Invest Like the Best with Patrick O'Shaughnessy
Tal Zaks - Bridging Science, Medicine, and Returns - [Invest Like the Best, EP.406]
Tue, 14 Jan 2025
My guest today is Tal Zaks. Tal is a physician-scientist turned biotech executive and investor who served as Moderna's Chief Medical Officer during their COVID-19 vaccine development, giving him an extraordinary perspective on one of modern medicine's pivotal moments. His combination of medical expertise, platform innovation experience, and investing acumen allows us to explore the interconnected challenges of turning scientific breakthroughs into viable medicines while generating venture-scale returns. We dive deep into lessons from Moderna's mRNA platform, examine how emerging technologies might reshape drug development, and the fundamental question of what it means to make people healthier. For investors, entrepreneurs, and anyone interested in the future of medicine, this discussion provides a window into both the immense potential and profound challenges of advancing human health. Please enjoy my conversation with Tal Zaks. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Ramp. Ramp’s mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Ramp is the fastest-growing FinTech company in history, and it’s backed by more of my favorite past guests (at least 16 of them!) than probably any other company I’m aware of. Go to Ramp.com/invest to sign up for free and get a $250 welcome bonus. – This episode is brought to you by AlphaSense. AlphaSense has completely transformed the research process with cutting-edge AI technology and a vast collection of top-tier, reliable business content. Imagine completing your research five to ten times faster with search that delivers the most relevant results, helping you make high-conviction decisions with confidence. Invest Like the Best listeners can get a free trial now at Alpha-Sense.com/Invest and experience firsthand how AlphaSense and Tegus help you make smarter decisions faster. – This episode is brought to you by Ridgeline. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. I think this platform will become the standard for investment managers, and if you run an investing firm, I highly recommend you find time to speak with them. Head to ridgelineapps.com to learn more about the platform. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes. Follow us on Twitter: @patrick_oshag | @JoinColossus Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes: (00:00:00) Welcome to Invest Like the Best (00:08:37) State of Medicine Today (00:09:44) Investment and Innovation in Medicine (00:13:14) Challenges in Biotech Investment (00:17:18) Personalized Cancer Vaccines (00:22:58) Investing in Biotech: Process and Considerations (00:28:38) Multidisciplinary Approach in Pharma (00:41:35) COVID-19 Vaccine Development (00:46:27) Funding and Manufacturing Challenges (00:48:01) Unprecedented Vaccine Safety Measures (00:50:38) Public Perception and Trust Issues (00:53:54) Future of mRNA and Nucleic Acid Medicines (00:58:04) Personalized Medicine and Data Collection (01:04:48) AI's Role in Healthcare (01:08:34) Investment Strategies in Therapeutics (01:14:57) The Human Element in Medical Innovation (01:21:58) The Kindest Thing Anyone Has Ever Done for Tal
Chapter 2: What are the current challenges in biotech investment?
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My guest today is Tal Zaks. Tal is a physician-scientist turned biotech executive and investor who served as Moderna's chief medical officer during their COVID-19 vaccine development, giving him an extraordinary perspective on one of modern medicine's pivotal moments.
His combination of medical expertise, platform innovation experience, and investing acumen allows us to explore the interconnected challenges of turning scientific breakthroughs into viable medicines while generating venture-scale returns.
We dive deep into the lessons from Moderna's mRNA platform, examine how emerging technologies might reshape drug development, and the fundamental question of what it means to make a healthy person healthier.
For investors, entrepreneurs, and anyone interested in the future of medicine, this discussion provides a window into both the immense potential and profound challenges of advancing human health. Please enjoy my conversation with Tal Zaks.
So Tal, maybe to begin, you could give us your one minute summary of your career, just so people have context of where you're coming from in all the incredibly interesting topics that we'll get into today.
Well, in a nutshell, I'm a physician scientist who spent all of my life figuring out how to translate the wonderful innovations of science in our era into better medicines for patients. I was fortunate to have been trained by some of the best. I trained for more years than I would recommend anybody. in the sense of having done an MD, a PhD, a postdoc, a residency, a fellowship, two internships.
