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[This is an excerpt from The Biotech Leader’s Handbook by Angelos Georgakis.–LT.]
The founder of Vertex Pharmaceuticals, Joshua Boger, once said, “Drug discovery is an insanely complicated activity; what makes a great leader in our industry is the ability to hold a team together for a very long time.”
So, how can a leader in our industry hold a team together for a long time?
Here are 11 strategies for you to consider.
A biotech team is a bunch of brilliant PhDs, postdocs, and scientists. These folks are hustlers by nature, but even with them, you still have to press the right buttons.
Smart people don’t respond well to being told what to do. What you do is, hopefully, you inspire them to want to take action. And this is the difference between leaders and managers; managers tell people what to do whereas leaders inspire them to do it.
Daniel Pink in his book Drive mentions autonomy as one of the three things that motivates people among purpose and mastery: “People need autonomy over task (what they do), time (when they do it), team (who they do it with), and technique (how they do it).”
Take the time to leave the lab, the clinic, the office and meet with the patients of the disease you’re trying to treat. It will give you so much energy and focus.
The former CEO of Novo Nordisk, Lans Sorensen had all the employees meet with patients and understand what their lives were like and how the company’s products were transformative.
Stars won’t work for any company; they want to work for a company that sets out to achieve the impossible, i.e. a new modality, a new class of medicines. Attracting and retaining talent always starts with a bold vision.
The daily grind can consume you. You need a spiritual practice as a team.
Spend five minutes every week after an all-hands meeting reflecting on your why. Have different people talk about what the vision means to them. This spiritual practice will save you when you find yourselves at crossroads.
George Yancopoulos, the Founder of Regeneron, once said:
“What most companies do is… at a very early stage when they really think they’re onto something, they go to Pfizer or Amgen or Merck and they sell out, and the company is gone!
They get what seems to be a lot of money upfront, let’s say $100m, the couple of guys who started the company make a decent amount of money… but the company is done! It’s then all up to the big company, but what happens is ultimately most of these projects die in the big company…”
Teams often get divided in the face of critical decisions like the ones Yancopoulos is talking about above because the vision is not iterated enough.
Back in 1995, Microsoft paid writers big bucks to write Encarta, an encyclopedia it sold on CD and as software. Ten years later, they had to close it down defeated by a competitor that paid no one and offered its encyclopedia for free: Wikipedia!
External incentives can’t inspire people to care. As Wharton Professor Russell Ackoff said, “Money to a company is like oxygen to a human being. If you don’t have enough, you have a problem. But if you think life is about breathing, you’re missing the point”.
The other problem with incentives is that they’re easy to match, so they don’t give your company a competitive edge for recruiting, retaining, and engaging top talent.
Be generous and reward your team for outstanding performance. But traditional “if-then” carrot type of rewards kill your team’s intrinsic motivation. Don’t waste those infinite reservoirs of energy waiting to be deployed to your bold vision.
How can you motivate them if you don’t know what motivates each one of them? Talk to them. 1-on-1. Soul-to-soul.
Tell them, “What is your personal vision? What are your aspirations and dreams? How can we help you achieve those dreams?”
Your job is to align what your company’s trying to accomplish with what they’re trying to accomplish. If you can match their unique skillset, intelligence, passion, and aspirations with your vision, you have succeeded.
And remember that people just want to talk to you to tell you how good they feel or how challenging times seem. If you have enough time for your people and you show you care about them, they pay it back 10x.
Your team needs to be invested in a decision, otherwise, their execution will be half-hearted or won’t even happen. You create buy-in when you make people feel that they’re part of the decision. Not all perspectives can win but all perspectives must be considered to create buy-in. People want to feel heard. This applies to decisions, company values, and vision.
Steve Jobs used to say about his mentor and coach Bill Campbell, “Bill can get A performances out of B players”. But to achieve that, you first have to believe that a “B player” is capable of achieving A performances.
You have to believe that the person you thought of as a B player can be an A player. What would happen to their performance if you truly believed they’re an A player? How would you treat them differently? How would they treat you and the task differently?
Tell people how amazing they are; knowing is not enough. Add a weekly “love the team” block in your calendar. Write or talk to them one by one, not a “Hey thank you for your hard work everyone” in Slack!
When you catch yourself feeling grateful about someone or something they’ve done, let them know. When you hear something nice said about someone, let them know. And be specific: “Jen, I appreciate you for updating those process documents”.
Don’t try to retain your stars by locking the door. Help them learn and grow, rotate them, invest generously in their training without any expectation. If they leave you, they’ll tell everyone in the tiny biotech world how great you are.
They may have to go somewhere else to get the necessary skills and come back when the time is right to contribute 10x towards your vision. And remember… just like your vision and your culture, your alumni will help you win the long war for talent.
Last, and most important…
You don’t have to always show up confident and powerful in front of your team. You shouldn’t. You must not!
Motivation is like a feeling; it comes and goes. This is where the leaders who want to do a great job fail: expectations.
There is no way you and your team can feel motivated 24/7. Feeling disappointed because you didn’t get the expected data? Are you worried about fundraising? You’re still a great leader…
Don’t spend infinite amounts of energy to hide those emotions from your team and show up emotionless. Because you make them more anxious when they can see a gap between what you feel inside and what you project outside. The dissonance is what makes people feel unsafe.
Slow down to acknowledge those feelings. You must let those feelings out first before you can create space for motivation.
“Disappointing data, I know. Feeling like s*** today like everyone else. BUT I’m confident we’ll figure this out.”
All your team needs to feel pumped again is that “we’ll figure this out.”
Take a moment as a team to grieve the data you didn’t get, accept, and then move onto action.
Loving you, Angelos.
You can get a copy of The Biotech Leader’s Handbook on Amazon.
I’m excited to announce “Bridging the Gap.” It’s an event for the biotech innovation community Jan. 23 in Cambridge, Mass.
An outstanding group of speakers is coming together to support a new generation of scientists through the Damon Runyon Cancer Research Foundation.
Academics: Please apply for a sponsored free ticket here.
Noon-12:45 pm Registration/networking. Lunch provided.
