The a16z Show - Food, Drugs, and Tech—100 Years of Public Health

Episode Date: January 14, 2020

The federal agency known as the FDA, or the Food and Drug Administration, was born over 100 years ago—at the turn of the industrial revolution, in a time of enormous upheaval and change, and rapidly emerging technology. The same could be said to be just as true today. From CRISPR to synthetic biology to using artificial intelligence in medicine, our healthcare system is undergoing massive amounts of innovation and change. Covering everything from gene-editing your dog to tracking the next foodborne outbreak, this wide-ranging conversation between Principal Commissioner of the FDA Amy Abernethy and Vijay Pande, GP on the Bio Fund at a16z, discusses how the agency is evolving to keep pace with the scientific breakthroughs coming, while staying true to its core mission of assessing safety and effectiveness for consumers in the world of food and medicine. Highlights:What the FDA looks like today and the key steps of the FDA process to getting a drug/product to market [2:20] How to manage a culture when mitigating risk is a top priority while aiming to innovate for the future [5:22] Creative problem-solving in times of crisis, such as the Opioid crisis [9:58] Preparing for and preventing drug shortages at scale [13:30] How advances in bioengineering are transforming healthcare [16:00] How the FDA is thinking about n=1 therapies and its applications in the future [18:54] The future of healthcare privacy [26:10] The ways the clinical trial process are shifting [29:26] Innovations in Bioengineering as they relate to regulating food in the future [36:02] How the FDA handles foodborne illnesses and its plans to innovate food safety [39:12] Discussion about the next 100 years of the FDA [41:25] Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hi and welcome to the A16C podcast. I'm Hannah. The federal agency known as the FDA, or the Food and Drug Administration, was born over 100 years ago at the turn of the Industrial Revolution in a time of enormous upheaval and change and rapidly emerging technology. All of those things could be said to be just as true today. From CRISPR to synthetic biology to using AI and medicine, our healthcare system is undergoing massive amounts of innovation and change. This wide-ranging conversation between principal commissioner of the FDA, Amy Abernathy, and Vijay Ponday general partner at A16Z, took place at A16Z's annual summit in 2019 and covers everything from gene editing your dog to tracking the next food-borne outbreak, how advances in bioengineering are transforming health care, clinical trials,

and drug development, and how the federal agency is evolving to keep pace with the scientific breakthroughs coming while staying true to its core mission of assessing safety and effectiveness for consumers in the world of food and medicine. So thank you so much for joining us. Terrific to be here. Hello. So, you know, in thinking about how to start this, I was thinking about the origins of the FDA. So the FDA started in 1906, 113 years ago.

And it's an interesting thing about that time because, you know, it's turn of the previous century, a time of a lot of tumult, innovation, technical change, an industrial revolution, you know, things that actually really were the driving forces to create the FDA. And like, here we are another turn of the century, another industrial revolution, another amount of tumultuous change. You know, what are the needs of the FDA right now?

And, you know, is its core mission really still relevant? Is the FDA still relevant? Yeah. So I'm going to go back that 113 years in the time the FDA was formed and continues to be the largest consumer protection agency, was formed out of 100 laws. I think that the issue that was going on at the time was unhygienic conditions in the Chicago stockyard. And you can imagine there's been a lot of responsibilities of the FDA over time, thalidomide, etc. But practically speaking, the FDA is responsible as a science-based agency to protect and promote public health,

including through making sure that we have safe and effective medical products to use every day with our patients, as well as through promoting innovation. You know, your question was whether or not the FDA is still relevant. And I would argue that in a time of rapidly emerging biology, when we've got more and more scientific innovations and potential products coming to bear, the need to make sure that we have an objective way of assessing safety and effectiveness and providing consumer confidence that this treatment is appropriate for me is a responsibility of the FDA that actually has more responsibility, not less.

Well, so, you know, in that context, let's talk about what the FDA looks like today. In those older days, you know, the data came to the FDA by the truckload. You know, I'm just imagining, like, reams and reams and paper and so on. And, you know, I'm just curious to get your take for, you know, what does the current system look like? And, you know, could you take us through, you know, how this works? So, you know, a couple of things. I actually think that there was a time when it probably came on horse and buggy, not just trucks. Yes, yes.

