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This episode is unusual.
Many listeners know of Rob Perez. He’s an operating partner at General Atlantic, the former CEO of Cubist Pharmaceuticals, and the founder of Life Science Cares.
Rob and I have gotten to know each other better the past couple years through our shared passion for mobilizing the biotech community to fight poverty. The Timmerman Traverse for Life Science Cares campaigns have raised $2.9 million over the past three years.
Longtime listeners may recall Rob was a guest on this show five years ago, and he spoke about Life Science Cares then. This time around, Rob wanted to turn the tables. He asked the questions, and I was the guest.
We discuss how I grew up on a small family farm in southwestern Wisconsin, some early career influences in newspapers, and how I adapted to the market forces that upended journalism in the 21st century. Those experiences all combined to lay the foundation for this new chapter as both journalist and social entrepreneur.
Now, before we get started, a word from the sponsors.
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Look forward to face-to-face interactions this October at the BIO Investor Forum—or BIF—in San Francisco.
Accessible and intimate, this conference is designed to accelerate growth within the biopharma industry. BIO’s One-on-one partnering system seamlessly brings together emerging biotech companies with industry partners, investors, and bankers.
Don’t miss your chance to network, learn from experts about what’s on the horizon for biotech, and pitch your unique story to potential partners. Held October 17th and 18th at the Hilton San Francisco Union Square.
Now, please join me and Rob Perez on The Long Run.
Two recent Wall Street Journal deep-dives nicely bookend a critical, and unresolved, tension faced by large pharmaceutical companies: how can their R&D organizations discover, develop, and deliver the new medicines patients await, and the growth and return on investment that shareholders demand?
Early this year, I discussed an April 2023 profile of Lilly by journalist Peter Loftus, who described how the company, led by a physician-scientist named Daniel Skovronsky, revitalized Lilly’s R&D, after a decade of documented struggles.
Skovronsky’s pivotal insight, as I wrote, was “recognizing that key decisions were being driven by commercial needs, rather than the best science. Marginal products were advanced (only to later fail) because they targeted a specific commercial need.”
Poor decision making driven by non-scientific considerations – particularly commercial desiderata—is a frequently-encountered challenge in drug development, as VC David Grainger, in particular, has highlighted (see here).
But other large pharma companies (I suspect many if not most large pharma companies) are concerned that they’ve provided excessive latitude to their R&D organizations, rather than insisting on a greater commercial focus.
For instance, last week, Journal reporter Jared Hopkins described how Novartis’s CEO had decided the company’s early efforts were excessively driven by science, and needed a greater emphasis on commercial prospects. Consequently, future sales will now be forecast for products before they enter clinical trials, Hopkins wrote.
Associated with this change in emphasis, the early research arm of Novartis, the Novartis Institutes for BioMedical Research (NIBR), “will soon simply be known as Novartis Biomedical Research,” according to STAT.
The new Novartis mindset contrasts, directly, with the approach instituted by former NIBR head Mark Fishman, who deliberately excluded such forecasts from early-stage decision-making (as I discussed a decade ago in Forbes). A decade later, Novartis brass clearly became concerned that NIBR had become “too academic” – pursuing interesting questions without sufficient regard for commercial applications.
While appealing in theory, Novartis’s proposed remedy – essentially, use early commercial forecasting to “pick winners” – makes sense only if you have the actual ability to do this. Otherwise, you have just an exercise in false, if comforting, precision.
A classic study by BCG found that there was essentially no correlation between predicted peak sales for a drug at the time it was approved (i.e. with complete phase 3 data) and actual peak sales. Several years later, as I discussed in Forbes, McKinsey confirmed the fragility of sales forecasts. Consequently, the notion that one can predict sales before human studies in any meaningful way is purely wishful thinking – the sort of “sterile information” (to use Nassim Taleb’s term) that corporate planners typically love, but which is functionally worthless.
As Taleb testified before the Financial Services subcommittee of the U.S. House of Representatives in 2014,
“Some may use the argument about predicting risks equal or better than nothing; using arguments like ‘we are aware of the limits.’ Risk measurement and prediction —any prediction — has side effects of increasing risk-taking, even by those who know that they are not reliable. We have ample evidence of so called ‘anchoring’ in the calibration of decisions. Information, even when it is known to be sterile, increases overconfidence.”
How to pick projects, then?