12:45-1:30 pm. Fireside chat. Supporting the next generation of scientists
Phil Sharp, Institute Professor, MIT; Nobel Laureate
Vicki Sato, Board Chair, Denali Therapeutics, VIR Biotechnology; Former Harvard Business School professor
Luke Timmerman, founder and editor, Timmerman Report (moderator)
1:30-2:10 pm Rising Innovator Flash Talks
Nabiha Saklayen, CEO, Cellino Bio
Omar Abudayyeh and Jonathan Gootenberg, McGovern Institute Fellows, MIT
Ester Calvo Fernandez, PhD candidate, Califano Lab
Basem Al Shayeb, CSO, Amber Bio
2:10-2:40 pm Rising Innovator Q&A moderated by
Ann Dewitt, General Partner, The Engine
2:40-3:10 pm BREAK. Coffee.
3:10-3:15 pm Nucleate Overview
Soufiane Aboulhouda. Co-founder of Nucleate
3:15-4 pm Fireside chat: Venture capital trends
Abbie Celniker, Partner, Third Rock Ventures
David Schenkein, General Partner, GV
Dylan Neel, MD-PhD candidate, Harvard Medical School (moderator)
4-5 pm Enabling Bold Innovation in Oncology
Rosana Kapeller, CEO, Rome Therapeutics
Regina Salvat, principal, Sofinnova Investments
Marcela Maus, Associate Professor of Medicine, Harvard Medical School;
Mass General Cancer Center Director, Cancer Center Program in Cellular Immunotherapy; Paula J. O’Keeffe Endowed Chair in Thoracic Oncology
Yung Lie, CEO, Damon Runyon Cancer Research Foundation (moderator)
5-5:30 pm BREAK. Drinks served.
5:30-6:15 pm Fireside chat: Life Sciences Leadership
John Maraganore, Former CEO, Alnylam Pharmaceuticals; CEO JMM Innovations
Peter Barrett, Partner Legacy Funds, Atlas Venture
6:15 pm. Food served.
6:15-7 pm Tabletop discussions & networking. Nonprofits supporting the community.
7-8 pm Open networking
This event is part of the $1 million Timmerman Traverse for Damon Runyon Cancer Research Foundation. A team of more than 20 biotech executives are banding together to raise these funds. We’re also training to hike together to the summit of the highest peak in Africa, Mt. Kilimanjaro, in Feb. 2024.
All ticket proceeds from this event will go to support Soufiane Aboulhouda’s fundraising campaign.
Our mission is to support the next generation of cancer researchers. We are working to provide them the funds — the breathing space, really — to pursue their bold and brave ideas. These are the kind of flexible funds that can propel a young person’s career and advance an entire field of inquiry. Damon Runyon grants support postdoctoral researchers and young faculty at institutions all over the country.
Throughout Damon Runyon’s more than 75-year history, it has supported 13 people who went on to win the Nobel Prize, and 100 who went on to be elected by peers into the National Academy of Sciences.
For more information about Damon Runyon Cancer Research Foundation, and the biotech team working to raise $1 million, Click Here.
This event is being brought to you by the GSAS Harvard Biotech Club. If you have questions, or if your company is interested in sponsoring this event, please contact Soufiane Aboulhouda at email@example.com.
Thank you for your support.
Today’s guest on The Long Run is Nancy Stagliano.
She’s the CEO of South San Francisco-based Neuron23.
The company is privately held and was started in late 2018.
The idea, like we’ve seen in oncology, is to develop targeted therapies for molecularly-defined subgroups of patients with Parkinson’s, Alzheimer’s, and other common neurodegenerative diseases.
Nancy is a neuroscientist by training. She got her PhD at the University of Miami before continuing with a postdoctoral fellowship at Harvard Medical School. She made the move to industry at the peak of the genomics boom, going to work at one of the early movers — Millennium Pharmaceuticals. She later became an entrepreneur and CEO at CytomX Therapeutics (now publicly traded), before running two other startups – iPierian and True North Therapeutics — that were successfully acquired by large pharma companies.
The lead program at Neuron23 is a small molecule drug candidate aimed at the LRRK2 protein that’s associated with Parkinson’s disease. The company conducted its first trial in healthy volunteers in 2023 and has selected a lead candidate for further study in Parkinson’s patients. Neuron23 is advancing that drug with a blood-based companion diagnostic, developed in partnership with Qiagen, in hopes of selecting the patients most likely to benefit from the drug.
The LRRK2 gene was first discovered in 2004, and there are still no targeted therapies for Parkinson’s based on this finding. There are many reasons for that, and I’m including a recent review article from NPJ Parkinson’s Disease in the show notes on TimmermanReport.com.
Nancy sees brain science as the “final frontier” of biomedicine. The long-term vision is to get the right drug to the right patient at the right time, like scientists have been talking about in cancer for 30 years.
Now, please join me and Nancy Stagliano on The Long Run.
I’ve always been captivated by and drawn to the intersection of raw emerging science, ambitious determined talent, aggressive capital, and savvy strategy that come together in an often-combustible mix to generate novel therapeutics.
At the earliest stage, it’s critical to figure out what you’re going to aim at (the molecular target) and what type of therapeutic you’re going to use to hit that target. Also essential: conviction that if you are technically successful and your product runs the gauntlet through regulatory approval, there will there not only be patients interested in using it (presumably your original inspiration for the medicine), but also, physicians interested in prescribing it, and payors willing to reimburse for it.
(I note in passing the strategy outlined above is deliberately target-centric, representing the approach many drug developers take. For a broader perspective, see this characteristically insightful discussion by Derek Lowe of an exceptionally comprehensive review [plus rich supporting materials] by Arash Sadri arguing target-based approaches haven’t been as productive as we tend to imagine. Meanwhile, the utility of phenotypic-based approaches [see this 2011 piece I wrote for Forbes] may be gaining traction in the industry. As noted in this recent publication by thoughtful scientists at Pfizer, phenotypic-based approaches were critical for the discoveries of ivacaftor for cystic fibrosis, daclatasvir for hepatitis C, and risdiplam for spinal muscular atrophy.)
The complex web of considerations involved in coming up with new medicines is not only what makes the process so challenging and exciting but is also why I look forward each year to Bruce Booth’s annual review of the industry.
