Science, Business, and Innovation in Big Pharma: A Conversation with Novartis’ CEO

Editor’s note: This article is based on an episode of the a16z Podcast, which you can listen to here

Hi everyone welcome to the a16z Podcast, I’m Sonal. Today we have one of our special episodes live from the road — well, not really live, but recorded during the JPM health conference here in San Francisco this week — so in this conversation, a16z bio general partners Jorge Conde and Vijay Pande join me to interview special guest Vas Narasimhan, the CEO of Novartis, which is one of the largest healthcare and pharmaceutical companies in the world: In terms of volume, they’re the largest producer of medicines, with 70 billion doses a year across a wide range of therapeutic areas from cancer to cardiovascular disease and more. 

So in this episode, we cover everything from the latest trends in therapeutics — from cell and gene therapies to RNA and proteins to other emerging areas and what’s science vs science fiction — we also briefly touch on clinical trials, go to market, talent, hubs, startups working with big pharma. And throughout, we consider where tech comes into all of this, and what happens when science becomes engineering.

But we begin with the *business* of science — and innovation, both inside and outside…

Vas: You build up R&D expertise in our industry over long periods of time. If you think about cardiovascular disease, we’ve been in it 40, 50 years. When you think about transplant and immunology, again, 40, 50 years; oncology, 25 years. So you build up an accumulated expertise, and really the art of it is to make sure you have a depth of new medicines to keep filling your pipeline in each one of those therapeutic areas. Now there are instances where we find new breakthroughs in areas we’re not in, those you have to really think about, think about, are you gonna really stay in that area for the long term?

The other element of the story is when you really have exhausted your pipeline. We’re not so good as an industry at this, but you have to also be prepared to exit, I think, areas where you’re gonna be sub-scale. And that’s something we’re working on; we’ve made a number of exits actually this year where we’ve just said, this is areas we just can’t sustain longer term.

Sonal: Can you give us a little bit more color on how you make those decisions, especially as, as a CEO steering this? That’s a pretty big — I mean frankly it’s a decision that every big company, regardless of industry, has to think about, which is essentially what to proactively invest in and what to proactively opt out of — “killing things”, as we call it in the media business, is a pretty hard thing to do — How do you think about that, and how do you tease apart the signal from the noise when you get a lot of inputs both internally and externally?

Vas: So we do it currently in, at two levels. One is, from an overall portfolio standpoint, we’ve made the decision to really focus as a medicines company powered by advanced therapy platforms and data science. So in order to really make that happen we transacted in 2018 around $50B of deals to really change the shape of the company. We took principled decisions to leave consumer healthcare because we just didn’t believe we’d be a long-term leader in consumer healthcare; a decision to spin our Alcon business, which is to get out of medical devices and contact lenses. And alongside that, as we’ve moved out of those other areas, we made significant investments, acquisitions in this next wave of therapy: cell therapy, gene therapy, and in an area called, “radio-drug conjugates”, which is a nuclear medicine kind of area.

So that was at one level, at the portfolio level, changing a 20-plus-year trajectory to actually become a very diversified company. It really came out of a conviction in my mind that science is moving so fast you have to focus your capital and really focus your energies.

That’s at the macro level.

Now when you zoom into innovative medicine, we have to decide, okay, which therapeutic area do we stay in, cardiovascular disease — or do we stay in ophthalmology? I mean, those are pretty tough decisions, because if you take down an R&D effort… So one example for us was infectious diseases where we had a longstanding effort — it was not an easy decision — there was a lot of going around, are we really sure? Because you can’t change your mind now, you know, in three or four years and say, “I wish I had it back.” It will take you another 10 years to build it back up again.

You can’t change your mind in three or four years and say, “I wish I had it back.” It will take you another 10 years to build it back up again.

Jorge: The cycles of innovation in science are accelerating, the science is moving much more quickly than it has in the past. So assuming that that’s the case, will it continue to be true that it will take decades to build expertise in any given therapeutic area? In other words, will there be future emerging players that come much more quickly than they have historically?

While the pace of science is improving dramatically, we also have to keep reminding ourselves and being humble with the fact that we understand a fraction of human biology.

Vas: I think there can be very fast players who are really working on a couple of medicines or a couple of assets. But when I talk about building up a capability I’m really talking about a scaled capability that could generate new medicines consistently over time.

And while I do believe the pace of science is improving dramatically, we also have to keep reminding ourselves and being humble with the fact that we understand a fraction of human biology. And actually when you look at attrition rates in our industry — really the chances of success that we have — they haven’t moved in the last 15 years. Still when we bring a medicine into human beings, on average only 1 out of 20 works, finally.

And that has stayed constant despite we’ve had this explosion in new science.

Sonal: So I wanna quickly pause on that for a moment because that’s a pretty important point. So only — over the last, say, 10, 20 years, only 1 out of 20 medicines actually work in the human body?

Vas: Once we get it into human beings we have about a 5, 5-10% success rate, <Sonal: wow!> and it varies by therapeutic area. We’ve actually been fortunate at our company we average in that same metric about 8% to 10%, But if you look industry-wide it is about 5%.

Vijay: The attrition rates are pretty constant, but the costs still keep going up, too.

Vas: They do.

Vijay: How does that work out? Because in a sense, one analogy that people use is almost like trying to get oil out of the ground, and you know, the low-lying fruit — I’m mixing analogies here! — but the low-lying fruit has been taken and it’s just harder and harder to find new therapeutics.

Or do you feel like the science is moving fast enough that that’s not an issue?

Vas: I think we go through waves. There was a period of time where probably in the 1990s and early 2000s we had a pretty big wave of innovation and we could bring a lot of medicines forward. <Vijay: yah> We went through a lull for seven to eight years.

Now with, again, the explosion and ability to really understand the mechanisms of disease we’re seeing a renaissance, a record number of FDA approvals. We’re investing heavily in new therapy areas. I mean, 15 years ago people would’ve said, “You’re crazy if you think we’re gonna do gene therapy and cell therapy” — and all of the things now that we’re doing at scale.

You know, the costs really come from our ability to manage complexity. When you look at it over time the trials get more complex. The requirements from regulators get more complex. Because the science gets more complex, we can actually measure more things, so we add, and add, and add, and that’s led to an interesting pretty linear increase in costs per patient in our clinical trials.

I don’t think it has to be that way. I think what really our industry has not been great at is really deploying technology to make this much more efficient, so I think there’s a lot of opportunity.

The costs really come from our ability to manage complexity.

Vijay: And why do you think that’s the case, that it’s been so hard to deploy?