One view – as analyst Jack Scannell has argued, as I discussed here — is to prioritize areas with good translational models, as slightly better models contribute more impactfully to the odds of success than increasing by several orders of magnitude the number of molecules screened (which is the canonical large pharma approach). In fact, Scannell describes this excessive reliance on scale vs smarts as the “mis-industrialization” of science, and this description seems spot on. (Taleb and I made a similar point in a 2008 commentary in the Financial Times, here.)
Focusing on areas with good translational models is explicitly the approach Vertex is taking, and arguably Regeneron as well. Both Vertex and Regeneron are compelling (and remarkably rare) examples of established drug discovery companies that are still dominated by their powerful R&D cultures; more commonly, the business of running a drug development company is considered too complex to be left to the scientists (in the same way that running a hospital is increasingly considered too complex to be left in the hands of doctors, rather than corporate managers). It’s also true that Vertex and Regeneron are both comparatively small; Regeneron, the larger of the two, employs about only about a tenth the number of people that Novartis does, for example.
Without question, there are many areas of biopharma that arguably benefit from the rigor of traditional corporate industrialization – in particular, the focus on consistent, repeatable processes that operate on a global scale. Manufacturing, engagement with regulators, sales and marketing, even late phase clinical trials largely fall into this category, and tend to be areas that big pharmas do particularly well, and with which corporate executives are fairly comfortable.
But coming up with new products? Not so much.
While pharma companies aspire to develop new products with a range of defined characteristics – the so-called “target product profile” – you generally can’t discover on command.
Moreover, many commercially successful products were developed without any initial interest or support from commercial teams. Merck (see here) was days away from out-licensing pembrolizumab (Keytruda), a drug that had been kept alive by researchers despite the best efforts of successive management to stop the program. Similarly, when Millenium acquired Leukosite, they had no idea that the transaction included a molecule, bortezomib, that would become the most important commercial product in their portfolio.
Big pharmas are built around establishing repeatable processes…but there simply isn’t a playbook for serial creative success.”
Big pharma brings considerable resources to discovery, as well as an extremely high degree of rigor, and often an exceptional amount of implicit knowledge, particular in areas like medicinal chemistry. But large pharma (as Safi Bahcall has nicely described – see here and here) also tend to be exceptionally bureaucratic and process-driven, and I’m not optimistic you can process and administrate your way to creativity and insight. I’d argue the process-driven mindset that serve other aspects of drug development and delivery so effectively (enabling the distribution of safe and effective medicines to patients around the world) have, on balance, an adverse impact on novel discovery. Moreover, for all the efforts of big pharma to enable research groups that are more “biotech-like,” the incentives and cultures of most large corporations just doesn’t seem to allow for this, in practice.
This isn’t an argument for aimless discovery research. Rather, it seems inevitable that an ever-greater number of future first-in-class medicines will come from the focused and high-performing teams in the start-up world. Big pharmas are built around establishing repeatable processes, and driving incremental refinements. But original discovery – to the dismay of most large pharma – often isn’t about turning the crank faster.
Rather, originality in drug discovery, as in other areas, requires, in addition to an intended destination, a high degree of agility, freedom, and imagination; the capacity to embrace and live with uncertainty, rather than obscure it with sterile forecasts; and the humility and ambition to imagine a future different from and beyond what we might anticipate, and extrapolate to, today. Perhaps most difficult for large, process-driven corporations, it requires a recognition that there simply isn’t a playbook for serial creative success. These are principles that exceptional leaders like Ed Catmull, the CEO of Pixar in its heyday, have intuited, and that most large corporate cultures struggle to sustain.
Recent Astounding HealthTech columns on Pharma R&D
Today there are two guests on The Long Run: Zach Weinberg and Alexis Borisy.
They are co-founders of Curie.Bio.
Curie is a venture capital fund with $520 million, mostly for seed investments and Series A rounds in biotech startups. It also is also building up in-house R&D expertise which it uses to help the entrepreneurs it backs.
Curie pitches itself as different from other venture firms partly because it allows the CEO/founders it backs to hold onto a greater percentage of ownership than traditional VCs have been willing to hand over. Curie also says it wants to allow entrepreneurs to retain more control over decision-making. This boils down to a battle cry of ‘Free the Founders.’
They are tapping into the zeitgeist. Many biotech entrepreneurs feel they sweat bullets for years, shouldering too much of the hard work and risk, without reaping enough of the rewards.