Booth, as most readers know, is a partner at Atlas Venture. The Cambridge, Mass.-based firm invests in early-stage therapeutics companies. Booth also writes about the industry at LifeSciVC, and curates the always worthwhile “From the Trenches” blog.
Atlas’s yearly examination of the state of the industry examines many of the key factors that influence medicine creation. The last year, in particular, has been especially challenging for young and emerging biotech companies. As Booth acknowledges, “2023 couldn’t end fast enough for most of us in biopharma; it’s been a tough year in the capital markets, and the industry is facing it’s fair share of headwinds.” Even so, he recognizes that “despite all that, great science and medicine continues to advance.”
The entire 54-minute video, available on YouTube, is must-see for anyone in the industry (I’ve already watched the whole thing twice). Five key figures – associated with important themes – stand out.
$80 Billion. This is the approximate projected size of the market for obesity medicines, a category, we learn, that is now considered “likely the biggest drug class in history.” On the other hand, Booth notes, there are also very high healthcare costs associated with obesity which could presumably be ameliorated by effective treatment; certainly, this is the argument manufactures will continue to make.
Commentary: The newfound success of obesity medicines is fascinating. Up until very recently, obesity was considered a drug development graveyard and a space most companies wanted to avoid. The rationale: the amount of weight loss that seemed to be attainable through medicines had been fairly minimal, while the risks (remember fen-phen?) were well-documented. In particular, the concern was that regulators, aware of how widely new medicines in this category would be used, were likely to look at candidate medicines extremely critically – and to demand costly, and lengthy, cardiovascular outcome studies to document safety. (It was also often assumed that medicines in this category would be regarded as mere “lifestyle drugs,” like sildenafil for erectile dysfunction, rather than more essential medicines, like statins for reducing risk of cardiovascular disease.)
The success of the new Eli Lilly and Novo Nordisk medicines – delivering not only a remarkable degree of weight loss, but also documented evidence of cardiovascular benefit (as Booth highlights) – is what has changed the benefit-risk calculus so dramatically.
In short, the drugs seem to work for many patients, and will give doctors something more powerful in their armamentarium than to helplessly advise “diet and exercise,” with minimal expectation of actual benefit (an experience that I vividly recall from my time in clinic).
One lesson: better medicines can completely reframe a therapeutic category; credit to Lilly and Novo for persisting.
Digital/data/technology question: will the arrival of effective weight loss medicines (with more in the offing, including oral formulations) create an opportunity for digital health companies to support patients on these medicines, as well as patients who can’t or won’t take these medicines, or will the arrival of effective pharmacological solutions largely obviate the need for a tech-supported behavioral fix?
21%. This is the fraction of big pharma drug launches — roughly one of out of five new FDA approvals — that end up becoming “blockbusters.” These products are defined as generating annual sales of more than $1 billion. Another 18% are “high sellers” – $0.5B-1B a year; the rest of launches attain sales lower than that. But when accounting for the cost of R&D for each of these drugs, it turns out that the vast majority of revenue is derived from the blockbusters; high sellers cumulatively bring in slightly more than they cost, while the rest are estimated to contribute negatively to R&D returns, according to Booth.
To bring the point home, Booth notes that just seven medicines – representing just 4% of total approvals over the last decade or so – generated approximately 28% of the total industry revenue.
Commentary: It is not news – or should not be news – that investment returns in pharma, like returns in many other industries including book publishing, music, and venture – follow a power law, rather than a bell-curve distribution (a distinction I discuss in my 2007 WSJ review of Nassim Taleb’s Black Swan, entitled “Shattering the Bell Curve,” here).
But surprise or not, the continued dependence on blockbusters is a source of fragility for pharma companies who urgently need to find the next one before revenues dry up. It’s also a concern for patients with conditions associated with smaller markets. This can include not only rare genetic diseases, but also patients with cancers that might be exquisitely characterized and amenable to a precise molecular therapy. As Booth points out, not only can such populations be challenging to identify in the context of clinical trials, but even if a medicine for such a narrow indication is successfully approved, “if you’re a larger company, building a commercially sustainable long-term franchise based on such tiny market opportunities is incredibly challenging.”
Ideally, big pharmas would be able to support themselves through the development of a portfolio of products that might be exquisitely effective for relatively small populations. This was explicitly the ambition of precision medicine. Yet in practice, developing medicines for small, molecularly defined populations hasn’t generally been as efficient as was hoped and expected at the beginning of the genomics era.
Gene therapy provides another example of what Booth describes as “a real disconnect between clinical impact and value for patients” on the one hand, and the view of “the market and the street,” on the other.
Despite gene therapy offering exceptional promise for patients, as well as a number of recent approvals, Booth points to data showing most companies in this space have been shellacked by the market; presumably this reflects investor concerns about the commercial promise.
In contrast, what seems to occur far more commonly is that the industry uses molecular characterization of disease to identify promising targets that can then be targeted in a range of conditions, often with little if any molecular characterization of the patient. This approach seems especially common in the very hot therapeutic area of inflammation and immunology, where the goal seems to be to replicate the success of the TNF-alpha inhibitors adalimumab (Humira) and the etanercept (Enbrel), each an example of the so-called “pipeline in a pill” (or injection in this case).
A single medicine that can effectively treat a range of patients is of course a positive development. But the concern is that if the only medicines that big pharma can develop with favorable economics are those for huge markets, many patients with potentially treatable conditions are likely to be left behind.
12%. This is the fraction of new drug approvals over the last six years that are (a) from big pharma and (b) sourced internally. More than half of all new drugs approvals — 57 percent — are from “emerging pharmas” (i.e. not “top 20”). Many of these new drugs (perhaps not surprisingly) are for smaller market indications.
But even within top 20 pharma, according to Booth’s data, only about a quarter of new approvals originated in-house; the rest relied upon the broader biotech ecosystem, and ultimately were in-licensed or obtained through the acquisition of a smaller company.
Commentary: In 2010, savvy industry analyst Andrew Baum, then at Morgan Stanley, famously suggested that pharmas (especially those not particularly good at internal research) consider evolving their model from R&D to S&D (search and develop), as I discussed here. In today’s world in which internal discovery groups account for such a small fraction of all launched products, it seems reasonable to revisit Baum’s question, particularly through the lens of what qualities and capabilities are most critical for today’s big pharma discovery organizations?