So practically speaking, I usually think about five key elements in. drug and biologic product development. So there's the discovery phase, then there's time of pre-clinical development. After you've done adequate pre-clinical development in line with good laboratory practice, then you'd submit an investigational new drug application for a drug or a biologic to the FDA, which gives permission then to start clinical studies with people. And a drug or biologic will go through a series of clinical studies, typically phase one through through three, although these days those lines are blurring for exactly what drugs, followed then by a new drug application or biologics application of BLA to the FDA.

And then the final stage after FDA marketing approval would be post-marketing assessment and continuous surveillance about this particular medical product. So that's kind of the usual steps. Given that, you know, what would you want to modernize about it? You know, how do you take us into the next 100 years? Yeah, so, you know, I think that goes back to the horse and buggy in the trucks and how information has historically gotten to the FDA. So I came from the landscape of a health tech startup company focused on data. I was recruited to FDA with the expectation that I was going to show up and focus specifically on digital innovation for the FDA

and sort of went smashing into a realization that, in fact, the way the majority of our applications still, come in is through PDFs or sort of essentially large digital representations of what used to come in on trucks. We now receive most of our drug applications, but not all, in some kind of electronic format. As a matter of fact, just two weeks ago I had to approve a book of work in orphan diseases where we're still getting things on paper as one example. But most things come in as a digital application, also with some digital data but that gets stored at FDA. And as I think about where we're going in the future,

practically speaking, in order to become more efficient FDA, we're going to need to receive more and more of those applications in digital formats that start to represent structured data, structured data that we can review at scale and also structured data that allows us to now continuously surveil medical products in a better way in the future. And so what I realized was that,

If I was going to be a person focused on data at FDA, I was going to need to also think about how do we modernize the FDA's underlying infrastructure to take that forward, and that's why I took on the CIO job. Yeah, in addition to the infrastructure, it's interesting to think about the culture. And because, for example, if there's a great drug that nobody ever gets, you know, nobody ever knows about it, let's say there was a cure to cancer, but the FDA didn't approve it, there's no outcry because no one ever knew about it. But on the other hand, if the FDA let something through that actually, you know, has harmful effects, you know, thalidomide and other classic examples, then, you know, then there's a huge backlash.

You know, how do you build and how do you innovate in a culture that has to deal with such strong asymmetries? So it's interesting. As you describe those asyncetries, what you all heard describing is the practical reality that that can make you very risk adverse, right? Yes, exactly. Particularly worried that I'm going to do something wrong and I'm going to flub up and that's actually going to. have very public impact, and we see that all the time. I think that in order to develop solutions for regulatory innovation, then what you really have to do is come up with flexible mechanisms that also allow us to deal with the risks, but also take some risks when appropriate. And so that means that as FDA, one of the things we focus on is risk-based scientific

decision-making and trying to right-size the degree of review and expectation with the potential risk of this particular product. And what do I mean by risk? Sometimes those are safety risks, right? So the risk of, for example, hepatic failure or the risk this drug might take a person's life. Sometimes the risks of the size of the population impacted. So you're trying to balance the urgency of this particular problem sitting in front of

you with the number of people where this product may impact. Also, there's risks of public perception and expectation. And then the last sort of set of risks is where can you de-risk it? Exactly. So you can de-risk it by trying to make sure that, for example, preconditions are met as it relates to the manufacturing process. You can de-risk it by understanding in a consistent way toxicity. You can de-risk it by having consistent expectations around clinical effectiveness. You know, so I think what's interesting is to think about that.

in even the other context of what the FDA does, I think people often don't realize that the FDA isn't just about, let's say, approving drugs. We think about clinical trials. There's a lot of things that you do to protect American consumers. And when we're talking about it, it's almost like,

I feel like you could have a show that's like CSI, FDA or something like that, where you have these investigations of the crises. So like, you know, like sometimes it's slow-moving crises, like the opioid epidemic. How do you, like for a crisis like that where it slowly sneaks up on us and then it's too late. How does the FDA even think about that?

Practically speaking, the FDA is responsible for many types of medical products. And any one of those can have a crisis I've discovered. So we have food and drugs. We have biologics and devices. We have animal food and drugs. We have cosmetics. We have nicotine-based products, vapes. And so the distribution of potential crises are real.