I mean, you get the picture. And then I finally went out to do something in the world and spent the first part of my career in drug development, mostly as an oncologist and oncology drug development. And then the second good chunk of time at Moderna as the chief medical officer, developing that as a platform as opposed to any one medicine.
And for the past several years, I've moved over to the investor side to help Aligning the translation of science into medicine with actually a return on investment of realizing how important it is to get those two in alignment to do good in the world.
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Chapter 3: How did mRNA technology change vaccine development?
Chapter 4: What is the significance of personalized cancer vaccines?
For investors, entrepreneurs, and anyone interested in the future of medicine, this discussion provides a window into both the immense potential and profound challenges of advancing human health. Please enjoy my conversation with Tal Zaks.
So Tal, maybe to begin, you could give us your one minute summary of your career, just so people have context of where you're coming from in all the incredibly interesting topics that we'll get into today.
Well, in a nutshell, I'm a physician scientist who spent all of my life figuring out how to translate the wonderful innovations of science in our era into better medicines for patients. I was fortunate to have been trained by some of the best. I trained for more years than I would recommend anybody. in the sense of having done an MD, a PhD, a postdoc, a residency, a fellowship, two internships.
I mean, you get the picture. And then I finally went out to do something in the world and spent the first part of my career in drug development, mostly as an oncologist and oncology drug development. And then the second good chunk of time at Moderna as the chief medical officer, developing that as a platform as opposed to any one medicine.
And for the past several years, I've moved over to the investor side to help Aligning the translation of science into medicine with actually a return on investment of realizing how important it is to get those two in alignment to do good in the world.
Well, your intersection there of investing and deep science and personal hands on work and as a physician is exactly the Venn diagram that I've been searching for to have this kind of conversation, which is really about the current state and potential future states of medicine therapeutics.
And the investing returns that might be earned from paying some special attention to those areas, which I think is the key distinction between the therapeutics themselves and the potential for returns. Maybe you could give us the equivalent of a state of the union on that topic of how do you see the world today of medicine versus maybe your career?
Like if you compare today's snapshot to everything you've seen across your working career, I think that would be a great place to start just to give us context of where we are today.
I think we're overall in a good place. I'm optimistic because the advances in science and technology have been so robust and amazing. Now, that is tempered, I think, by two opposing forces, if you will. The first is the translation of all that wonderful science into medicine is probably as challenging as it ever was in terms of the unpredictability of what makes a good medicine.
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Chapter 5: How does AI influence drug discovery and development?
In that context, at least as far as a randomized phase two goes, when you give a personalized vaccine to those patients, it cuts the recurrence rate of cancer by about half, by about 50%, which is quite significant.
So going back to this, what sounds like a or the key bottleneck, which is that we just don't know how to predict whether or not something will work. Can you break that down further?
Because I'm trying to wonder if there's a version of the future where it does become easier to simulate, model, predict more like an engineering challenge versus a complex biological system challenge that we can't predict. Practically or theoretically, what is in the way of us predicting things better and therefore drastically improving the efficiency of our efforts?
So I'd break it down to three phases. And I do think that the world is improving. The first phase is, is this target for intervention actually relevant to the disease? That's basic biology. And I think we're making great strides there in understanding biological processes. Some of it is big data. Some of it is just old school grunt work.
But there's a lot of tools that have been developed that are being deployed that are making this much more accessible. to us understanding disease better. The second has to do with what's called drug discovery. Okay, so I've got a protein whose function I want to alter. What is the ability to actually discover a new chemical entity or a new protein entity or nucleic acid entity that will actually
interfere there, that will actually do the pharmacological effect. And there, I think you're seeing a very significant deployment of these modern AI tools across the industry now.
And it's very quickly becoming, to a certain degree, commoditized with the advancement of alpha fold predicting protein structures and people applying the same kind of tools into chemical discovery space to come up with new chemistries.
I've seen people even apply these tools into figuring out these lipid nanoparticles that will shepherd mRNA into different tissues to come up with better formulations and ways of bringing that medicine into the right places in the body. So I think drug discovery is getting a leg up and a significant one from the various applications of AI tools.