6%. This, astonishingly, is the cumulative success rate for clinical trials in 2022 – down from 16% in 2017. In other words, a product that enters a phase 1 clinical trial has only a 6% of emerging with regulatory approval, according to data Booth presented.
Commentary: Why have things gone from bad to worse over the last five years? Contributing factors suggested by Booth include challenges associated with the many new modalities that are coming forward (itself a good problem to have); tougher regulatory review; and unexpected late-stage blow-ups (admittedly a rather tautological “explanation”). I suspect that another factor could be an increased interest in oncology, since the success rate for oncology drug development is notoriously low – I have heard “5%” thrown about.
Interestingly – and despite what seems to me like an all-out effort by every big pharma I know, and involving all the top management consulting firms – the composite development time associated with taking new products from preclinical studies through registration has actually increased slightly over the last five years, with most of the uptick occurring in phase 3.
I suspect that as result of concerted efforts on the digital and data front, we will start to see some meaningful improvements in the efficiency of clinical trials – I don’t know any big pharma that’s not working on this. It’s possible technology can also help the industry achieve meaningfully reduced clinical development costs (which apparently haven’t changed much over the last five years either, according to Booth, and remain staggeringly high). But as I’ve discussed, a critical need for the industry right now is to improve the probably of success for trials, especially late-stage trials. Meaningfully increasing this value would be incredibly important and (as Andreas Bender has reviewed) also exceptionally difficult (extravagant claims to the contrary).
1. This is the number of times that Booth mentioned AI or ML in his talk. Specifically, it was in the context of drug failures (there were many), and included in this list was BEN-2293, which he described as the “lead program” of BenevolentAI. Booth’s conclusion: “Even AI and machine learning can’t help you escape the challenges of clinical trial attrition that faces so many companies.”
Commentary: Atlas has decided over the years to stick close to its knitting and focus on what might be called traditional early stage drug development; they are extremely excited about novel biology, but aren’t focused on emerging digital and data technologies. Other early-stage biotech venture firms – Flagship comes to mind – are aggressively leaning into such technologies.
Booth’s presentation captures a view of drug development that, in my experience, largely mirrors the perspective of most senior biopharma drug developers. It’s not so much anti-digital/data/technology as it is an anti-digital/data/technology exceptionalism.
To hear and read Booth is to come away with an appreciation for the magnitude of our collective challenge. It’s exceedingly difficult to come up with new medicines, especially given the complexity of biology and disease, and our still relatively shallow understanding (see here). While digital/data tools like Alphafold can be selectively helpful, they don’t seem (yet) to offer magic answers to the many challenges the industry faces.
Yet even with these caveats, we also must remember that, as I’ve discussed and as Booth highlights, we live in an incredibly exciting time for biological technologies. There are, as Booth emphasizes, a huge number of promising emerging modalities (even if most, as he also points out, are not yet associated with significant commercial value).
“Fundamentally,” Booth observes, “we are awash in great science and great ideas.”
While I am not optimistic that AI will offer great stand-alone oracular value in the near-to-medium term (i.e. “develop this, not that”), I believe there are exceptional uses of digital and data technology to support and enhance emerging biological tools – in particular, to automate processes, capture data more consistently, and accelerate learning cycles. Harvard’s Marco Iansiti and Karim Lakhani have pointed to Moderna as a leading success story here.
Particularly in situations where the process is the product (cell therapies come to mind), the ability to rapidly iterate and optimize your product will likely depend upon how effectively you leverage the powerful digital and data technologies now at hand.
Ultimately, I suspect, the question won’t be — and shouldn’t be — whether to prioritize biological or digital technologies. It will be how to most effectively integrate both categories of technologies and pragmatically leverage their combined power to get better at developing the impactful new medicines we all hope to deliver to the patients awaiting them.
Today’s guest is Diana Brainard.
She’s the CEO of Waltham, Mass.-based AlloVir.
AlloVir is developing off-the-shelf T cell therapies to fight common viruses. The company is developing these T cells, from donors, and modifying them so they can be given to patients with weakened immune systems. The company’s lead T-cell therapy candidate is made to fight six common viruses, including adenovirus, Epstein-Barr, cytomegalovirus, JC virus and others.
If AlloVir is successful, these T cells will first be used in patients who undergo stem cell transplant therapies, a common form of treatment for leukemia and lymphomas. This virus-fighting T cell infusion should also be practical to make available at a lower cost than the well-known T cell therapies for cancer.
Diana started her career as a physician-scientist at Harvard Medical School and Massachusetts General Hospital. But that was only her start. She comes to this new challenge at AlloVir – attempting to develop trailblazing treatments for patients who have no good options – after a long and distinguished track record developing drugs for common infectious diseases.
She worked for a decade at Gilead Sciences, and was intimately involved in development of the cures for hepatitis C – marketed under the names Sovaldi, Harvoni, and Epclusa. By the end of her 10-year tenure, she was senior vice president of infectious disease at Gilead, overseeing the company’s HIV portfolio. At the end, she led the frenzied all-hands-on-deck work on remdesivir – a treatment that still remains effective for patients with COVID-19.
In this episode, we talk about her roots in the humanities, how storytelling influenced her approach to patient care, and how a coach helped her grow from a strong individual performer into an organizational leader. Side note: before this interview, I had only spoken to Diana once before, but I felt like I knew her in some ways already. She is married to TR healthtech columnist David Shaywitz, who, I would say, to borrow a phrase from the author E.B. White, is both a “good friend and a great writer.”
Now please join me and Diana Brainard on The Long Run.
Most leadership offsites I’ve attended have included some flavor of personality assessment – not so much to formally classify us, but rather to make the point that different people have different styles, and to emphasize that you can’t assume everyone you work with approaches the world the same way you do.
In this spirit, I wanted to share a simple binary personality framework that resonated with me, even though I learned about it from a surprising source — several articles about Game of Thrones author George R. R. Martin (see here, here).
According to Martin, writers tend to be either architects or gardeners.
Architects, he explains,
“plan everything ahead of time…They know how many rooms are going to be in the house, what kind of roof they’re going to have, where the wires are going to run, what kind of plumbing there’s going to be. They have the whole thing designed and blueprinted out before they even nail the first board up.”