And as you think about something like the opioid crisis, a problem that snuck up on us in many different ways. And as I step back, and I came to the FDA in March, so I've had sort of the opportunity to watch this as an insider outsider during this period of time. You know, as information starts to accumulate that says we've got a really big problem here, that information comes from the public. It comes from across different places in government. And now we need to step back as a nation, but also as an agency, and say, what do we formally do? And so as an agency, we put in place an action plan that had several parts that focus on what we're responsible for. And I'm going to come back to that key point in just a second. But then also asked, how does that interdigitate with all the other plans that are going on across government

and also across the health care setting to try and solve for. So I'll say the last piece that I found very interesting since I've been in government is that there are very clear rules of the road of our authorities. It took me a while to get used to that word, but the word would be authorities. Authorities, yes, yes. Yes, so our area is of responsibility and sort of legitimate jurisdiction. And practically speaking, with respect to the opioid crisis, we need to go back and say, where is FDA do we have authority to try and help resolve this? problem. So practically speaking, we can help reduce the number of opioid tablets, for example, a patient has access to after back surgery or knee surgery in order to reduce the chance that

this particular person has access and becomes addicted in the first place. As a second example, we can increase methods for access for, for example, melanchone-based treatments in the field. And we've done a number of projects to try and make sure that there is patient-informed labeling, and other aspects in the field. And then also, we can start to think about developing and helping to develop new treatments for the treatment of pain, as well as new treatments for the treatment of addiction

and start to solve a problem on that side. So as FDA, we have to stick in our swim lanes, but then we have to think about how that grooves with everything else across government so that there's more of a nationwide approach. Yeah, in that sense of handling the authority, nature of things, like, you've got to make some tough decisions, Like even things like contaminated food going across the border, like you've got to inspect trucks.

How do you know which truck to look for? Yeah, so, you know, as I got to the agency, I was surprised to find not only are responsible for regulating about, you know, 20% of international GDP. As I mentioned, that sort of is cross-broad number of products. And the weed that we regulate is different by product. So about 15% of our food is imported. we need to be able to make sure that the food is appropriately safe. It's appropriately labeled that it's legitimate for sale in this country. And so that means that if you're sitting at the border of Mexico,

we need to basically investigate trucks that are coming across the border and look for violative products that aren't appropriate for sale in the United States, either for safety concerns or commercial concerns. How do you know which truck to look at? And if we don't get that right, we can basically stop traffic. for miles. So we have, in the case of inspecting trucks, we have something called the PREDICT program. And the PREDICT program is a 10-year-old rules engine that's been written by some of the different centers across FDA where the rules start to predict which truck

is most likely to have unsafe food. Now, you can imagine that if we wrote those rules 10 years ago, they might be old rules. And it's true that we update the rules every year, but we do so by hand. And as I think about trying to develop a more modern agency, can't we update the way that we modernize those rules? And so right now we've got an experiment going on where we're looking at machine learning-based prediction of which trucks we should expect on the border. And can we now use machine learning as a way to be smarter? Importantly, though, going back to your point about we can't get it wrong because we lose consumer and confidence when we get it wrong, we can't just say machine learning is going to be great, let's go. We actually have to thoughtfully do the experiments

to say if we apply a new approach over the old rules engine, are we going to now be able to improve our inspection on the border? Yeah, no, I think it's fascinating to imagine that there is this world where the FDA is doing deep learning, machine learning to be able to do this. You know, I think perhaps also we underestimate maybe cases where maybe you have averted crises that we never heard of. You know, those might be the best TV shows. I mean, are there any cases like that? As you were saying this, I was trying to come up with some crises I could talk about.

That was actually what I was like me. So, you know, here's one that I just recently learned about. I think we're all very worried about drug shortages. I'm an oncologist by background. I practiced in academia. I took care of adults, but certainly when we think about children with leukemia, one of the drugs that's currently in a shortage is a leukemia drug for pediatric leukemia. And we try and think through how do we help avert drug shortages.

And in 2018, we had something north of 50 drug shortages, but the little known secret is that we helped to avert over 160 drug shortages. That's because, practically speaking, we have developed a whole staff focused on drug shortages. We've tried to start to figure out ways to predict what is going to cause a drug shortage and try and intervene beforehand, speaking directly to manufacturers. It takes sometimes a while to build that muscle, right?