It’s still early days for Curie Bio, but this is conversation worth having about the terms of engagement in biotech startups.
Now, before we get started, a word from the sponsors.
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One of the most respected, independent investor events is returning to San Francisco! In person for the first time since 2019, the BIO Investor Forum will be held on October 17th and 18th at the Hilton San Francisco Union Square. The conference showcases drug development programs that are ready for partnering or venture funding.
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Now, please join me and Zach Weinberg and Alexis Borisy on The Long Run.
Welcome to the next big biotech expedition — Timmerman Traverse for Damon Runyon Cancer Research Foundation.
A team of executives, investors, and scientists are coming together on Kilimanjaro, the highest peak in Africa, in February 2024.
We’re on a mission to raise $1 million for young scientists across the US.
Damon Runyon gives promising young researchers the freedom to pursue brave and bold ideas. Its grant recipients have opened up entire new fields with targeted therapies, CRISPR gene editing, and cancer immunotherapy.
The biotech community knows the value of this work and is stepping up to support it.
I’m happy to announce that PPD Biotech Solutions, part of Thermo Fisher Scientific, is our lead sponsor.
Corporate sponsorship opportunities are available for this special expedition for cancer research. See Elyse Hoffmann, senior director of partnership strategy at Damon Runyon, to brainstorm ideas. email@example.com.
We’re pushing ourselves to do something difficult. We’ll enjoy Mother Nature. We’ll form meaningful relationships on the trails.
We’re going to make a positive impact for young scientists.
Thank you for your support!
Today’s guest is Fred Appelbaum.
Fred is a physician, scientist, and administrator. He’s an executive vice president at the Fred Hutchinson Cancer Center in Seattle.
He’s also the author of a new book, “Living Medicine: Don Thomas, Marrow Transplantation and the Cell Therapy Revolution” published by Mayo Clinic Press. It’s excellent.
Fred knows a lot of this story from firsthand experience.
He has spent his career conducting research and treating patients with leukemias, lymphomas, and other cancers of the blood. He’s a pioneer in the field of bone marrow transplantation and was the lead author of a 1978 paper in the journal Blood that heralded the first successful engraftment of autologous bone marrow in patients with malignant lymphoma.
One of Fred’s key influences was E. Donnall Thomas. Don Thomas won the Nobel Prize in 1990 for the discoveries that paved the way for bone marrow transplantation to become a common, and lifesaving procedure, for people with blood malignancies and more. Thomas died in 2012.
There’s a story to tell here about Don Thomas.
In this conversation, Fred discusses the book, the researching and writing, and a few things he learned.
Now, please join me and Fred Appelbaum on The Long Run.
Today’s guest on The Long Run is Colin Hill. He’s the co-founder and CEO of Aitia (pronounced Ay-tee-ah).
The company was founded in 2000, and previously known as GNS Healthcare. The GNS part was short for Gene Network Sciences, which gives you some sense of what it was about.
Aitia is a new name to reflect a new strategy. The company has undergone a shift in the past year to focus on drug discovery and development of its own novel medicines. Aitia is seeking to leverage deep wells of genomic, proteomic and other comprehensive ‘omic datasets. When the data can be extracted from human samples, it creates what Aitia calls a ‘digital twin’. It believes this type of data will shed light on the complex networks of human biology that sometimes go awry and lead to disease.
For many years, Colin and colleagues worked with partners – both large pharma companies and with healthcare payers – that sought to discover some useful insights in those large datasets. It wasn’t seeking to discover drugs on its own, move them along in early development, and create value that way.
Colin came to this work with a background in math and physics, first at Virginia Tech and then at Cornell University. He took the entrepreneurial leap a little over 20 years ago, at a time when the genomics boom and the first Internet dotcom boom were on. He’s seen fluctuations in the hype cycle and found ways to adapt the company so it could keep going.
Over time, Colin and the Aitia team obtained access to more datasets and kept honing causal AI algorithms – which seek to predict disease and tell us what’s going wrong mechanistically that is causing disease.
The proof, like everything in biotech, will be in the clinical data. But it has secured drug discovery partnerships this year with UCB and a second partnership with Servier.
Now, before we get started, a word about Timmerman Report.