Vas: I think it’s — you know, we’re a high-margin industry, so unless it’s easy enough just to keep arguing to yourself it doesn’t really matter as long as we get another big medicine out it’s okay. Let’s just keep going.

Vijay: Yeah, well — if you screw things up there’s a huge cost.

Vas: There’s a big downside. But I think now we’re reaching the point where we have no choice but to really now engage technology. I mean, there are estimates now from various sources that believe you could take out 20% of clinical trials costs if you were to actually to really deploy technology at scale.

Jorge: If the attrition rates have been flat for as long as they’ve been, and there have been all these proliferations of new platforms out there — these gene therapies — is there a measure that you qualitatively — or even quantitatively! — can look at that says the medicines that are getting through are meaningfully better medicines, or different medicines?

In other words, the failure rate might be the same, but the impact of success is it greater now in any measurable way?

Vas: So it’s a very important point, and there’s no objective measure. I mean, various institutes have different measures, but nothing I think that is used externally. We internally have just set a very clear bar now for ourselves, primarily because we live in a world now where nobody wants a “me-too” medicine, or a medicine that’s just incrementally better. We say to ourselves, it has to replace the standard of care. And that usually means it gives such a big clinical benefit to patients that it just becomes the de facto medicine of choice in that therapeutic area.

That’s a shift. It means a lot of projects no longer make the cut because you’re really asking yourself, If I don’t have something really transformative, I’m not gonna take it forward anymore. And so all of our research teams and development teams are having to now come to grips with that: that we will stop projects, unless we really believe it can redefine the standard of care.

Sonal: I have a process question behind this because it’s parallel to this idea of basically going for a slugging average vs. batting average — outsize hits with great outsized impact. So behind the scenes, what are some of the mindsets that you and the R&D teams bring to bear to make these investments for slugging versus batting average; how do you set things up to make that happen?

Vas: We have all of the various review committees, and portfolio meetings, etc. But really what it takes is a lot of discipline about the criteria that you’re using. So we have very clear criteria and we try to apply that rigor. I there’s a lot of romanticization in R&D about big ideas. So much of it is just about discipline, and discipline.

Sonal: Right, the nitty gritty.

Vas: The nitty gritty. Disciplined execution of how you look at projects. So I think that’s one element.

I think second you have to build patience, because part of the reason mediocre projects go forward is you start to worry you don’t have enough in the pipeline, and you start to lose faith that something’s gonna come.

And you have to believe in your own scientists and your own R&D engine to say, I’m gonna say no five times ‘cause I believe the sixth one could be the big one — rather than get worried and just start letting things through, because actually what you do is you crowd out the money.

Vijay: It’s an opportunity cost.

Vas: It’s a huge opportunity cost when you take those, and that’s been a real ongoing challenge for us. I think the third element is to bring a real lens of what does it take to be successful in the market?

Historically we just had a belief that if we had a great product it’ll all work itself out. Now we actually ask the market access teams that have to negotiate with payers to show up at every meeting and say, “Actually even in phase two it’s a really early for us. What is it really gonna take to, let’s say, bring a new medicine forward in ASMR, a new medicine forward in multiple sclerosis?” And if we don’t make the cut we just have to be brutally honest with ourselves.

Vijay: Are reimbursements more important, or more on your mind, than for just getting past the FDA?

Vas: It used to be we’d think about reimbursement as we got to launch. Now we’re thinking about it really early in development.

Historically we just had a belief that if we had a great product it’ll all work itself out.

Vijay: For people that are new to this space — or just a lot of entrepreneurs, they think that the FDA is the real challenge and just getting something through clinical trials is expensive, and hard. And that’s true.

But the reimbursement — being first in class, having this huge jump in care — that is the real challenge. And so what I would love to see, especially in our founders, is for them to work backwards — but work backwards not from getting through trials, but work backwards from reimbursement. <Sonal: Ahh, fascinating.>

Jorge: And the way that Vas describes it is absolutely true, a lot of people view reimbursement as a process to get to market access.

But reimbursement is really just a proxy for value proposition… So what are the actual user stories? Who’s gonna actually value this? Who’s willing to pay?

It’s almost like a pricing study, and it’s almost like price discovery in the consumer world. In this case it’s obviously the payers, not the direct beneficiary of the therapeutic. But they do bear the burden of the cost. And so they’re the great arbiter of saying, Is there a true value proposition? And that’s why when you talk about industry moving away from me-too drugs, it was because a me-too drug arguably could not show a very significant marginal increase in value proposition and therefore it was very difficult to justify and increase the premium price. And so that historically has been the big challenge.

A lot of people view reimbursement as a process to get to market access. But reimbursement is really just a proxy for value.

Sonal: On that note, I do find it ironic that a big part of business is still generics. I mean, what is that but a me-too drug? How does that fit into this big picture?

Vas: Yeah, so, you know, if you look overall at Novartis, generics from a sales standpoint and a value standpoint, it’s a small portion of the company.

But look at a volume standpoint, and it’s the biggest part of access. And so really what our generics business does is when medicines go off patent, we then produce them at scale. I mean we’re the largest producer, for example, penicillin in the world. So we have a huge role to play in providing access to medicines around the world. I mean, right now Novartis reaches about a billion patients a year through our work, and a lot of that is through our Sandoz generics unit.

Jorge: So if you break it down, if there’s 70 billion doses that are Novartis drugs every year, how many of those 70 billion are generics?

Vas: I would say roughly 80% of that.

Sonal: And is it standard that big pharmaceutical companies have their own manufacturing facilities — and do you see that changing any time in the near future??

Vas: Most pharmaceutical companies have their own <Sonal: ok> manufacturing. I mean, there’s different trends right now. There’s a pretty significant increase of use of Chinese and other producers for many elements of the manufacturing, but still historically we’ve had our manufacturing facilities.

The biggest trend we have right now is a shift to these advanced therapy platforms. So what we’re having to do is as our volumes go down — and the older medicines that were produced in huge volumes… we’re now building up cell and gene therapy production facilities around the world. So that’s a shift we’re seeing.

Jorge: You talk about Novartis becoming a medicines company using data science and novel platforms, but you’re very specific about saying “medicines”. Are medicines and therapeutic synonyms in the Novartis mindset?

Vas: I would say yes. There’s, of course, a gray zone here as to what is a therapeutic.

But we would say, you know, medicines is our proxy for therapeutics. I mean, one example we launched in the U.S. a digital medicine. I mean, with Pear Therapeutics this is the first digital app with an FDA label that’s being used for opioid addiction and other psychiatric illnesses. And it is literally an app that has run clinical trials and has gotten an FDA-approved label. So that’s truly an example of a therapeutic, but I would put that within our world of medicines.