You can imagine we have to first understand what are the causes of drug shortages and how are we going to go after them, but then practically speaking, once we do so, we can help to avert that crisis. I see the same kind of thing right now in food-borne outbreaks, where we have a call every morning at 9 a.m., where we're sort of talking about the things

that worry us for the day. I've officially stopped eating. Well, that's not good news for any of us. Because I'm getting very concerned about what food. newborn outbreak, there's going to be a threat of the day. But there's sort of like this continuous sensing to try and avert problems before they come. Yeah, fantastic. So let's change the channel. So we're watching CSI. Let's switch to a different

show. Let's get into maybe something more like Star Trek. So let's talk about the future because the future that's really becoming today and like stuff that I remember like five, 10 years ago were things that I thought would be sci-fi like, you know, gene therapy, gene editing, CRISPR therapies, you know, it's kind of crazy that I was actually talking with my oldest daughter and she was finding that you could get kits off of Amazon to do DIY CRISPR and that she could make our dog glow in the dark. She liked it not to make our dog glow in the dark, but I think in the end, I think it was just crazy that this is the world that we're living. And so, you know, there's two sides of this to dive into. So maybe the first side is like,

We'll get to kids and dogs going in the dark in a second, but for thinking about the clinical side of this, how does the FDA think about something like CRISPR? Because it's both gene editing, it's a therapy, there's a delivery aspect, you know, does this live with the FDA? Does this live with the AMA? You know, how do you even start to think about people

throwing these crazy new developments at you that could radically transform medicine and cure disease, but now has to really put. push the paradigm for the FDN new ways? So I think one of the things to go back to is this issue of risk-based regulation. You know, how are we going to start to solve for this in a way that appropriately has the right regulatory paradigm for this problem at hand and aligns with the level of risk? And as I think about this space, you know, I think that we are living in a space that gets

closer and closer to customized or individualized therapies, the landscape of the end of one, and how do we make sense of that? And practically speaking, in order to get there, you have to have some kind of framework that you apply that says, all right, before we talk about CRISPR specifically, or anti-sense, illegal nucleotides, you know, before we talked about any specific thing, like what's the framework we're going to apply and how do we do so in arithmetic? based way. Yes. And practically speaking, that includes what do we know about safety? What do we know about safety in vivo and in vitro? In the animals and in the petri dish? What do we know about biological plausibility? You know, what's our understanding from a biology perspective that this would

indeed work in the way that we would expect it to work? What do we have in terms of a predefined set of expectations in terms of clinical outcomes that we can monitor in an objective way to understand whether or not this intervention is making the difference that we expect it to make. Also, what do we need to think about with respect to whether or not this is going to apply just one individual person or we might now start to apply and scale this approach across multiple individuals? And that's actually going to start to balance how much risk we're going to take for this particular scenario. What can we think about consistency in manufacturing? And manufacturing, I think, starts to become more and more of an issue

across this space. And then practically speaking, what are the ethics? Like what's going to happen in the clinic? We may not responsible in our authorities for ethics, but I think we're responsible for at least consciously thinking about what's going to go on. So as I think about this space of incredible new therapies coming forward, we all need frameworks that we can apply and where every one of us can look at through a different lens and say, I can understand why that's the order and that's how we're going to start to work our way through it. Well, so let's drill down a little deeper because the end of one framework sounds like mind-boggling from a therapeutic point of view. But maybe there's actually presence, you know, so think about surgeries, like heart surgeries are all kind of n-equals-1, people are different.

There's some similarities, you know, and perhaps we're starting to look at things like carti, not just as, actually, to back up. So, car-tie is in this sci-fi category. You take T-cells out of your body, out of your blood, you re-engineer. to make them sort of supercharged, and you put them back into the patient. And the results of that are just mind-boggling, that tumors can melt away within days, and people are just literally cured of cancer. And so, you know, Carty has sort of biopharmus aspect. Maybe it has, like, a sense of doing molecular surgery, you know, like in these N-E equals-1

cases. So I'm curious to get your sense, like, N-Equals-1 is not completely unprecedented, but, you know, what are the things that we need to do to bring it into the future? So, you know, not only is InV1 not precedented, we kind of go back across medicine across time, we actually really started off as in a One medicine and became more quantitative across time, especially as we had interventions that were applicable to populations, including in the million. So practically speaking, we do have frameworks for in-of-one therapies. You mentioned surgery.