Harry Potter author J.K. Rowling, for instance, has been described as “a great example of an architect.”
Martin says that gardeners, in contrast,
“dig a hole, drop in a seed and water it. They kind of know what seed it is. They know if [they] planted a fantasy seed or mystery seed or whatever. But as the plant comes up and they water it, they don’t know how many branches it’s going to have. They find out as it grows.”
Martin describes himself as “much more a gardener than an architect.”
While of course most writers — and most of us — have attributes of both architects and gardeners, these two archetypes can help explain some of the stylistic differences one encounters in biopharma and healthtech.
For instance, large pharma companies seem to select quite strongly for architects, particularly in senior leaders. Corporations value architect-like approaches to problems, generally wanting to see a complete, comprehensive plan that has been agreed to by all stakeholders before getting to work. (Safi Bahcall has discussed this nicely in Loonshots – see here, here).
In contrast, I suspect many startups are heavily populated by gardeners, innovators who begin with a promising idea or thesis, and then are comfortable (provided that their funding holds….) building it out, seeing where it goes, making agile adjustments on the fly as needed. The image of Mark Zuckerberg founding The Facebook comes to mind (see here).
In practice, this distinction isn’t a binary matter of either/or. Succeeding at scale in complex fields like biomedical R&D ultimately requires both sets of skills.
From the startup world, consider the example of Incredible Health co-founder Iman Abuzeid, a physician, management-consultant, and MBA by training. In a fascinating March 2020 podcast interview on 20VC, the highly recommended podcast hosted by Harry Stebbins, Abuzeid describes an approach that seems to combine the two.
After spending a year cultivating, perhaps gardener-like, a different sort of company (“a SaaS platform for small and medium healthcare business”), Dr. Abuzeid says her team discovered they “just could not get it to grow.”
At this point, the startup seemed to enter full-bore architect mode, switching to an approach that would have seemed at home in any large pharma (or management consulting slide deck).
The company conducted an incredibly comprehensive analysis including “coming up with a hundred different ideas” then “applying multiple filters.” After narrowing these down to 10 ideas, they did extensive customer research on each of these to wind up with the company that is now Incredible Health, a hiring platform for nurses, with a private company valuation (as of August 2022) of $1.65 billion.
If Dr. Abuzeid exemplifies how architect qualities can be helpful in a startup, others highlight the value of gardeners in big corporations. Ed Catmull, former CEO of Pixar, makes the case for why large enterprises should support gardeners, particularly businesses working in domains where creativity is critical, like movies – and, presumably, coming up with imaginative new medicines.
Discussing Catmull’s book Creativity, Inc in Forbes in 2014, I wrote,
“Catmull emphasizes that most creative works, such as movies, are emergent phenomena, the result of a multiplicity of interacting forces into which we only have limited understanding. Great projects aren’t born, but develop over time, and are inevitably associated with a vast number of false steps and dead-ends.”
I’ve also discuss the application of Catmull’s perspective to biopharma here, highlighting Catmull’s key insight that (as I distilled it) “Each project requires its own journey; each film is the result of an often-difficult, frequently chaotic, and largely unpredictable evolutionary process that (with considerable coaxing) reveals itself over time.”
Particularly in today’s difficult funding environment, I suspect at least some venture investors are placing more emphasis than usual on the sort of highly architected analysis and approach that Abuzeid and her team adopted.
I continue to hope that large pharmas will eventually learn to recognize the value of emergent discoveries, and the gardeners who cultivate them, as Catmull has vividly described and passionately championed. However, I’d also caution young gardeners to think twice before staking their careers on this still distant possibility. For those who are determined, learning the language of architects can help. Inside large corporations, substantive architect/gardener partnerships can prove particularly productive and offer the organization access to the best of both mindsets.
Developmental biology is an intrinsically captivating field, focused on how a single cell gives rise to a complex, fully-formed organism.
As Doug Melton and I wrote in Cell, back when I was a post-doc:
“As we study the life and lineage of a particular adult cell, we ask the same questions that a biographer asks of her subject: what were the critical decisions that defined the trajectory of this life, and when were they made? What was the contribution of neighbors, and what role was played by more distant influences? What was the role of chance? At what point was the final fate initially specified, and when was it ultimately sealed? In essence, we would like to understand the molecular biography of the cell.”
It turns out that a colleague of mine, Dr. Ben Stanger, a physician-scientist and gastroenterologist now at the University of Pennsylvania, and who was in Melton lab at the same time I was, has now written a book, From One Cell, about the history of developmental biology, and it’s absolutely captivating.
In a style that’s engaging but not self-absorbed, Dr. Stanger steps us through the fascinating history of the field. Perhaps because of his own laboratory experience, he has a particular appreciation for the technical difficulty of key experiments, a perspective that brings a sense of immediacy to his descriptions of the work and allows us to appreciate the magnitude of the achievements.
Even when he moves to more contemporary advances, like the discovery of induced pluripotent stem cells (iPSCs), his explanations are clear, simple without being simplistic. You’re left with a visceral appreciation for the beauty of development, the virtue of basic research, and the promise this field continues to offer for the understanding and treatment of disease. It’s a magnificent and inspiring read.
Admittedly, it is perhaps a less than ideal listen; I had purchased the audiobook, and while the professional audiobook narrator has a fine voice, he mispronounces many essential terms (like “Drosophila”).
Hopefully, if Dr. Stanger writes a second edition, he will narrate the audiobook himself.
While we’re on the subject of audiobooks, two that are, in fact, narrated by their respective authors, and which I really enjoyed are:
While most a16z podcasts relating to AI tend to emphasize the technology, the latest episode of “Bio Eats World” features an inspiring discussion with Stanford AI pioneer Fei-Fei Li, and focuses mostly on her remarkable life story. (Her journey also features prominently, we learn, in her just-published book entitled “The Worlds I See.”)
Li is perhaps best known for her foundational work in computer vision, and specifically her critical early recognition of the need for a massive robust dataset to train AI models. Her development of ImageNet (first described in this 2009 paper) enabled the training of deep learning models that dramatically accelerated and energized the field.
Li was born in Beijing and raised in Chengdu. As a child she was a “STEM kid,” and particularly loved physics, she tells Vijay Pande, Andreessen-Horowitz general partner, Bio+Health lead, and host of “Bio Eats World.”