Jorge: So software is a drug?

Vas: Yeah, software as a drug.

Jorge: [What’s the] most surprising indication that you would expect to see for a digital therapeutic? Because I think most people assume that it’s gonna be around behavioral health issues or addiction, like the work you’ve done with Pear. Can you imagine moving beyond that from an indication standpoint for digital therapeutics?

Vas: I mean, my hope would be we could develop one for obesity, right? That somehow that a digital therapeutic could actually just move the needle a little bit more on obesity — it’s such a massive issue for society — and it should be one where a behavioral intervention on top of other interventions could actually move the needle. Because so much of it is behavioral.

Sonal: There’s not an, example [of a digital therapeutic] that’s non-behavioral in your future?

Jorge: Right, you’re not curing sickle-cell [anemia] with an app!

Sonal: Right. I mean, I would put a guess around fertility, but one could argue that’s also psychosomatic.

Vijay: Yah, behavioral… The thing is… if you think about the modern medical marvels, I think about antibiotics. I was sick when I was in college and I had a super high fever. I got an antibiotic and next few days I’m fine. Maybe without that I’d have been dead, and so that’s kind of magical. And it’s not like I have to take antibiotics for the rest of my life or whatever, I’m just cured.

But the amazing thing about behavioral is that’s where you don’t have this. I can’t imagine that you have a molecule that cures depression, that you take and you’re just done. Or you take a couple doses and then you no longer have Type II diabetes. And behavioral is really broad: It’s depression, it’s smoking cessation, it’s Type II diabetes, it’s even quite possibly Alzheimer’s. I don’t know if you’ve seen all these?

Sonal: I’ve seen a lot of recent papers on this, it’s fascinating.

Vijay: Yah, yah. And so these are actually the areas where if you look at the biology of Alzheimer’s disease, that’s just a mess, you know? It could be that for these things where you have a very clear target, I just have to hit the ribosome with the bacteria and then we’re done. That’s easy.

But there may be actually in the future things where that just is hard to hit with a molecule, and all that is primarily behavioral.

Sonal: Interesting. So basically you’re almost arguing the question might be moot because all of disease is behavioral in some capacity?

Vijay: Well, no — all the stuff that’s hard. <laughs>

Jorge: Complex is complex…

Vijay: The low-lying fruit, molecularly, is not behavioral.

Jorge: There’s this infrastructure layer that’s being created now around gene therapies. So as folks figure out manufacturing, as people think about delivery, as people think about all of the various components or modular aspects, do you think those are things that necessarily would be owned by one company — or are these horizontal infrastructure layers that a third party should develop and deploy across the industry? How do you think this plays out?

In other words: Is there a startup that figures out AAVs? Do they, sort of, supply AAV to the industry or do they go and develop their own… gene therapy?

Vas: It’s a very timely question; we don’t know the answer yet. I think right now in this nascent phase that we’re in, we believe that we need to just own it. Because the launches are so important, that we can’t afford there to be a lot of experimentation and not really owning the supply chain. We’ve done $15 billion of acquisitions just last year in this space, not including all of our internal work in each of these areas, so we’ve chosen to build out the infrastructure ourselves.

I think as the technology matures we’ll get more comfortable about which areas we could send out. I also think the entrepreneurial world will also figure out where they can play a role. I think that’s still all being figured out right now, and I actually don’t have a view yet. I don’t know what’s gonna be the elements we must own and what are the elements that we could afford to give to other parties.

Sonal: You know, on that note, I’d love to hear from you more about how you figured out the build vs. buy piece then. Because a big part of your work is focused on innovative medicines. And you made this argument that it takes 10 years to build up a base — even longer, 20, 30 years — and yet you’re also acquiring the expertise for the very new, cutting-edge things. Which almost makes it seem like you don’t have to even bother building up that base.

Why not just acquire it? So how do you navigate the build versus buy part of this?

Vas: I think when you wanna enter very new areas, sometimes it’s prudent to ask yourself, Does somebody have this much more figured out than you do?

So if you take the example of gene therapy, we acquired a company called AveXis, really, I think, the front, leading-edge gene therapy company. Now the scientists at AveXis, they’ve been working at this actually in their academic labs for 25 years. I mean, they’ve been working on trying to hone how to use AAV vectors to get to the neuromuscular system of children to address these issues. They’ve actually figured out the manufacturing. They built the manufacturing site. We were working on gene therapies ourselves in-house, but when we looked at that we said, this is an opportunity to really accelerate what we’re doing. And so it made sense, I think, to go, to go external.

There’s always that balance. We are a company that’s very focused internally on research. We consistently invest at the high end on internal R&D simply because we believe that’s the heart of the company. But what I’m trying to keep asking our people is if there’s somebody out there who’s got it better than us, let’s just go get that and then we’ll build off of it.

Sonal: Yeah, I love that. But there is a classic NIH – “Not Invented Here” syndrome —

Vas: There is.

Sonal: — And when you have a strong internal R&D culture, it does compete with NIH. A lot.

Vas: It does.

Sonal: So the question that really begs is, how do you then — with all these amazing acquisitions — integrate them into the company? And actually make sure the classic Chesbrough study of all these acquisitions not being killed by the big company… like, how do you balance that piece?

Vas: So I think there’s two things I’d say. One is, as an R&D person I have sort of the ability to really get in there and have the discussions directly with the scientists and argue why we actually need to go external and really evaluate the case, with hopefully objective eyes.

The other thing we’ve decided to do, at least with these very new three technology platforms, is leave them as independent units  and really let them grow up independent from the big R&D and manufacturing machine <Sonal: that’s right>. Because I think exactly for that concern. It makes sense to let them build up, and really incubate these new technologies, get them all sorted out, and then we can ask the question, What’s the right setup down the line?

Sonal: Right, right, right. That is what the classic studies show, that is the way to success. I did, by the way, find it very fascinating — because I wasn’t aware that you have a scientific background? — it reminds me of this idea that we have around CTO-led [companies], you know really having technical people at the helm.

So I am curious about your view — I mean, besides being able to talk to the internal scientists — how has that affected your own career and trajectory at Novartis so far?

Vas: Given the company’s heart is innovative medicine, and most of my background has been in drug development and really developing vaccines, and then developing various medicines, I think it gives me a really good insight into the heart of the company, our key technologies. If you think about our pipeline today, I know every asset, every clinical trial, I know all the clinical trial end points.

So that, I think, gives you a certain insight into where the company is heading. And also I think enables you to hopefully guide the company into the right areas in the future. I think it’d be self-serving to say that it’s better to have an MD/ R&D person running companies, but I think it does give you a different perspective on an R&D industry like ours.