Another one, you know, in the landscape that I came from in cancer medicine was bone marrow or sim self-transplant, right? These are places where we need to first have the scientific innovation that goes along with biological plausibility to start to figure out how we're going to move forward with new techniques and treatments about what to do. But then start to apply a systematized set of expectations about how to refine this and get it right. So if I go back to a bone marrow transplant, you know, we started to develop refined processes to understand which patient was appropriate for transplant. where do we manage them in the hospitals? Ultimately, we went to the home. How are we going to do that? What's the actual therapy as well? Supportive therapies are going around, including supportive therapy for the family. And we slowly but surely worked our way through not only the individual treatment,

but also all the processes that we needed to go along. And I think what you're going to see in, for example, cell therapy in many of these other places is that not only do we develop end-of-one activities where we say biological plausibility and safety and good manufacturing and effectiveness, but we also start to refine how they perform in a greater scenario or greater system of care. And we've seen that happen over and over again across time. So, you know, getting back to this genia out of the bottle, the fact that you can get these crisps or kits on Amazon,

and actually literally you can YouTube this. There's like some guy that has like 15 glowing dogs. You know, how do you think about that when people can do things like that in their home, how do you think about what should the FDA do about things like that? So, you know, it goes back to this issue of authorities and sort of

where's our, you know, core responsibility and how we need to move things forward. You know, practically speaking, we're responsible for thinking about medical products that are now going into commercial use, and specifically for now the treatment of medical problems

or sort of justifiable claims. So the individual patient who's buying it off the internet, injecting it into their side, it gets into this very fuzzy area of exactly what are our authorities. And I think that becomes now really much more of a national conversation around rights and privacy as opposed to, you know, FDA approval of the kit. And practically speaking, if the kit was going to be approved for commercial purposes with claims and labeling, et cetera, that's when it starts to get into the FDA perspective. it gets really murky when we live in this landscape of the internet without claims.

You know, we see that pop up not just in CRISPR kits, but CBD and vaping. Like, there's a lot of other places. And, you know, that's why I kind of went back to that point around authorities. Like, there's sort of, like, really clear guidelines of what does the law say? And then, like, as you move out, how do we think about that? So, and, you know, maybe the ultimate sci-fi example of thinking about FDA into new areas are that even algorithms themselves can have therapeutic or diagnostic value.

And how do you think about sort of regulating these algorithms themselves as they change and go through revisions and have impact on how we make either clinical decisions or even our therapies themselves? So this is a really important area of, again, new regulatory paradigms and really trying to figure out,

how do we do this? And as I think about algorithms and the regulatory paragraphs around them. First of all, I tend to divide this into two main categories, algorithms that have a responsibility of acting as a medical treatment. So essentially software as a medical device. And there there's a risk-based paradigm that asks the question, does this particular software product ultimately basically take the place of the judgment of the physician

and ultimately now make a clinical decision on the physician's behalf without the physician? intervening. And depending on whether or not that's going to happen, then there's a differing set of expectations in terms of the development of the regulatory paradigm. There's a couple of issues, though, that goes along with this. One is that as software can update so quickly, developing regulatory paradigms that allow also update cycles that keep pace with software updates cycles is one of the things that, as FDA, we're working on. Yeah, actually, is that even possible? I mean, because, you know, people can update software obviously very quickly. So this is something that as FDA, we've been speaking publicly about quite a bit,

can we come up with essentially preconditions for software updates so that if there are strong quality controls in the way software is developed, well-understood product performance in terms of the expectation updates, can you now have algorithm updates that are as expected and don't require the same level of review. And so that's something that we're certainly spending a lot of time working on through a series of pilot projects. Also, the other part of what you've just mentioned in terms of our sci-fi land is that, practically speaking, software and algorithms

are actually also innovating all across the spectrum of life sciences and healthcare just outside of what we regulate. So it may not necessarily be a software product that's acting as a, diagnostic or treatment activity, but it's a software product that's intended to support life sciences more globally, whether that's to make clinical trials more efficient, to match patients to clinical trials, to curate data, to help do workflow in the hospital. And all of those kinds of software products, we don't directly regulate, but importantly, those products also need some good signals of here's what good looks like, and here's how you should think about good software controls in those settings as well.