When Li was 15, her family immigrated to Parsippany, New Jersey, where she attended public high school. She says she barely spoke a word of English at the time and took ESL (English as a Second Language) classes in every subject but math.
A “profound” influence during high school, she says, was a math teacher named Mr. Sabella, who really took her under his wing, even setting up an individual advanced calculus class for her, which he then taught.
She credits her parents for supporting her pursuit of her passions. In particular, she says, they never pushed her to be a doctor or lawyer. She tells Pande that when her parents’ friends and neighbors learned she was majoring in physics, they told her parents to discourage her from pursuing such a “useless” area of study. Her mom, she relates with pride, simply responded, “She likes it.”
Li describes a remarkable double life as an undergraduate at Princeton. During the week, she says, she was a typical student, who enjoyed college life and “loved physics.”
Yet because her parents’ jobs had become precarious, she scraped together money to buy a dry cleaner in Parsippany, which she would manage in person from Friday night through Sunday, before returning to student life at Princeton.
Pande astutely observes this may have served as useful entrepreneurial training, adding “you weren’t playing around – it was survival.”
Ultimately, Li’s career in computer vision took off, leading her ultimately to Stanford University, where, among her other responsibilities, she is the founding co-director of the Stanford Institute for Human-Centered AI.
AI, Li says, is at a real inflection point. Like DeepMind founder Mustafa Suleyman (whose new book, The Coming Wave, I just reviewed in the Wall Street Journal – see here), Li emphasizes that AI technologies are powerful tools that can be used for both good and ill. She explains that she’s less concerned about the rhetoric of human extinction (a preoccupation of some AI “doomers”), and more interested in identifying pragmatic ways to ensure these powerful tools contribute positively. Li says this requires us not only to embrace the tools, but to recognize our responsibilities.
She emphasizes that policy should involve not just guardrails but also good incentive structures, and she would like to see more public sector investment in the technology so that all the resources and talent don’t wind up at private companies.
In applying AI to healthcare, she notes that in both healthcare and medicine, there is a robust regulatory framework already, and says we don’t need to reinvent the wheel. Rather, she suggests, we should update the framework where needed, and “inform, educate, and communicate with regulators” regarding AI.
She acknowledges the existence of a “catastrophic layer” of concerns, and worries about disinformation, in particular the potent combination of AI and social media (Suleyman’s similar concern around deepfakes is highlighted in the review).
More hopefully, and sounding a bit like John Maynard Keynes, Li says she “dreams about” a “dignity economy,” where productivity is increased so much by tech that we work because “we feel it gives us agency and dignity and reward,” rather than representing a daily grind we endure so we can put food on the table.
She acknowledges that attaining this nirvana will require hard work and require us to navigate many potential pitfalls; In particular, she worries about AI’s impact on “communities that are underserved and underrepresented.”
Li’s remarkable journey, and her reflections on it as elicited by Pande, engage and inspire. While Li’s science clearly centers around AI, her story serves to highlight the promise and possibility of human potential.
I can’t stop thinking about a recent series of poignant blog posts, written by an emergency room physician affiliated with the Mayo Clinic. Her husband has been battling a terrible cancer – recurrent/metastatic head and neck squamous cell carcinoma.
Given what she does for a living, the author, Dr. Bess Stillman, is about as well-positioned to be a savvy patient advocate as anyone could possibly be.
And yet, she describes in earnest, eloquent, horrific, harrowing, infuriating detail her Herculean efforts to find and enroll her husband, Jake Seliger, in a suitable clinical trial. Jake has also written a thoughtful companion piece offering specific, constructive suggestions – an incredibly generous contribution from a person who has suffered so much.
While I plan to highlight a few key points, these riveting first-person perspectives should be required reading for everyone in medicine, everyone in biopharma, everyone involved in the regulation of clinical trials (both FDA and Institutional Review Boards [IRBs]), and every entrepreneur eagerly trying to develop “solutions” for clinical trials.
So often, those of us in medicine, as well as those of us in biopharma, talk amongst ourselves about clinical research, and the challenges of conducting clinical trials. Typically, we review this information from a physical and emotional distance – perhaps in the context of a scientific result, or a discussion of an organizational process we seek to improve.
Physicians tend to absorb trial results from medical journals and at medical conferences and think about how to apply the findings in their day-to-day practice. Biopharma companies extensively discuss the many challenges associated with study operations and are motivated to improve the performance of discrete metrics like site activation time and speed of patient enrollment.
But most of these discussions tend to be – pardon the phrase – exceedingly clinical, and ultimately quite detached from the human experience. Neither the discussions of journal data nor the tactical review of study conduct begin to convey the visceral, excruciating frustration and anguish that comes across in the writing of Dr. Stillman and Jake.
Clinical trials represent the foundational tool of medical progress; they are the standard by which potential interventions are evaluated by the biomedical community. When I was a third-year medical student, and learning about patient care in the hospital, it was routine for published clinical trial results to form the basis of our team’s discussion. It’s the way we weed out the treatments that don’t work and elevate the ones that meet the standard of safety and efficacy.
Many academic leaders have made their name through their leadership of well-controlled, carefully conducted clinical studies that advanced patient care. Sometimes, these involve huge, sprawling enterprises, as is often the case in cardiology; the TIMI study group comes to mind, founded by the legendary Dr. Eugene Braunwald, and now led by Dr. Marc Sabatine (a former MGH colleague). But trials can also involve smaller, more focused efforts – here I think of the pioneering work in reproductive endocrinology led by Dr. Bill Crowley at MGH.
Clinical trials are of course critical for biopharmaceutical companies as well; a candidate therapy needs to pass the exacting standards of FDA review in order for the company to be able to market it in the United States, for example. Most R&D dollars in big pharma are invested in the execution of clinical trials, particularly “late phase” (phase 2 and 3) studies.
Not surprisingly, the industry is constantly seeking to improve the way trials are done. A wide variety of vendors, from large contract research organizations (CROs) like IQVIA and prominent software-as-a-service (SaaS) platforms like Veeva, to a galaxy of niche startups, contribute, or aspire to contribute to the process. It’s a massive ecosystem, all ostensibly built around the idea of improving the trial process and accelerating the development of promising emerging medicines.