Sonal: Right, it might even be able to help to be able to empathize when you are killing a project, that you actually know what it’s like to feel that.

Vas: Well, that’s for sure. <laughs>

So if you go back 10 years ago, something like a CAR-T therapy would’ve seemed science fiction-y… or at least maybe 20 years ago. If we look forward 10 to 20 years, what are the modalities of the future?

Sonal: Okay, so on that note what are some of the most interesting and most “innovative medicines” categories?

Vas: You know, when you look broadly right now I think you’re seeing a few big areas of high innovation.

In the whole world of CAR-T — so cell-based therapies — really what this is, is harnessing the power to take cells out of the human body, reprogram those cells, and put them back in the human body. CAR-T is the way we do that in cancer, but there’s certainly the opportunity to do that in many other diseases. There’s companies working on trying to cure sickle-cell disease, others working on other inherited disorders… so really reprogramming cells.

Jorge: So if you go back 10 years ago, something like a CAR-T therapy would’ve seemed science fiction-y… or at least maybe 20 years ago. If we look forward 10 to 20 years, what are the modalities of the future, you think?

Vas: I think a couple things will likely come. I think xenotransplantation, which has been in and out and worked on — and what’s interesting is every one of these comes up and down — so gene therapy, cell therapy popped up in the ’90s; kind of went away; popped up in the 2000s, kind of went away. And then, the key lynchpin issues were solved, and then it was unlocked in xenotransplantation, where you were able to make organs for transplantation in animals, that enable them to have a sufficient number of transplantable organs for human beings. I think we’re probably gonna get there in the next 10, 10 to years.

Sonal: Interesting.

Jorge: So regenerative medicine makes a real comeback?

Vas: I mean, well, I think another area — yes, xenotransplantation being one — I think the other area is we are gonna be able to start to solve problems of regenerating tissue. We already see examples where we in our own labs can start to crack, how can you regenerate cartilage, or how can you regenerate other tissues in the body? Which would, again, seem like science fiction. But I think actually harnessing the pathways to really get regeneration to happen, which would help healthy aging, is another thing I think will likely come. So there’s a lot of things that are still on the way absolutely.

Jorge: Can you imagine a moment in time where aging becomes a therapeutic area for pharma companies?

Vas: You know, we had actually an aging program, a small aging program for some time where we were trying to work on things like sarcopenia, which is muscle wasting, and similar kinds of conditions. It turned out to be very, very difficult, because, again, multifactorial.

And you probably need a medicine with behavior, with diet, with exercise, with all kinds of things to help healthy aging happen. But like I said, I mean we continue to focus on more of the pure regenerative parts. If you think about– the whole world of joints and movements has not really been addressed and cracked, and so this is an area where we have exploratory programs to see maybe we could find something. I mean, if you could regenerate cartilage, or tendons, or enable muscle strength incrementally, you might be able to improve healthy aging quite a bit.

Sonal: Fabulous. Why don’t we actually shift into the innovative medicines set of therapies?

Vas: Another big area, hot area is in the world of RNAs. So these are really ways to deliver — let’s call it, genetic instructions — into specific cells. This has been an area that’s been worked on for many years. It’s always been difficult, but I think companies are now starting to crack the problem of delivering RNAs into specific cells in a highly effective way.

Sonal: Can you give me just a concrete example of how that plays out with a real disease?

Vas: So there’s a couple of really nice examples now with RNA interference, and one that our company is working on is RNA interference to impact a factor that’s really a big part of heart disease: It’s called “Lp-little-a”. “Lp(a)” is actually thought to be one of the remaining risk factors for heart disease that has not been addressed — everybody’s of course addressed cholesterol extremely well, triglycerides — Lp(a) is another factor, but there’s never been a medicine against it.

And it’s really hard to drug Lp(a). And so the only way to really target it turns out to be using RNA-based therapies. These RNA-based therapies are able to block the production of the gene — translation of the gene into the protein — and then actually reduce the Lp(a) in the blood. And so this is one example of how we’re trying to take this [RNA-based therapies] into an area where otherwise you wouldn’t necessarily have a therapeutic against something that could have a big impact for patients with prior heart conditions.

Jorge: So RNA interference is essentially a mute button for a gene of interest?

Vas: That’s right, yeah.

Sonal: I love that, and that’s a great example. And by the way, Lp(a) sounds like the name of a rapper. <group laughs>

Just remind me really quickly: Obviously I know what I learned about RNA from biology class in the sense of proteins, but can you give us a little more about what’s unique about RNA-based therapeutic modalities?

RNA interference is essentially a ‘mute button’ for a gene of interest.

Vas: Yah, absolutely. So when you think about the history of our industry, maybe another way to describe the trend I see that’s happening is we used to be about chemicals — so small molecules. So for probably 100 years, most of the pharmaceutical companies had their basis in the chemicals industry. And so we made these small molecules that happened to have various effects on the body, and over 100 years we figured out we could really target what those chemicals do.

Around the late 1980s we realized you could actually make large molecules — large proteins — and make them be therapeutic. So this is antibodies and recombinant proteins, and that led to a whole new renaissance in our industry. And over the next 20 years and up to today, probably [are] still the largest categories, so-called biologic medicines. These are antibodies and proteins.

What I see happening now is a shift to a next set of modalities that move beyond small molecules and proteins, and that is now really touching other elements of what happens in a cell. So one is RNAse — which is really the way DNA gets translated into a protein and goes through an RNA — so that’s one new modality.

Another modality — both of them really are about editing DNA in different ways — one is to take the cells out of the body and edit the DNA of the cell or enable the cell to produce something different; the other is to do it inside the body, that’s what we call gene therapy. So we make that distinction of “cell therapy” and “gene therapy”.

Jorge: So cell therapy is ex vivo, gene therapy is in vivo?

Vas: Actually it’d be in vivo. 

Sonal: Right: inside, outside.

Vas: Inside, outside. So these are new ways of actually delivering medicines or creating medicines in the human body.

And now you see early-stage companies doing even more radical things, trying to turn red blood cells into therapeutics, amongst other things. So it’s really an expansion, if you think about it, of the game board of how you can address human diseases.

Jorge: I love, sort of, the sweeping history you have here in terms of starting with chemistry and then moving to large molecules, and then now moving more into the cell and gene-engineered world.

Historically every single drug program has been a very bespoke thing… its own ground war, right? You have your target discovery, then you have your validation, and you have your lead, then you optimize that molecule, and so on and so on. And, my sense has always been that because it’s so bespoke that there are some learnings that are generalizable in any given disease area but every program is a unique thing.