You know, actually one bit of news that came up was Google purchasing a huge amount of health care data. Yeah. And data and understanding how that gets regulated is, I would think, would also be a really tough question. I mean, how do you think about, you know, how these new kinds of data that people are generating and then new people want to get access to that? You know, how does the FDA think about ownership of the data, privacy, and, you know, what are the opportunities there and the challenges? I thought you were going to go there in this session. So, you know, so data ownership and privacy. So there's an easy way for me to get out of this as the FDA,

which is that practically speaking, when the data comes to the FDA, it's the, much of the data that comes to the FDA is the proprietary information and confidential information that belongs to the company, and so we treat it as confidential information. And then there's other information that we use, for example, for drug surveillance

and those kind of things that are sort of more publicly available data sets. So, you know, in a lot of ways, I think that the easy FDA answer is we don't have a lot of things that we specifically have to worry about. But in my CIO role, I just recently started pushing on the fact that I really think we need a chief privacy role at FDA, which we don't currently have. And when I brought this up, people said, well, you know, the data that comes to us is de-identified. We're, you know, this is not the problem that we're really living in. But, you know, if we go back to what prompted the question, which is, you know, with the ascension data and what's going on right now in the Google story, I think practically speaking, our laws of the past, HIPAA, really contemplated a different world than we live in right now, whereby essentially in 2019 and going forward is very hard to maintain privacy of any individual. And maybe even hard to deonomize.

It's really very hard to denomize. And it's not just genomics data, right? The longitudinal story of your health care, every single time you visited the doctor, how much medicine you received, whether or not you got an additional test as an EKG, you know, that pattern is your unique footprint as well.

And so there's lots of different ways that data these days actually has a unique and representative pattern that really is individual to you. And I think, you know, the reason I brought this up at FDA as a chief privacy office, is that I think, practically speaking,

even information that is de-identified from a HIPAA perspective actually still is probably re-identifiable, even in our context. And we need to be starting to think about what does that mean now and in the future. And also, you know, what are some of the creative ways that we can start to prepare? Some of it's just having the conversation. Some of it is making sure that we are absolutely fierce

when it comes to security and understanding who's accessing what data for what purpose is. But it's also, when do we start using, for example, synthetic data? How do we actually start to think about what new tools and techniques and tricks we can use for data now and in the future to preserve privacy? And I think that's all of our responsibility. Well, so I also wanted to talk to you about a different way to think about data, which is we could imagine in clinical trial the future that's maybe fairly different. So, you know, I think, I don't think anybody disagrees or maybe I could be wrong that we, it's important for the FDA to test toxicity. You know, we don't want to put out things that are toxic.

But maybe a bold thing to say is that, you know, FDA will run phase one clinical trials, we'll review phase one clinical trials. But maybe we don't need phase two or three. Maybe we, especially for maybe life-threatening diseases, we let real world evidence and payers decide efficacy, as they're going to do anyways through reimbursement. And that maybe the FDA could actually pull back and data gets used differently. Do you think that's viable?

So three things. to kind of go into this, that will underscore ultimately what I believe is a resounding yes. Yeah. So, practically speaking, we are already starting to see new drug development paradigms in terms of starting to shake up their traditional phase one, phase two, phase three happen. And, you know, practically speaking, we've seen drugs approved based on phase one data. We've seen expansion cohorts all happen within the phase one setting, which is basically you now end up with phase one trials with a thousand patients on a phase one

trial. That's not the way I was taught as a clinical trial. And there's sort of phase one slash two or something like that. Yeah. There's some of them phase one too. For them, they're just expansion cohorts in what it's traditionally caused phase one. But practically speaking, I think that what we're seeing is a blurring in the phases. What we're also seeing now is contemplation of platform trials. We've been talking about platform trials for a while. They're really hard to pull off. They're hard to pull off because of issues of contracting and intellectual property. They're hard to pull off because the underlying infrastructure is tough, which I'll come back to. But, you know, practically speaking, we've talked about platform trials

where we can now, in one clinical trial setting, start to evaluate multiple investigational products simultaneously. We've started to say, can we, once approving a drug, start to now use information in the real-world setting, whether that is prospective or retrospective, but classically said real-world data and real-world evidence, to start to create a total product story or a totality of the evidence

around this particular product. So I think this is the landscape we're going to. I also think that we had essentially an accelerant put into the story in December 2016, which was 21st Century Cures. If you look underneath the hood of the 21st Century Cures legislation, what you see are a number of elements

that push us in the direction of starting to accelerate our clinical evidence development process. What kind of elements? So this includes starting to double down on how we think about surrogate inpoints, how we use patient reported data in the process, how we actually start to understand and enable platform trials, how we now use real world evidence, and asking FDA to get smart about when and we can confidently use real world evidence. And so I think that all of those were enabling features within 21st century cures, and now we all have a responsibility to start to figure it out. My last point around this, which is that it is really hard to do because