As clinical researchers recognize and constantly try to address, very few patients in the US actually participate in trials. The number I’ve often heard quoted is 4%; for cancer trials the fraction may be somewhat higher (see here). Dr. Stillman writes that according to “Judy Seward, head of clinical trial experience at Pfizer, only 8% of adult cancer patients take part in trials.”
Adds Dr. Stillman, “Given our recent experience, I’m amazed it’s that many.”
Jake’s challenges, as described by Dr. Stillman, began from the moment he was failed by available (FDA-approved) medical treatment. That meant his only options were to enroll in a clinical trial.
The first problem was Jake’s “lackadaisical” and “sluggish” oncologist at the Mayo Clinic, whose approach seemed characterized more by resignation than by urgency.
This struck a chord with me. As I wrote in an op-ed for the New York Times a few years ago, we had a similar experience with an oncologist at Memorial Sloan Kettering Cancer Center who was treating my uncle for pancreatic cancer. This doctor wasn’t keen to prescribe antibiotics for my uncle’s lung infection. My aunt had to fight exhaustively to ensure her husband received appropriate treatment. The medicine worked, and he enjoyed (yes, enjoyed) a number of additional months of life.
I can’t imagine what it must be like as an oncologist focused on the treatment of patients with terrible cancers. Perhaps some degree of emotional detachment is required to help these physicians do their unusually difficult job day after day. But what many patients so fervently want and need is a doctor who will engage with them through a litany of treatment failures, and stay with them, continuing to urgently, aggressively, energetically search (if this is what the patient prefers) for the best possible path forward.
Dr. Stillman’s second challenge was the website that lists all clinical trials – clinicaltrials.gov. The good news is that all studies are listed in this database. The bad news is that it’s not especially user-friendly – which makes finding relevant trials surprisingly difficult for patients.
For starters, the search function is miserable: “If you change the wording of the search subtly,” she writes, “you’ll get different outcomes.” Searches for “Squamous cell carcinoma of the head and neck” and “Head and Neck Squamous Cell Carcinoma,” she writes, “all yield different results. There’s no apparent standardization.”
It’s also difficult to keep the different treatment options straight. For example, in some trial listings, a medicine might be referred to by the company’s internal designation (she points to “MCLA-158” as one example), or by the standardized name (“petosemtamab,” e.g.) in others. Inconsistency in nomenclature makes it difficult to keep even identical therapies straight.
Then there are the various inclusion and exclusion criteria associated with different trials. “It’s easier to find a dress to my exact specifications out of thousands on H&M.com,” she explains, “than it is to find a clinical trial” that a patient might qualify for.
How does a patient sort through such a list? Ideally, an oncologist might be able to help. But Dr. Stillman found that most oncologists had no idea how to help, other than a general suggestion that the patient go to a large cancer center that runs a lot of trials.
Far more helpful, Dr. Stillman and Jake discovered, was a skilled individual named Eileen Faucher. A biochemist who had spent years consulting for pharmaceutical companies, Faucher has now set up her own business, serving essentially as a private wilderness guide for clinical trials.
“Eileen charged a lot per hour but her fees were worth every penny: she was efficient, and without her I’d have been lost,” Dr. Stillman writes.
Similar services are cropping up to help people (who can afford the fees) make their way through the staggering complexity of our healthcare system.
Other examples include concierge physicians (who provide you their cell phone so you don’t have to endure endless phone trees to leave a message at your doctor’s office and pray someone eventually calls you back) and patient navigators, who help sort through hospital bills and insurance coverage. See also this WSJ review I wrote a few years back on a book, The Patient’s Playbook, describing the need for a “medical quarterback.”
And what about the many AI services increasingly pitching their wares to patients, hospitals, and pharmas? Unfortunately, Dr. Stillman and Jake discovered that several AI services purporting to help patients sort through clinical trial results – ostensibly, computerized, scalable version of Eileen — weren’t useful or helpful in practice. “Based on what we’ve seen so far,” Dr. Stillman writes, “the ‘AI’ tech isn’t there yet.”
Once Dr. Stillman, working with Eileen, developed and refined a list of the most promising trials for Jake, she discovered how difficult it was to determine if he could enroll.
For starters, the contact info listed for many trials was inaccurate or not useful. Other times, when she reached a person at the clinical trial site, she was told the only way to receive information about the trial was by voice, read over the phone – all other modalities were prohibited. And that was when information was provided at all.
More commonly, she discovered that even when she reached someone at a trial site, almost no one would even discuss whether Jake was eligible without him first showing up for a doctor’s appointment to “establish care.”
Generally, this wasn’t allowed via telemedicine. That would constitute practicing medicine across state lines, a big no-no. However, in some places, it seems like a “don’t ask/don’t tell” system exists in which patients can assert they are calling from in-state.
The alternative to telemedicine is a lot of travel, which is expensive, time-consuming, and exhausting for cancer patients. As Dr. Stillman observes, it’s “very hard for sick patients to travel and ‘establish care’ at multiple sites.”
Even the process of “establishing care” involves multiple parts. First, a chart must be created in the hospital system (without which you don’t exist, Dr. Stillman writes). Then, you need to be seen by a physician. This physician “can’t do anything for you — even answer questions — until after your first visit (even though they can review information you send),” she explains.
It’s also at the in-person visit that you are finally told what trials are open and what you might qualify for.
Even after a patient seems like he or she might qualify, they need to demonstrate they are at just the right point in their disease; the implicit message, Dr. Stillman writes, is “please be dying, not too quickly.”
Please be dying, not too quickly
In Jake’s case, he needed to demonstrate his disease was progressing rapidly enough to qualify. But his disease progression clock would start over each time he had a dose of chemo (which wasn’t expected to be curative — he was taking it simply to try to stay alive long enough to make it into a clinical trial). On the other hand, if tests showed he had metastases to the brain, he would be considered too sick to qualify.
Strikingly, Dr. Stillman and Jake discovered considerable site-to-site variability for what was considered sufficient progression. Even at different sites associated with the same clinical trial (and thus with identical entry criteria), some physicians would decide Jake was progressing fast enough to qualify. Others thought he wasn’t – yet all these decisions were based on identical clinical and imaging information.