When you start to move to the RNA world, to the cell world, to the gene world, is it gonna become much more of a modular world where the first version of a CAR-T is gonna be by definition less sophisticated than the second version, but the second version will be built off the first? And you go from being in a bespoke world to going much more into an iterative world.

Vas: Unfortunately, in our industry it’s the answer is always that it depends. In the specific example of CAR-T, I do think that’s what’s gonna happen because you have such complex manufacturing that you’re gonna have the first generation, let’s say, of a CD-19 CAR-T, which is a CAR-T that targets P-cell cancers. And you’re gonna try to then move into the next generation that hopefully has more rapid manufacturing, maybe higher efficacy, and then even more rapid manufacturing. So you’re gonna get into that iteration.

Now it’s not like medical device iteration. I mean, this is still gonna take years to do, but you are gonna get to that iteration.

I think another way — what I see happening though with these new technologies — is real platforms insofar as once you have the backbone of the production, and even the go-to-market model depending, you can put multiple products onto the platform. What we have done at our company is build a global network of manufacturing sites that can take cells out of human beings, and reprogram the cells, and put them back in the body. And we’ve built the links into hospitals to enable us to do that. So you have that as a capability. You also have the capability to understand how to use what’s called a “lentivirus” to reprogram a cell. So we’ve got all of that.

Now, we can apply that in very different ways — in cancer, in sickle-cell disease, in inherited disorders — and use that same infrastructure to actually then keep pushing the medicines through. That’s very different than what we’ve had to do in the past, where every single medicine had a bespoke production process — it had to have its own manufacturing facility – now, we can actually build that platform and then layer medicines on. It’s no different in gene therapies when you think about AAV vectors (these are ways to deliver these gene therapies into the body): Once you solve the process, let’s say, for one of these vectors, you can apply it to multiple different diseases and not have to recreate everything again.

That’s a shift I see in how our industry operates.

Sonal: You know, I find that fascinating because it actually sounds a lot like what we talk a lot about, this theme around “engineering biology”, and when you bring engineering principles and mindsets to biology.

Vijay: Yah, you just mentioned multiple places where the repeatability, and different aspects of engineering have already come in. How is this trend gonna continue, where is gonna be the new places where engineering can play a role?

Vas: Yah I think the easiest place is gonna be in continuing to innovate on the processes by which we really manipulate cells, and genes, and really get to the next wave of manufacturing. ‘Cause I would say we’re really on the only learning-to-crawl [phase] with respect to most of these technologies and how we produce them; pretty rudimentary.

And so I think there’s gonna be an engineering problem of how do you handle cells, and how do you handle the vectors, and make this a much, much more efficient process? And there’s a lot of very smart engineering firms now working on that space. So I think that’s one place.

An area I’m quite interested in is how we can get much smarter at actually engineering the medicines themselves?

Vijay: Yah.

Vas: I mean, we spend a lot of work investing in AI and 3-D visualizations to say, In a so-called world of chemical biology, or if you even think about using quantum chemistry to really understand how to define your monoclonal antibody, how can we do a lot more engineering of medicines up front?

Because we really come from a heritage where everything was just trial and error. We just tried many, many, many molecules until we found one that worked and then we just took it forward. How can we become much smarter about that? And so in our research lab they’re spending a lot of time thinking about, How do we engineer the medicine up front to do what we want it to do? And that’s a whole new world, I think.

Vijay: Yeah. Also, there’s presumably gonna have to be a culture that shifts along with this. I read Alan Greenspan’s book on the history of capitalism, and he talked about how in Europe, furniture was bespoke, and you’d make this beautiful chair, and it’s this handicraft. And they actually hated the idea of factories in engineering because it takes the art out of it.

Sonal: Not “artisanal” anymore.

Vijay: Yah, it’s not artisanal anymore. But I think once you shift towards that mindset where you have reproducability and almost a factory-like process, that can be built.

Once you can have that shift, as long as everyone is ready to make that shift, then things can really start rolling. But there has to be a major shift. And it turns out, in America people really didn’t care about that artisanal part as much and we got factories, and that was a huge part of the early, like, late 1800s. And I’m curious — you spoke so much about how Novartis is changing — and so presumably, there’s an internal cultural change as well?

Vas: Yah. We’re trying to make a quantum change, I think, in our culture. What we have is — as context, I believe we’ve moved to become truly just a knowledge organization; vI mean, so much of the rudimentary tasks have been either automated or sent to third parties.

So we have a whole organization of knowledge workers, 50% of them are millennials, and they wanna work in a very different environment than, let’s say, an industrial company 20 years ago. And so we call our new culture “inspired, curious, and unbossed”, and we want our people to feel inspired by the work; really curious about the outside world; and not live in a bossed company, but really lived in an unbossed, much more empowered, company.

And when we talk about areas like digital and data science cell and gene therapies, it’s so critical because these are so complex areas. You need your people to figure out the answers, and we can’t be in a world where everybody’s waiting for management to tell everybody what to do, because none of us know what to do either. Because these are whole new spaces for us.

So that’s a big shift. The other element of that journey is to get a lot more comfortable with rapid failure. I mean, we have a much more rapid cycle. We can’t expect that we’re gonna sort it all out and it’s all gonna work perfectly because the first thing we’ve learned already in cell and gene therapy is nothing works the way you expect it to work, right?

Vijay: Yeah. And so you built a platform for rapid iteration.

Vas: That’s the idea, yeah.

Sonal: What I love about that, it reminds me of computing and software companies and the shift from waterfall, to like more devops, agile;

Vas: Yah, the same principle. We’re a little late to the party, but yeah, that’s the idea.

Sonal: Even microservices-architecture enabled… Yah, it’s the same kind of principle. That’s fascinating.

So, we haven’t talked about the big elephants — in a good way! — in the room: of AI and ML, you know, artificial intelligence and machine learning. Let’s talk about AI, and ML, and data. I mean, it’s not a question of if or when, it’s how.

The question I have — because quite frankly it’s a very hyped topic, too, and people promise all kinds of things when they talk about applying AI and ML to medicine — I’m very curious for your take, as the head of Novartis: Where do you see the strongest applications of AI and ML?

Vas: Well, I have to first say I completely agree about the hype cycle here. I mean as we’ve gotten quite scaled and working on digital health and data science, we’ve learned that there’s a lot of talk and very little in terms of actual delivery of impact.

