

18:16

Speaker 2

In indeed.

And just as a side note, you did ask for timeline and apologies got lost a little bit there.

In May when the target launches, we will then be launching a first five year implementation.

But we do have the hope of a second five year implementation at the very least.

18:33

But honestly we would like to see this become an Evergreen process for the VQN.

And you know we have a good idea of how something like this might work.

We have the Biomarkers consortium that we have been working on as public private partnerships and that’s been going for 20 years.

Like next February, we’ll be celebrating the 20th anniversary of that.

18:51

And so I would love to see that happen for the VQN.

Like, you know, if we’re all around 20 years from now, come back and tell you that we’ve been just as successful.

18:59

Speaker 1

That’s that’s incredibly impressive.

19:01

The Real Drivers Behind New Approach Methodology Adoption

Now, Patrick, let me ask you it in your perspective when this field, is it really science is pushing a soul forward or is it public pressure that we have behind that as well or a bit of both?

19:12

Speaker 3

So it’s a great question.

I, I think it’s a combination of, of the two, right?

The public pressure has actually been around for a very, very long time, right?

Decades for sure.

But now I, I think the, the legislative mandates have really provided the spark to have the conversation now.

19:29

And I think that the scientific community, the regulators aren’t doing this to be compliant or to respond to public pressure, right?

It’s, it’s about how far the science has come and, and at the time is now to actually make a difference.

19:46

Or we know that 90% of all the drugs that go into human trials fail, yet just about every one of them demonstrated safety and efficacy in animals, right?

Which simply tells us that the approach that we’re using really isn’t working very well.

20:04

Other forecast that was wrong 90% of the time, you should should probably look for, for something else right to, to to guide you.

So the objective here really isn’t just to reduce the use of animals.

While it’s something that we all, we all want to do, the objective I think is to, to actually find something that works better, right?

20:27

And find something that’s more more predictive and address that, that 90% failure rate.

And it’s, it’s a very exciting time because of all of the tremendous advances in biology and these alternative method systems and cell culture and, and all of these things along with the computational that we, we actually have demonstrated that we, we can do this better.

20:52

And, and now is the time, right?

Because I, I like to say that the, you know, ultimately the best way to protect a patient in a, in a phase one clinical trial is to have is that’s been derived based on human biology and, and not Beagle biology.

21:09

Speaker 1

It makes a lot of sense.

21:10

Regulatory Precedent, Standardization, and Collaborative Data Sharing

So at at that point now as we have all this progress rate ammunition behind this, what do you think is a single biggest barrier to name acceptance and what’s also a most promising breakthrough?

21:22

Speaker 3

So to me and, and based on the all of the partners that we, we speak with everyday major, a major issue is that regulatory precedents, right?

Someone’s got to get there 1st and establish that the pathway.

21:38

Otherwise everyone will continue to be cautious.

There are questions about, well, if I go to the FDA, will this be acceptable?

What happens if the FDA says OK, I also want my drug approved in Europe.

So is EMA going to be on board as well?

21:54

And, and, and, and fortunately, you know, they, it turns out they are, but there are always right, always questions and different and interpretations of, of standards and, and so on.

So I, I think that to me, getting, getting those first few cases through will will really provide that regulatory precedents that will give others confidence, right, to follow through because, you know, no one, no one ever gets fired for doing it the same way.

22:24

It’s always been done, right for the last 30 years.

So we need we need some first steps in case studies, I think.

22:32

Speaker 1

That makes sense.

What’s your perspective, Stacey?

22:34

Speaker 2

Yeah, I mean, I think Patrick touched on what I see as the biggest barrier a little bit, and that’s the standardization and the, you know, reproducibility of these.

It’s really hard for the regulators.

They’re used to looking at large amounts of evidence and they don’t like to accept things until there’s a large amount of evidence behind them that they work.

22:52

And it’s often really hard to get to that level of evidentiary standard for acceptance for it.

And you know, that was, it’s true of drugs, it’s true of devices, it’s true of, you know, even animal models back when we when we started using them.

So it’s just going to take a little bit of time.

23:09

But because right now there are so many different new approach methodologies and they’re all slightly different, even if they’re all slight, you know, meant to be addressing the same context of use or the same problem.

That if we can work to actually standardize how they read out what those readouts look like, it’s going to be easier for the regulators to begin to accept those.

23:29

And then, you know, I think the biggest step forward will be just the fact that I think we are at the precipice where you know, data collection, data harmonization, data standardization, it’s no longer a technical problem.

You know, the tech companies is fault have solved for this, but we need to now start amassing that and we need to start curating it to be useful for the regulators as well As for the the developers and things like that to to use for their technology.

23:56

And so I think the idea of collaboration and data sharing and and central repositing of this type of information, I think that is our key step forward or is we are we where we need to be?

24:11

No.

But I think we are on that trajectory and I think I would like to hope that as we saw with COVID under the pandemic, we’re all starting to get in the same boat and we’re all starting to push the oars in the same direction.

And I think that can be our biggest kind of accomplishment for right now.

24:28

Is that like just to really centralized all of that together?

24:33

Speaker 1

Well, thank you so much Stacy and Patrick for this engaging conversation.

And it’s it’s definitely nice to end a note on an uplifting, optimistic viewpoint going forward.

That’s the end of this podcast.

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