If you like listening to The Long Run, you’ll love a subscription to Timmerman Report. This is where you can read my in-depth reports on the most interesting startups in biotech, my regular Friday Frontpoints column that summarizes the issues of the week, plus insightful coverage of current topics in biotech from a rotating cast of contributing writers. Individual subscriptions are available on a monthly, quarterly, or annual basis. Group subscriptions provide a license to companies that have more than one reader. Go to TimmermanReport.com and click on ‘Subscribe’ for more.
Now, please join me and Colin Hill on The Long Run.
As drug developers consider how to leverage AI and other emerging digital and data technologies, they look to related businesses, such as healthcare systems, for lessons and learning.
We would be hard-pressed to find a better guide to AI in healthcare than Ziad Obermeyer, an emergency room physician and health science researcher at the University of California-Berkeley. His research focuses on decision-making in medicine and on the equitable use of AI in healthcare.
Obermeyer was recently interviewed on the NEJM-AI podcast by co-hosts Andrew Beam and Raj Manrai (both faculty in the Department of Biomedical Information at Harvard). The entire conversation — reflective, nuanced, and chock full of insights — should be required listening for anyone interested in potential applications of AI in biopharma. The most relevant highlights for biotech are summarized below.
Obermeyer didn’t set out to become a doctor. He initially studied history and philosophy, then did a stint as a management consultant before eventually applying to med school. He hit his stride once he began his residency in emergency medicine.
“I really, really liked being a doctor,” he says, “and I think there’s something about that exposure to the real world and the problems of patients that I think it’s shaped the problems that I work on in my research as well.”
Obermeyer began to see medicine as “a series of prediction problems,” and saw artificial intelligence (specifically, machine learning) as a tool that could help make doctors better by assisting them with challenges like establishing a diagnosis, assessing risk, or providing an accurate prognosis.
If you walk into a doctor’s office today, he readily acknowledges, you’re not overwhelmed by a sense of futuristic technology as you fill out paperwork and listen to the fax machine hum.
However, he notes, AI is already widely used in medicine – it’s just operating at the back end, behind the scenes. “On the population health management side, on a lot of other operational sides, like clinic bookings, things that have a direct impact on health, these tools are already in very, very wide use.”
Medicine today is still quite artisanal, guided by rules of thumb and local traditions, Obermayer says. Much of the problem, he suggests, is that it’s hard for doctors to wrap their heads around the volume and variety of healthcare data, which are “high-dimensional” and “super complicated.” To make the best possible predictions given the number of variables, he argues, requires assistance through approaches such as machine learning.
The question isn’t how we think about AI plus medicine; rather, he says, “that is medicine. That is the thing that medicine will be as a science.” This perspective is shared by others in the field including Harvard’s Zak Kohane, who often asserts “medicine is at its core an information- and knowledge-processing discipline,” and progress requires “tools and methods in data science.”
Casting his eye towards the future of AI in medicine, Obermeyer can envision both bear and bull scenarios.
His fear is that AI tools, in addition to harboring biases (see below), will be used for “local optimization of a system that sucks and that isn’t proactive, that’s very oriented towards billing and coding.” He can envision “a very unappealing path where we just get a hyper-optimized version of our current [suboptimal] system.”
”There is a certain lack of ambition in how people are applying AI today,” he said.
More hopefully, he can imagine a future where AI helps solves some of healthcare’s most vexing problems. One opportunity area he sees are addressing conspicuous “misallocation of resources” – essentially, improving our ability to provide the right treatment for the right patient at the right time.
For example, Obermeyer points out that many patients die of sudden cardiac arrest, while at the same time, the majority of defibrillators implanted to prevent sudden cardiac deaths are never triggered. It would be far better medicine, he observes, if more defibrillators were implanted in the patients who would ultimately need them.
He also envisions how AI might enable new discoveries around the pathophysiology of disease by linking biological understanding, biomarkers, and outcomes. A squiggle on an ECG isn’t just an image that an AI can recognize, like a cat on the internet. “We actually know a lot about how the heart produces the ECG,” he explains. “We know what part of the heart leads to what part of the wave form. We have simulation models of the heart that we can get to produce waveforms.”
Consequently, he views the idea of “tying together that pipeline of biological understanding of the heart and how the heart generates data,” and connecting data about patient outcomes is “super-promising,” and suggests it may eventually lead to new drug discoveries. “There are a lot of things you can do once you get the data talking to the biology,” he says.