But we’ve learned a lot. I think that the first thing we’ve learned, is the importance of having outstanding data to actually base your ML on. In our own hands, in our own shop, we’ve been working on a few big projects. And we’ve had to spend most of the time just cleaning the data sets before you can even run the algorithms. That’s taken us years just to clean the data sets, and I think people underestimate how little clean data there is out there and how hard it is to clean and link the–

Vijay: It was never intended to have this type of analysis done, right? It was intended for a given project and that was it.

Vas: Right, yeah, that’s, that’s been so much of it. And then the other thing is there are patterns that can be really learned from it.

I mean, do you have a good training data set to actually train the algorithms? So there’s a few places I think we’ve seen a lot of traction. One, I think the vision or image problem has been very well, well solved. So right now we’re in the process of digitizing all of our pathology images and having AI just be able to scan all of the pathology images… at Novartis.

Vijay: That’s great.

Vas: And we have millions of, of course records of biopsies and tissue, so that’s a huge project we have called PathAI; really work on that as a single example.

Sonal: I mean, that’s, like, a goldmine.

Vas: It should be.

And if you then apply that as well to the vast storage of imaging data we have from our clinical trials, we have 2 million patients in clinical trials at least in the last 10 years. And we have MRIs, CT scans, retinal scans, heart scans, and all of that as well. I think ML can have at least a significant potential to really find hopefully new insights. So I think the vision/image problem has been one we’ve been able to really take on.

Another area is in our operations. So we built an operational command center. It took us, as I said, two and a half years to build it. We call it Sense, and what it enables us — a team sitting centrally in our headquarters — to do is look at all of our clinical trials in the world, and AI is predicting which trials are gonna enroll on time or not enroll on time; predict which ones are gonna have quality issues or not quality issues. And the reason we could do that is we had 10 years of history to train the algorithms.

And we run about 400-500 clinical trials a year, so we have a lot of data that we could train the algorithms.

Jorge: Does that mean you had to dig all the way back into automating real-time information on clinical trials? So the data entry on a clinical trial as a patient is enrolling, has that all been automated as well? Because that used to be done on pads.

Vas: It’s a great question. Really what we focus on is the operational data, so one level up from the patient: Is the trial enrolling on time? Are the sites open? All of those elements. On the operational side, it was really easier to do this than trying to get all the way down to the patient-level data.

The other area interestingly in the financial area as well, we find that AI does a great job of predicting our free cash flow, predicting a lot of our sales for key products, and it does better than our internal people because it doesn’t have the biases and the data is very clean. And we’ve got very long-term data. So that’s been all positive. But there have been other areas where I think it’s just simply not met up.

I mean, I think the Holy Grail of having unstructured machine learning, go into big clinical data lakes, and then suddenly find new insights — we’ve not been able to crack. Mostly because the data, to link it up, we are spending a lot of our energy just trying to get all of our data harmonized so that some algorithm could maybe find anything of use.

Jorge: An area that’s desperately in need of innovation is how we think about clinical trials, recognizing we have to operate within the system that we live in.

But if you could design testing safety and efficacy in humans on a blank sheet of paper, what would look different from a clinical trial perspective — versus where we are today and the way we do it now?

Vas: The ideal world, if we could get there, would be to have integrated health records where we could easily insert the fields that we needed for clinical trials and then we could use something like a blockchain (or some other distributed architecture) that enabled patients to consent for us then to access the data, and then, you know run the trials through that. And that would eliminate so much of the effort of creating a second database versus the EHR, monitoring that database, QAing that database, locking that database. You could get the data on an ongoing basis. I mean, it would radically simplify this. I believe that’s a huge, huge opportunity.

I think we have a long way to go, as you know, because EHRs are not where they need to be; we’re probably not where we need to be to get there. But I see opportunities in baby steps to actually get towards that, and we’re experimenting with that, and other companies are as well.

I think we have a long way to go, as you know, because EHRs are not where they need to be.

The other thing people talk about – but I’ll take a skeptical voice around it — is the ability to use real-world evidence to try to get at these things. But as somebody who’s worked in clinical trials for most of their time in the industry I do believe that the power of randomization, the power of blindedness, is what enables us to control for all of the things we don’t know about the complexity of human life and human biology. And to think that we’re gonna take that away and then be able to really determine the efficacy of a medicine puts a lot on the statistics that I don’t think we have.

And so I’m more of a real-world evidence — I don’t know if it’s a skeptic, but realist — who says, after we have randomized placebo-controlled data that really tells us that something has the effect we think it is, then to explore more effects or explore more uses through real-world evidence makes a lot of sense. But I don’t see this as a panacea that suddenly will make the world much, much easier.

Vijay: I mean, that’s my expectation as well, is you’ll see it first come out as, like, a phase four. Something where you’re using real-world evidence, which right now is used for reimbursement anyways, and so on. But then maybe see how far it can go back… but it’s not gonna replace it.

Sonal: You guys don’t think — I mean, not to sound naïve — but you don’t think a secular shift, like, sensorification of everything, and everyone really truly has continuous wearables, like everyone’s wearing a CGM [continuous glucous monitor] by default…? I mean, I hear you on the statistical side, there’s a lot of other spurious variables and things introduced into that equation. But it’s a very huge, very deep, nuanced, patient-level set of data; seems like we can’t ignore the power of that. Where do you fall on that?

Vas: First of all, I would say just in general, sensors is another place where there’s been a lot of hype of expectations. I mean, we’ve been really trying to explore the use of sensors in clinical trials now for (in my own experience) at least six years.

And it’s been tough to get sensors that really meet the clinical-trial grade outcomes. I mean, they really show that they can be validated versus our current clinical end points. Now as consumer products, fine. I mean, perfect.

Sonal: Yes, right, but you’re talking medical grade.

Vas: But here we need to really be able to replace pretty rigorous tests, and we haven’t seen that yet. Now we’re exploring the use of many different sensors, and the real power of it is a continuous variable to actually see how a patient’s doing in between.

Sonal: Like a longitudinal study.

Vas: Between the study visits. And so I think that will help a lot. But I still think in the end you’re gonna need to randomize and blind. I mean, if you don’t randomize it’s really hard to figure out what is going on in a complex system.

Vijay: I agree with– short term. I think longer term…my gut feeling is that this is a solvable problem statistically, because there’s even issues with clinical trial design that one has to overcome today because randomization isn’t just picking people literally randomly necessarily.

Vas: True.

Sonal: It’s a sample, not a population… Yes, I agree, small “n”, not capital “N”.

Vijay: And there’s been a lot of work on causality theory in statistics. So there are advances… but I think it’s not there now.

Sonal: More to say there? That was really interesting.

Vijay: What’s the role of bringing innovation in from the outside through partnerships and M&A, in ML?