The part where we're still behind is in what's called development or clinical development, putting it in people. There's no shortcut here. We don't have a holistic model of a human being. We have made progress in understanding what natural outcomes are for people with high quality, big data sets that you can apply machine learning tools.
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Chapter 6: What investment strategies are effective in therapeutics?
So I joined one of the best VC firms I could find, Orbimed, because they have a track record of actually being successful. And I wanted to learn the answer to that question. And luck certainly plays a role. But here's a few thoughts. that I think are not as obvious. We all recognize the importance of learning from failure.
And I listened to your podcast with Jared Kushner the other day, his version of what is God trying to teach me, and I thought that was extremely well put. For many, many years, my favorite quote of all times was Nelson Mandela's who said, in life, I've either succeeded or I've learned. And coming out of the COVID success, it actually made me realize
That's a misguided quote because it suggests that you haven't learned from your successes as much. And I would argue that the best investors actually learn more from the successes than the failures. And that's not trivial. When you fail, there's a whole bunch of things you can point at as causal elements. But when you succeed, as you say, What was it? Was it luck? Was it talent?
Was it getting the right people around the table? It's a combination of factors. And I think the good investors develop this sense of pattern recognition of what works. And as I've tried to uncover it for myself, frankly, out of my own curiosity, I think in the domain in which I function, which is venture capital, and it's very different from private equity or some other corners of investors, but
For venture capital and specifically for what I call collaborative venture capital, which is the type of venture capital we have, which is to say that, yeah, we will often seed companies, but we very quickly look to syndicate deals. We look to work with other investors. We look to broaden the investor base, but also broaden the competencies we have around the board.
For me, the answer has been really a combination of talent of people and the content that we believe has a leg to stand on. And What it means, and when I call ourselves collaborative venture capitalists, it's because, at least for me, it's always critical to look at the talent that comes together as much as it is the content.
Yes, I get excited by the science of what they're trying to solve, but I have to get excited about the people who have the experience and the wisdom to navigate and understand what it takes. Because as that book points out, and you correctly point out, it's almost never a straight line.
And if it's not a straight line, it means that you need people around the table who have the experience and ability to look around corners. And it means that you need enough of a capital and strategy structure to give you some degrees of freedom of movement. I can tell you early on in my career as an investor, I made an investment in a small company. It was a very rational thought.
It was for a certain idea of a drug that would have a certain effect against cancer. And it was a small team and it was very linear. And it didn't pan out.
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Chapter 7: What lessons were learned from the COVID-19 vaccine rollout?
Chapter 8: What is the future of mRNA and nucleic acid medicines?
People have been trying to do cancer vaccines. I've been trying to do cancer vaccines since I was a postdoc, and so far we haven't. I think this one has a chance of working, and here's why. But until we test it in the clinic, we won't know. And we've been fortunate that that phase two actually read out with quite a successful readout last year.
In fact, the phase three now is enrolling, and hopefully we'll have a personalized cancer vaccine on the market before too soon.
Sorry. So just so I understand that one, which sounds incredibly exciting, I could get tested blood work or something else or genome or something, some combination of things that's unique to me. And then a vaccine would be created to reduce the odds or eliminate the odds that I would get the cancers I'm most prone to get.
So that's still in the future. The way this works is people with early stage cancer, specifically, we started with skin cancer. So cancer has been diagnosed, but it's early. Likely a surgeon will cut it out. And then there's some probability that it will come back. And what can we do to improve the odds that it won't come back?
I see.
In that context, at least as far as a randomized phase two goes, when you give a personalized vaccine to those patients, it cuts the recurrence rate of cancer by about half, by about 50%, which is quite significant.
So going back to this, what sounds like a or the key bottleneck, which is that we just don't know how to predict whether or not something will work. Can you break that down further?
Because I'm trying to wonder if there's a version of the future where it does become easier to simulate, model, predict more like an engineering challenge versus a complex biological system challenge that we can't predict. Practically or theoretically, what is in the way of us predicting things better and therefore drastically improving the efficiency of our efforts?
So I'd break it down to three phases. And I do think that the world is improving. The first phase is, is this target for intervention actually relevant to the disease? That's basic biology. And I think we're making great strides there in understanding biological processes. Some of it is big data. Some of it is just old school grunt work.
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