it takes putting the toe in the water, and that has to happen with some company or some investigators, you know, core baby. And that actually is really hard because it's hard to want to subject your particular product that you're studying right now. It may be your only shot on goal into a clinical evidence framework that we're still trying to all figure out. And so I'm not surprised it's taking us a while to figure out. We have to figure out not only how to do the work of new clinical evidence development paradigms, but actually people have to be ready to participate and it's taking us a while. You know, and it's interesting you mentioned 21st century cures. You know, I'm curious about, you know, to connect this to how we think about how innovations like this happen

with all the political landscape that has to make it happen. And, you know, what is this interplay between politics and the FDA? I mean, I hear there's an election. coming up sometime soon and that these are things that are, you know, these things turn into realities for the life that we all have to deal with here. It's really interesting. So if we look back, so Medicare Modernization Act was passed in 2003. So I'll sort of use that as my starting point. If I look back to MMA in 2003, around that time, we were contemplating sort of new payment

and delivery models, comparative effectiveness. there was a report from the Institute of Medicine in 2007 around building a learning healthcare system where basically available interconnected data could help us continuously optimize both healthcare delivery but also understanding performance of drugs and devices. That piece of work from the Institute of Medicine in 2007 basically said in order to pull this off,

we need a digital infrastructure. And basically, you know, it was a treatise, said, here's what's going to happen. The reason that's so important is that then we had something really important in 2008 or so, the global financial crisis, which then led to the stimulus bill. So it was because that treatise was already ready

and also came along with the point of view of we needed digital infrastructure to pull this off that embedded within the context of the stimulus bill, we got the High Tech Act, which led to the full-scale distribution of electronic health records. And we can all talk about electronic health records and good and bad points of view. But what you can see is that a big international experience,

the GFC, actually then had a direct day-to-day result in terms of an enabling digital infrastructure in the United States, circa 2009. You know, there's a bunch of different examples along the way, but if I then think about what was happening in terms of parallel legislation on the House and the Senate side that ultimately became 21st century cures, we had this conversation going on

around innovative legislation to try and accelerate the development of cures. But I don't know if many of you probably remember it largely got put on the shelf. And then we were moving into the election in November 2016. The election happens and now as a country sort of in a rather tumultous state

trying to figure out what might be bipartisan and 21st century Cures gets pulled back off of the shelf in December 2016, get signed into law. And so I think, you know, again, this was a piece of legislation that had been formed over the prior two years, a lot like that IOM report from 2007. It was generally ready to go. Yep.

And then there was an event, and then that's what pushed it along. No, that's fascinating. Okay, so let's change the channel one more time. So let's go to Food Network. So, you know, there is an F&FDA, right? and the food part, I think, is often underappreciated, and so I'm curious to dive in there. Or we could combine shows and talk about Star Trek on the Food Network.

So one of the fascinating areas that we see is genetic engineering and synthetic biology connecting to food. And you're seeing things like cultured meat, like meat that maybe originally the DNA came from an animal, but what you get out of it is like flame and yon or something like that in principle. and that, you know, when you start to see that being created, you know, how do you even think about that? Like, you know, and, you know, what do you worry about, how do you balance this innovation with making sure that we're being safe?

So, you know, I think these innovations have shaken things up a lot, right? So if we think about meat, there's a clear interplay in the United States between what's the responsibility of the FDA, what's responsible is the of the USDA? And so, you know, just in this particular space, we had to start to figure out, again, that language of the authorities. Where do we appropriately say this is the part that the FDA is responsible for and sort of our unique set of science-based skills versus this is the part that the USDA sees as their core responsibility trying to want to keep markets intact? And last year, we ultimately developed agreements with USDA so that the parts of the self-culture food activity that's got to do with cell culture, for example, and that part of the equation ultimately became the FDA's responsibility. And then as we moved now to marketing, et cetera, it became USDA. And I'm actually sure where the line got drawn.

But it sort of reminds me of a couple of things. So first of all, I go back to this point of authorities and jurisdiction. So as new innovations come about, we have to start to figure out, do we need to change the regulatory paradigm to make sure it works? The second thing is that we also need to think about how do we make sure consumers understand what's going on? So what does labeling look like? How do we talk about this? How do we have consistent language?