The exceptional promise of applying AI in medicine seemed to be matched only by the challenge of implementing it.
Obermeyer described hurdles in four key areas: data, talent, bias, execution.
Data. AI depends on data as its foundation. This can be a particular problem in healthcare Obermeyer says, noting that “getting the data that you need to do your research is a huge, huge preoccupation of any researcher in this area.” The problem, he continues, “is that the data are essentially locked up inside of the health systems that produced the data. And it can be really perverse… it’s Byzantine and it’s very frustrating, and I think it’s really holding back this space.”
Obermayer established an open-science platform (Nightingale) to make it easier for researchers to get access to datasets from healthcare systems. One example: the team digitized breast cancer biopsy slides that “were literally collecting dust on a shelf in the basement” of a hospital and linked these data to EHR information and cancer registry data.
Getting started wasn’t easy. He approached 200 healthcare systems, he said, and only five agreed to participate: several large non-academic health systems and a few small county hospital systems.
Obermeyer has also set up a for-profit company, Dandelion Health, that aspires to serve as a trusted data broker to make it easier for healthcare AI tool developers to think about their creative applications, rather than spending too much time wrestling to get access to the data in the first place. “There are so many insights and products that could directly benefit patients that are not getting developed today because it’s so hard to access those data,” he says.
Talent. Obermeyer sees healthcare systems as operating at a disadvantage in the digital and data world. “Hospitals can’t hire all the computer scientists that they would need” to do the necessary data science,” he says, “and they’re not going to win the war for talent against Google or Facebook or even just computer science departments of different universities.”
Obermeyer also doesn’t feel that it’s feasible to pair a healthcare expert and an AI expert; he believes it far better to have a “single brain,” even though he acknowledges this “seems ridiculously inefficient” because of the time and effort required to gain this kind of medical and data science expertise. (See here for a contrasting perspective from Dr. Amy Abernethy, championing the collaboration approach.)
The good news, though, is that Obermeyer shares the optimism of venture capitalist Bill Gurley that there’s a huge amount of useful, free information available online, and motivated individuals can find a lot of the training they need; this seems to be how Obermeyer himself became proficient in artificial intelligence.
Obermeyer suggests two conceptual paths for healthcare experts interested in mastering AI. One, he says, starts with statistics, since he (somewhat controversially) regards AI as “an applied version of statistics with real datasets.” In his view, there’s “no substitute for learning the basic statistical stuff. And I think as a starting point, that is an amazing place to start to get a handle on thinking about how AI works, where to apply it, where it can go wrong”
The second route into AI, Obermeyer says, and the one he took, begins with the microeconomics “toolkit,” which he argues was designed “for dealing with data that’s produced by humans and is messy and error prone and driven by incentives. That seems a lot like medicine to me.”
Obermeyer sees the ultimate goal of data science training as learning how to formulate problems effectively – “how to take an abstract question and then think about what is the data frame that would answer this question.”
Obermeyer also points to how helpful AI itself can be to trainees. ChatGPT is particularly helpful in writing code, he says, and approvingly cites AI expert Andrej Karpathy’s quip, “The hottest new programming language is English.”
Obermeyer sees the ultimate goal of data science training as learning how to formulate problems effectively – “how to take an abstract question and then think about what is the data frame that would answer this question.”
Bias. Obermeyer’s research is focused on bias and AI; he seeks to root out hidden bias, as well as to use AI to reduce bias.
Obermeyer is especially well known for a 2019 Science paper that identified an unexpected bias in a population health algorithm. The tool he studied looked at health data from a population and tried to predict which patients were most likely to get sick in the upcoming year, so they could receive extra attention, pre-emptively, and thus stay healthier.
When Obermeyer’s team looked at how this worked in practice, the team found that Black patients predicted to be at same health risk as White patients were far more likely to get sick.
As the authors explain, “The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients.” In other words, by using health care costs as a proxy for health care needs – a common assumption of convenience — the algorithm developers inadvertently overlooked, and ultimately propagated, an underlying bias.
Obermeyer has also explored how AI might be used to reduce healthcare disparities. He explains that the project was inspired by a talk from a friend and colleague, Harvard healthcare economist David Cutler, on the consistent observation that “black patients have more pain than white patients…even when you control for severity of disease.”