Both Dr. Stillman and Jake discussed a number of challenges in getting access to data. Much of what they had to say reminded me this National Academy of Medicine-sponsored paper that I co-authored during the COVID pandemic.
My co-authors and I wrote this passage about connectivity and last-mile issues in the context of COVID, but similar points could be made today regarding clinical trials:
“[D]uring the initial stage of the pandemic in the U.S., decision-makers were essentially flying blind. Electronic health record (EHR) systems were mired in a sea of codes, few of which pertained to COVID-19, due to its novelty. These systems were not connected to enterprise resource planning systems, and thus lacked the ability to correlate relevant patient encounters with human resources and physical capacity. The utilization of testing, PPE, beds, and ventilators varied within and across each and every health system (and often varied even across departments within a single hospital or clinic). Public health departments in charge of implementing rules, policies, public guidance, and contact tracing operations each operated within their own data silos—often taking the form of piles of spreadsheets—and were almost always unconnected and non-interoperable with any other health care information technology (IT) system. In too many cases, the only effective communication of data between health care delivery systems and public health agencies was through a fax machine.”
Dr. Stillman shines additional light on several of the critical information gaps that collectively result in a shocking lack of awareness and coordination:
Dr. Stillman’s frustration extends to regulators, both at the federal level and with local Institutional Review Boards.
For instance, she learned about data suggesting that a fecal transplant procedure can make some non-responders to the PD-1 inhibitor pembrolizumab (Keytruda – see here for more the remarkable history of this blockbuster product) start responding to the drug.
She pursued this idea as a course of treatment for Jake. Yet, she struggled to get permission to do this, she reports, explaining that the FDA cracked down on the fecal transplant procedure after two deaths (out of what she estimates are 10,000 procedures).
Her local IRB, she says, also hasn’t been eager to approve this procedure. With understandable acidity, she writes, “Luckily for Jake, the FDA and IRB will protect him from being one of the 2 in 10,000 patients who died from a fecal transplant by denying him this potentially life-saving therapy so that he could have a 99%+ chance of dying from his cancer. I feel safer.”
I’ve discussed the need for an approach to regulation that places a greater premium on the harms that can be done by delaying access to promising experimental medicines, and the value (see here) of a more personalized approach to regulation.
The challenge of contemporary evidence generation has been discussed very thoughtfully by Matt Herper at STAT, here. I’ve emphasized the need to learn from real world experience here. But if you really want to understand some of the gnarly operational issues associated with getting clinical trials up and running, you owe it to yourself to read this (now deleted but archived at the link provided) blog post, from an anonymous (but credible) clinical trials operations expert.
The author offers an unvarnished, behind-the-scenes view of how the trial process works from the in-the-trenches, operations side, starting with clinical site selection, which the author notes is “superficially quite reasonable,” yet “actually going through this process is an extended exercise in sheer absurdity.”
And on set-up process for clinical sites: “There are so many examples of horrible, atrocious inefficiencies that I encountered while dealing with the logistics of clinical trial setup that I could easily triple the length of this section if I wanted to.”
Much like Dr. Stillman’s articles, the anonymous author’s depiction of the messy reality of clinical trials operations and set-up offers urgently needed visibility into what’s actually going on. Uncomfortable as it is to read, the description provides an essential alternative to the abstracted, idealized version of trials so often shared on PowerPoint, and discussed in planning meetings. Everyone — including (perhaps especially) senior leaders and decision-makers — deserve, and would likely benefit from, a candid look into how the sausage actually gets made.
Both Dr. Stillman and Jake strive to be constructive. They highlight what patient-centricity (the core value expressed by every stakeholder in the clinical trial process) actually looks like from the patient’s perspective.
What’s needed, they suggest, is a far more convenient way to identify, prioritize, learn about, qualify for, enroll in, and participate in relevant clinical trials.
What’s needed, they suggest, is a far more convenient way to identify, prioritize, learn about, qualify for, enroll in, and participate in relevant clinical trials
Most of the people I know involved in the clinical research enterprise aspire to deliver this, yet it’s clear from Dr. Stillman and Jake just how far from this ideal we collectively seem to be.
As Dr. Stillman concludes, hopefully,
“There’s a future that can support the interests of patients who are willing to take part in trials, the patients who will benefit from the results, the doctors who are trying to do what’s right for their patients, as well as the interests of the pharmaceutical companies supporting the trials.
There’s a better path. We’re just not on it. Yet.”
Today’s guest on the The Long Run is Reid Huber.
He’s a partner at Third Rock Ventures in Boston.
Third Rock is known in biotech as one of the venture firms that creates new companies that seek to turn groundbreaking science into new medicines. Since its founding in 2007, Third Rock has put together a portfolio of 62 companies that have collectively created 20 products that have made it all the way through clinical trials and onto the market.
Some of Third Rock’s earliest startup investments have now had time to mature. Agios Pharmaceuticals for cancer and rare diseases, Bluebird Bio in gene therapy, Global Blood Therapeutics for sickle cell disease, Myokardia for hypertrophic cardiomyopathy and Sage Therapeutics for the treatment of depression – are a few examples of companies that have done what they said they were going to do. They created new products that help people, and they rewarded investors.
Third Rock is now investing out of a $1.1 billion fund, its sixth. I wrote about it on Timmerman Report in June 2022.
Reid joined Third Rock in 2018 after a long career at Incyte, a developer of drugs for cancer and immune diseases. He’s closely involved in a handful of startups, including companies developing cell therapies for cancer and autoimmunity; one that’s using machine learning for drug discovery; a precision neuroscience drug developer; and another that’s discovering small molecules that form covalent bonds with their molecular targets. And there’s more.
In this conversation, Reid talks about growing up in a middle-class family in central Illinois, how he got introduced to human genetics at an auspicious moment in history, and how he built a career in industry that connected the dots between human genetics and the making of new medicines.
I should also mention that Reid and I first got to know each other on the inaugural Timmerman Traverse for Life Science Cares in 2021 – a hiking trip for biotech executives who give back to fight poverty and support science education and job training in our communities.
Toward the end, we talk about some of the current challenges in the financial and political environment, but also why this is an amazing time of possibility in biotech.
Now, please join me Reid Huber on The Long Run.