Some of you may have heard the story last year around almond milk and that almonds don't lactate. Well, you know, it's because, like, practically speaking, what's rice milk and almond milk and dairy milk? Like, you know, how do we make sure that consumers understand what this is all about? And so, as I think about the innovations in food, I also think about what does that mean in terms of the innovations in the regulatory landscape. And if we don't try and keep those two things in lockstep, we gum everything up. Well, and it's interesting because, like, I'm not sure there's long lines of people protesting the fact that almonds don't lactate, right? I think there are. But, yeah, it comes from an interesting different set of incentives there, right?

Yeah. And so it's just interesting, what do you call meat, what do you call milk, what do you call cheese? You know, another aspect that I think is really fascinating is also all the things you have to do with food-borne illnesses. And just thinking about, like, how does the FDA sort of wrap their heads around that, considering that food could be coming from anywhere? And these threats are coming from anywhere. Since we're going to go back to CSI for a second. So these days, if there's a food, moral illness, we will take the bacteria and essentially

do a whole genome sequencing to really understand the outbreak as well as which individuals who are ill are they all related to the same outbreak. And for example, with Listeria, which particularly likes to be cold, so it tends to get stuck on the nozzle in the plant, and ends up in, for example, your frozen peas or other places, that, you know, ultimately, you can trace now through whole genome sequencing the fingerprint of the Listeria, then all the way back to the individual who had the bad product at their local whole foods, for example. And so the ability to now trace that all the way through is doable through modern technology.

And one of the things the FDA does in concert with CDC and now really through international database is maintain a database of all the different genomes. so that we can also track back and do this more quickly. And so that's kind of where things have been going in the food outbreak space. The other side of it is the application of technology to trying to improve our ability to go essentially farm to table.

So, for example, blockchain and distributed ledger technology to make sure that we can trace all the way from the farm to the grocery store. And one of the things we've been contemplating at FDA is, like, ultimately, could you imagine the application on your phone that allows you to scan peaches and understand did the peaches have a full supply chain that we could monitor. And so these are the kinds of things that are

now a part of the lexicon at FDA. We have a program called Smarter Food Safety. And that whole book of work is around thinking about how do we move this field forward. That's fascinating. Okay, so we just have a few minutes left. And I want to take us now through the future. So we, you know, We started the discussion by talking about, you know, the FDA being over 100 years old. And I know we've sort of talked about the sort of challenges and the work that's been done. Thinking about, let's think about the next hundred years and where does that go. We're going to have new types of challenges. One challenge maybe just to throw out you to start is that we're going to have even just a different way of thinking about disease.

That there's all this science in the science of longevity of just what can keep us healthier longer, where it's not about treating cancer, it's not trying about treating Alzheimer's, it's about making sure you never get cancer or you never get Alzheimer's and all the therapeutics that would be done to expand lifespan and expand healthy span. You know, that seems like just completely paradigm-breaking.

You know, how do you think about that? Well, so, you know, to begin with, I think that it helps us to distinguish between aspects of biological aberrance, right? Like, where essentially biology's gone bad and we're trying to think about treatments to fix it, versus the really difficult construct that you're talking about, which is how do we essentially apply preventative approaches

and have the confidence that these approaches are both safe and effective in a longitudinal frame that really is hard to contemplate because we don't know if we've ever gotten there. It's really hard to know that this particular treatment was indeed successful for this individual. So I'm curious, you know, as you do this here, This has certainly been an area of focus for you. What's your thoughts?

Yeah, I think a lot of it is also having the biomarkers that you can to know that, you know, to your point, I think a lot of what you've been talking about is just it's about measurement. And it's about understanding how those measurements correlate with a harm. And that I think we just need to know what to measure and that's work to be done.

But I think that that's something that I think is a part of the science already. And I think that, you know, if I follow on that, line of thinking. It's also about longitudinality. It's about saying here's a surrogate or an intermediate set of endpoints that we're going to monitor, but we're actually going to understand longitudinally. How does that then translate to what we understand is happening across time? Historically, the way we've often thought about effectiveness is sort of as a fixed

book of work. And I think that what you're going to see over time is we're going to talk more and more about longitudinal performance. And this is a perfect example of that. And so, you know, one last really quick question. So what does this mean for the next 100 years of the FDA? You know, what do you see the, what's your vision for it? What are we going to be talking about 100 years from now? So I think the FDA of the future is going to be far more digital and informed by data at all times. A lot of the activities are going to be automated so that we can focus our time and attention on the things that need to happen first, that we're going to be able to ultimately understand how products are performing across time and actually use that information from across time

to right-size indications in a smart way. Okay, well, thank you so much. Thanks.