Vas: One of the things we’re working through is how do we get the talent, you know?

Vijay: Yah.

Vas: As we really start to organize the data — and we’ve brought in some really great talent to really help us work on data architecture — and come up with a whole data landscape for the company, we’re always now thinking about, How do we treat data as an asset? That’s one of the things we keep harping on, is that data is an asset. Whatever data we collect from the external world has to be organized in a clear data architecture.

But then to take the next step to get the data scientist — to really find the insights — we’re not the traditional place for data scientists < coming out of Stanford who’s looking for where they wanna come…

Vijay: That’s right…

Vas: So we’re working through partnerships with universities, potential partnerships with startups; actually here in the Bay Area we have a center called Biome, where we’re working with different startups. And so these are the things we’re trying to do to engage and hopefully create an ecosystem that helps us do this, and not just do it ourselves. Because I don’t think we’ll be able to attract the scale that you would need.

Vijay: This is a Reese’s Peanut Butter Cup issue because startups sometimes have some innovation on the data science, but not the data. <Vas: yup> And so bringing the two together, I think, seems like a very natural combination.

Sonal: Where does the Reese’s Peanut Butter Cup bit come in?

Vijay: Oh, peanut butter and chocolate. Like, he’s got the peanut butter and they have chocolate.

Sonal: I don’t eat Reese’s Pieces.

Jorge: You don’t remember those commercials?

Sonal: I don’t remember them.

Jorge: Oh my God. <chuckles>

Sonal: I watched a lot of TV when I was growing up but I don’t remember that;)

I find it fascinating — because a lot of our bio entrepreneurs/ companies, the #1 thing that they tell me, [is] that drawing data scientists is one of the hardest challenges they have to face. And so you’re saying with the Biome and other things you’re doing that you have to kinda create the pipeline, not just source it?

Vas: That’s right.

And to your earlier point, the opportunity is to say, “Look, come and work with us and we’ll let you work with our data, and you can learn and we’ll learn, and maybe then there’s a partnership that’s created, or maybe you wanna come work for us, which actually would be great.” But that’s how we’re approaching it.

Vijay: Well, and here’s actually an interesting shift that can happen in academia. You know, with my group at Stanford, many people actually during their PhD have gone to work in pharma. And it’s impossible to pull the data out of pharma, but it’s actually easier to put the grad student into pharma.

Vas: Into pharma for sure.

Vijay: And so the grad student comes with the code, runs it on, internal through the firewall of pharma and we see how it does. And you can still publish papers — where maybe you have to obscure what the target is, or something like that — but you can at least see how things are going. And there’s nothing like trying it in the real world.

Vas: Yeah, makes total sense.

If I’m an entrepreneur, you’re an elephant and I’m a mouse, and if I have to dance, I have to hope you’re a very graceful elephant because otherwise you’re gonna crush me.

Jorge: So on this question of bringing in talent, you guys operate globally. Obviously you’re in… 150 countries? You’re headquartered in Switzerland; in the Boston, Cambridge area; you have a presence out here in Silicon Valley.

So how do you guys think about innovation hubs? Very simplistically, is all of the machine learning, artificial intelligence talent gonna be based out here? How do you distribute teams across the world?

Vas: So, it’s interesting, when you look at research, we have three main hubs — our three main hubs are in: Cambridge, in Basel, Switzerland, and in Shanghai in China — those are our three main research hubs.

In terms of development centers for product development you would add onto that list Hyderabad, India as well as Maine, and East Hanover, New Jersey.

But when it comes to data science and digital what we’ve actually decided to do is to take a much more distributed approach. So we’re building up these Biome centers in San Francisco, in London, in other locations in the Middle East, perhaps in China, just trying to say, We’re not gonna constrain ourselves with our current locations.

We’re gonna just try to source talent wherever it is — particularly because talent in these areas doesn’t necessarily have to be housed next to the other functions. We’re really asking these people to explore our data and find big new insights. So that’s the approach we’re taking right now… is really saying, you know, let’s go where the talent is, as opposed to force everyone to come to us. So we’ll see; that’s the experiment we’re undertaking.

Sonal: How do you see the future of that working out? Like, do you see that Boston, Silicon Valley, Basel, these places will specialize? Will they distribute?

Vas: We have lots of debates. If we were to build a scaled hub in digital or in data science health, where would we go? I think one of the challenges in the Bay Area is, again, just the competition for talent is so intense, especially in the tech sector.

Jorge: So we’re in the business of funding early-stage companies, supporting entrepreneurs. If I’m an entrepreneur, I obviously see a ton of benefit partnering with Novartis: access to data that doesn’t exist elsewhere, obviously validation in my approach in my technology, etc.

But if I’m an entrepreneur, I’m also scared to approach a large company like a Novartis because I’d worry about — basically you’re an elephant and I’m a mouse, and if I have to dance, I have to hope you’re a very graceful elephant <laughs> because otherwise you’re gonna crush me.

What advice would you give to entrepreneurs about approaching biopharma, large biopharma, in the spirit of collaboration?

Vas: Yeah, in data and digital what we’ve tried to do is make us feel a lot smaller, because we recognize that we are a huge beast. And so with things like the Biome, we work with StartUp Health. We work with many other entities to try to say, “How can we make ourselves feel small, or work in smaller units?” We created our own digital data organization so that entrepreneurs would have an input into Novartis, where it’s people like them. The people on that team are all come from the tech sector: They’re working in a much smaller, agile way; they do sprints and scrums; and they work in all the ways that people are used to working. And I would say really engaging through some place in a large company that I think has a natural affiliation for the entrepreneur makes a lot of sense.

It is harder on the traditional biomedical side, right? If you just think of us, we have 17,000 R&D people and spend $9B+ a year in R&D. So if you’re a small entrepreneur who wants to start working with us, it’s easy to get lost in the fray. We’re trying to work on that. Most of the companies in our industry try to have external offices that try to engage. We have an External Scholars Program where we really try to enable scientists to use our facilities, interact with our scientists. So we’re trying to experiment but I can’t say we’ve completely figured that out on the biomedical side. I’m much more optimistic on the data and digital science side. Mostly because we just brought people in from that world… and they just think differently.

Sonal: There was something I wanted to ask you earlier which was about measurement.

Because when you talked about the portfolio approach, I wanted to know how you think about actually measuring the way you make those investments in a portfolio. And the reason I ask is because there’s all these mindsets: Pasteur’s Quadrant, like, here’s a place where we’re gonna put more emphasis on basic research, and we’re gonna put more emphasis on something practical; or, there’s another approach, at Xerox PARC they used a modified real options analysis as a way to figure out how to do short-term, long-term, mid-term type investments.