For example, if you consider patients with equally severe knee arthritis, based on standard X-ray scoring, Black patients on average will report more pain than White patients. Cutler attributed this gap, Obermeyer says, to “stuff that’s going on outside knee” – psychosocial stressors, for instance. But Obermeyer thought the issue was something in the knee, and together they decided to study the problem.
Obermeyer’s team trained a deep learning algorithm to predict patient’s pain level – rather than a radiologist’s arthritis score – from the X-rays. “This approach,” the authors report, “dramatically reduces unexplained racial disparities in pain.”
According to Obermeyer, “the algorithm is doing a much, much better job of explaining pain overall, but it’s doing a particularly good job of explaining the particular pain that radiologists miss and that black patients report, but that can be traced back to some pixels in the knee.”
Execution. Motivated by both his sense of purpose and innate curiosity, Obermeyer was clearly frustrated by the slow pace of some of the research in academia, in contrast to an urgency he noticed from colleagues from industry.
“One of the things that I’ve really come to appreciate about the private sector and basically my new non-academic friends and acquaintances,” he said, “is, boy, do they get [stuff] done.”
“They don’t have projects like I have that have gone on for eight years. If it goes on for eight days, it’s like, what’s going on? What’s taking so long? So there’s an impatience and a raw competence that I’ve been trying to learn from that world.”
Obermeyer’s experience can’t help but resonate with drug developers. Ours, too, is a business focused on “a series of prediction problems.” Our work tends to leverage digital and data technology far less than many other industries, yet (as I’ve discussed) there are pockets (such as in manufacturing and supply chain management) where there is a remarkable level of sophistication. There would seem to be a profound opportunity for drug developers to make better use of multi-dimensional data. There is already a strong focus on applying emerging technology to improve near-term efficiencies, and the earnest hope these technologies also can be used to identify and elucidate profound scientific opportunities to improve human health. Access to high quality data remains a crippling problem for industry data scientists focused on R&D. Great talent is always in demand, and upskilling employees is an industry priority, while figuring out how to integrate most effectively talented drug developers with skilled data scientists remains a work in progress. Bias is, of course, an area of exceptional concern and focus, and the notion of using technology to promote equity is particularly appealing. Finally, the ability of industry to execute when inspired reminds us of what we can achieve, while the relatively limited impact of AI and data science in R&D across the industry to date, particularly when contrasted with the outsized potential, reminds us of how far we still have to go.
Recent Astounding HealthTech columns on Gen AI
Today’s guest on The Long Run is Yung Lie.
Yung is the president and CEO of the Damon Runyon Cancer Research Foundation. The New York-based foundation supports some of the best young scientists around the US and gives them funds to pursue their bold and brave ideas.
To give just one example, it bet on cancer immunotherapy research when it was considered a fringe concept, years before it became a mainstay part of everyday treatment.
Over its more than 75-year history, Damon Runyon has invested more than $430 million in almost 4,000 scientists. Thirteen have gone on to win the Nobel Prize, and 97 went on to be elected by peers into the National Academy of Sciences.
Yung is a scientist by training herself and was a recipient of one of those prestigious Damon Runyon Fellowships when she was a postdoc. She eventually joined the organization full-time, and worked her way up until becoming president and CEO in 2018.
I am particularly interested in Damon Runyon, as I have started doing volunteer work for the organization this summer. I’m recruiting a team of biotech executives and investors for the Timmerman Traverse for Damon Runyon in February 2024 on Mt. Kilimanjaro. Our goal is to raise $1 million.
I’m committing because I believe in the organization’s mission and am impressed with its ability to execute on a national scale. You’ll hear more about this expedition in the months ahead on Timmerman Report, but if you are interested in joining the Kilimanjaro team, or sponsoring the team, email me at firstname.lastname@example.org.
In this conversation, Yung talks about the philosophy of the organization, how it got started, its accomplishments, and a few challenges it sees for young scientists.
And now for a word from the sponsor of The Long Run
Occam Global is an international professional services firm focusing on executive recruitment, organizational development and board construction. The firm’s clientele emphasize intensely purposeful and broadly accomplished entrepreneurs and visionary investors in the Life Sciences. Occam Global augments such extraordinary and committed individuals in building high performing executive teams and assembling appropriate governance structures. Occam serves such opportune sectors as gene/cell therapy, neuroscience, gene editing, the intersection of AI and Machine Learning and drug discovery and development.
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Now, please join me and Yung Lie on The Long Run.