Do you have a way of closing the feedback loop for how you measure the success of how you’re allocating and deploying investments in R&D?

Vas: Yeah, I mean, we have financial measures, so we look at return on capital employed, MPV, MPV peak sales, all the traditional financial measures.

We look at really the scientific “innovativeness”, for lack of a better word — is this really something that’s changing the game from a scientific standpoint? — that’s a little bit more of a subjective measure, but we try to ask teams, is this really moving the needle from a standard of care, science? And we actually score that based on six different parameters that we make.

Sonal: Oh, interesting. Are you allowed to share the parameters?

Vas: I don’t know them off the top of my head. But, you know, we really try to score the medicines to say, Is this really transformative? So you have a financial score, you have a transformational score.

And then another kind of a subjective element is, does this strategically fit; so is it in one of our core therapeutic areas? So if somebody comes with a great breakthrough — which happens not quite often in an area that we’re not in — that’s the toughest one, because it’s a big breakthrough but we’re not in this space. And what do we do now? Do we really wanna build this up, or do we wanna just send it out to license, to a fund, or do something else?

Those are tough discussions, but we try to be disciplined because it’s, again, the patience in being really sure you build depth in your key areas. Because if you take another program on that means that there’s another program you have to stop. It’s a zero-sum game for us.

Sonal: Yes, it’s an opportunity cost.

Jorge: Well, one thing that’s funny, just listening to you talk about — and Sonal brought up this question of Not Invented Here syndrome — and when you contrast that with managing, having an organization that is naturally curious and unbossed, as you said.

Sonal: Inspired.

Jorge: Inspired. But managing that Not Invented Here syndrome, versus, maintaining the skepticism that things might be in a hype cycle and not chasing hype — it’s a very fine balance, right?

Vas: It is.

Jorge: It’s kind of like the not-invented-here, the other side of that coin is not invented yet.

Vas: Yes.

Jorge: And you gotta figure out where you are in that. And I think that is one of the most difficult things that an innovative company at the scale of which Novartis operates has to always find that balance. Between.

The other side of not-invented-here is not invented yet.

Vas: Absolutely. I mean, it is a balancing act between the different forces. I find a lot of it comes down to just encouraging people just to have open, frank debate. And be comfortable with task conflict without personal conflict. That’s what I keep telling our team: We have to be incredibly curious about one another, what one another thinks. I think that’s just all about trying to get the best ideas, and we’re just trying to debate, and– but it’s never personal. Because I think particularly in the world of science it often becomes personal. You know it becomes, This is about me and my science versus you not believing in my science, as opposed to saying, We need to just find a great medicine, or we need to just solve this problem. That’s a journey I think we’re taking the organization on. But I think that’s gonna be what’s really critical is having that radical transparency and the open debate.

Sonal: I find it fascinating because it alludes to the concepts around skin in the game. Because you want people to have skin in the game, but at the same time they need to have just enough out [of the game] that they can see things a little clearly where you’re not only attacking their sacred cows.

Jorge: Skin in the game, but not vital organs.

Sonal: Yah exactly that’s a great way of putting that; I love that.

Jorge: How long have you been in the CEO chair now?

Vas: Ah, one year.

Jorge: What’s the — you know, having come up through the R&D side of the organization — what’s been the most surprising thing to you now as the CEO, given that R&D is such a big part of what the company does?

Vas: I am just amazed by how vast our company is. I mean, even though I’ve been at the company since 2005, now actually overseeing a company that’s 120,000 people in 150 countries, you go anywhere and we are just a vast, vast company. So that’s one thing that’s really surprised me just to have to now — when you think about making a transformation happen and you try to make that happen in such a large enterprise — that certainly… I mean, that really hits you.

I think the other thing about this job is crisis management, which you’re just not exposed to. This job is a lot about managing crises, and that’s been a big learning curve for me. Because in the world of R&D we had clinical trials that last two or three years: everything’s sort of predictable, and we sort of know the decisions we need to make; a lot of documentation that you can lean on. Now you’re in the world of the ambiguous, the uncertain, and then things hit you completely from the blind side and you gotta keep moving ahead.

Sonal: If you were to write a letter to grad students, or just people kinda entering the space, what kind of skills would you encourage them to have? Like if you could have added things 20 years ago, what would you tell them to do?

Vas: I’d say focus a lot on how you lead people. I think there’s so much of a focus on technical expertise and thinking that that’s gonna get you there — it matters, of course, competence matters tremendously — but what really makes the difference is how you lead people, how you lead yourself. I think investing more in that would pay off a lot.

The other thing I’d say is don’t underestimate the importance of getting multidisciplinary exposure. Most people get worried when they have to make those jumps. I’ve had a career at Novartis where I’ve worked in commercial areas and marketing areas — so most of my time in R&D worked across four different areas of the business — and so with that diversity of experiences it enables you to take the right decisions.

There was one other point I wanted to raise: I think that what’s often lost on people, because you mentioned the [medical] miracles, right? <Vijay: Oh yah> How incredible it is that we find any human medicine at all, because if you think about it, every human being is probably 40 trillion cells that are working together.

Vijay: It’s amazing anything even works.

Vas: It’s amazing — but we understand a fraction of the proteins, what they do — 1,200 druggable proteins, but there’s only a fraction of those that we can actually drug. We don’t know what most of RNA does, non-coding RNA. We don’t know most of what the genome’s even talking about. And if you look at it, since the creation of the FDA, there’s only been about 1,500 new molecular entities ever found. <SC: wowww> And most of those are actually overlapping in similar therapeutic areas.

So actually if you were to count for — I haven’t done the analysis, but if you count for double counts — my guess is it’s in the hundreds of medicines that we’ve actually found…

Sonal: …And by the way, what’s the predominant therapeutic area?

Vas: Probably, I would guess hypertension cardiovascular disease but I have not looked carefully. But it’s worth reflecting on how hard it is to do what we do, and when we find– I tell our people, “You have to think every medicine we find is a miracle that fits in the, the palm of your hand.”

We’ve unlocked, in a sense, a billion years of evolution of the eukaryotic cell and human biology, and somehow we found something that was able to move the needle in this incredibly complex system. I think that’s easy to forget when we just overly simplify what we do.

Sonal: That’s a great note to end on. Vas, thank you for joining the a16z Podcast.

Vas: Thank you.

Jorge: Thanks so much.

Vijay: Yes, thank you.

How incredible it is that we find any human medicine at all! Because if you think about it, every human being is probably 40 trillion cells that are working together.