Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Jason Steiner, Architect & Advisor, AI-Drug Discovery
The past few years have seen a wave of AI investment in drug discovery in both large pharma companies and in venture-backed biotech startups. Expectations are running high. Management teams are betting that even marginal improvements on the ~90% failure rate of clinical trials will be worth the investment.
While there have been hints of improved R&D metrics in speed and cost, there has not yet been a clinical approval of a drug that may genuinely be considered “AI generated.” Benevolent AI and Atomwise, a couple of well-known AI-driven drug discovery startups, have made large strategic shifts in the aftermath of failures in clinical translation.
As AI tools mature, however, we are working our way through the Gartner hype cycle to the early stages of the Slope of Enlightenment. The future of AI in drug discovery is brighter than ever. The rise of “AI-first” biotechs, major strategic initiatives from pharma leaders like Genentech, GSK, and Sanofi, an ecosystem of industry providers developing products for the entire R&D pipeline, and a keen focus from regulatory agencies like the FDA are all pointing toward a more productive future.
The path to an approved clinical product is long and we haven’t seen an obvious AI drug discovery success, like a novel molecule invented in whole cloth by AI sailing all the way through the clinical trial process to FDA approval (though some companies may advertise this).
That’s probably a bit further out in the future, but when it happens, the whole world will know. In the near-term future, we might expect to see more substantial progress behind the scenes. There are many steps on the way where AI may be useful, and AI’s role in the fundamental mechanics of the drug discovery and development process are where I expect it to shine.
For those looking outside in, some of the major trends are detailed below:
This is not unique to pharma, however, its application across the massive data troves in both the scientific literature and proprietary databases is currently the most significant productivity lever. Efforts such as the JulesOS agent developed by GSK provide prompt-level access to the vast array of internal data to users without any need to know of such data a priori.
Similarly, an array of startups such as Elicit are providing products that can search and synthesize the scientific literature base en masse. Some of these capabilities are also being offered by frontier multimodal LLMs.
One such application was demoed by Google in its release of the Gemini model, showing the ability to scan hundreds of thousands of scientific papers, extract desired data, and update graphics and charts for review papers.
While it is still in early stages, the development of AI systems that can both synthesize, search, and reason across data is on the leading edge of scientific research in the form of “AI Scientists”. While de novo scientific hypothesis generation and testing is still in its nascency, the combination of generative models such as LLMs and search architectures such as those that powered the Alpha series of models from Deepmind, may enable a more automated form of science.
A key component of this will be building the physical and experimental systems that can translate AI-generated content into real physical testing and close the hypothesis/data loop.
One of the key shifts in life science research that began with genomics and is now expanding to many other types of biological information is the rise of “hypothesis-free” data generation.
In more traditional life science research, data has been viewed primarily as a means to answer a specific scientific hypothesis.
In the context of machine learning, however, data is viewed more from the perspective of the characteristics of its statistical distribution. This is a fundamentally different view on data generation.
As Aviv Regev, Genentech’s head of early R&D, has stated (paraphrasing) – they may make chemical compounds that will never become drugs, but that will be useful for training models that can generate many drugs.
Aviv Regev
A key requirement of effectively implementing this strategy is the “lab in the loop” model that integrates wetlab and computational functions. This type of model is being pursued by an increasing number of AI-first biotech companies but remains relatively rare in traditional pharma where organizational structures often place computational groups as separate functional departments that more frequently default to service providers for the therapeutic and commercial units instead of being fully integrated.
A more comprehensive integration of wet and dry lab teams can yield greater efficiency for both, for example, by prioritizing better design of experiments for improved productivity. Such active learning has recently been demonstrated, for example, by improving the efficiency of experimental design to search the genetic perturbation space of CRISPR screens using prior knowledge and deep learning models. This closed-loop integration often requires large established companies to overhaul existing workflows and ways of thinking.
Much of the AI industry has consolidated around transformers as the architecture of choice for development because of its inherent scalability on existing computing accelerators. The term “LLM” has often become erroneously synonymous with AI in general. The primary focus of many of the frontier models has been toward massively increasing the size and scale of the data and compute they require to train.
However, for many applications (both in bio and beyond), this trend is not critical. Model architectures are becoming more computationally efficient and frequently getting better at learning from smaller and more curated data sets. Just in the past year, for example, new architectures for non-attention-based sequence models such as Hyena and Mamba were published that rival and may exceed performance metrics of attention-based models with significantly lower computational overhead.
Further, the architecture space of models addressing biological questions has been much more varied both in size and diversity than those in the LLM field. Smaller models with more task relevant architectures will continue to drive useful applications in drug discovery. Scale and attention are certainly not all you need to drive R&D productivity.
Platform technologies in the life sciences have ebbed and flowed in favor over the past few years. They hold the promise of dramatically improved long-term productivity often at the cost of high near-term investments. Major platforms like CRISPR, mRNA and AI hold tremendous promise for the future of medicine, and we are in the early days. The success of mRNA during COVID and the recent FDA approval of the first CRISPR cell therapy for sickle cell disease are key examples.
But the productivity power of a platform is demonstrated most powerfully not in the first success, but the second.
While both mRNA and CRISPR are specific modalities that have had decades of foundational research underpinning them, AI is a broad-spectrum enabling technology that applies to the industry writ large. It is being aimed to address the fundamental levers of cost, probability of success, and time to develop a product.
However, if we consider an average development time of 12 years and an average clinical success rate of 10%, the second success proof point is challenging especially if we have not yet seen the first. Even a doubling of the success rate and halving the development time — both tremendous achievements — would yield a net probability of 4% for seeing two clinical successes over more than half a decade, requiring a minimum of 25 pipeline candidates per platform.
The industry has gone through peak expectation cycles. Companies that have overpromised are in the trough of disillusionment. But the future of AI in drug discovery is just in the early days of the slope of enlightenment.
It’s an exciting future.
Jason Steiner is an architect and advisor specializing in AI for drug discovery. To read more about the intersection of technology and biology — Jason writes at Techbio<>Biotech
He is also a member of the Timmerman Traverse for Damon Runyon Cancer Research Foundation, a biotech industry effort to raise $1 million for young cancer researchers with bold and brave ideas.
As Jason puts it:
The pace of technological development in the life sciences is tremendous and is often being led by early career scientists pursuing innovative research efforts. Unfortunately, public funding for young scientists has been declining for decades. To ensure that we can keep the pipeline of future scientific developments strong, particularly to address major diseases like cancer, please consider making a donation.
Luke Timmerman, founder & editor, Timmerman Report
This is the time of year when many people sit down and think about the causes they want to support.
I’m asking you to consider donating today to young scientists through the Damon Runyon Cancer Research Foundation.
Our system for funding science doesn’t do enough to support young people. The average age of a first-time NIH grant recipient was 32 in 1970. That number has now crept up to about 42.
This means too many brilliant scientists in this new generation are being forced to toil on insufficient wages, and without the independence they need to break new ground. People need a chance to get on a sustainable career path in their 30s.
By supporting outstanding young scientists, we in the biotech community can make a difference. We can breathe oxygen into creative new ideas that otherwise would be cast aside by cautious, incremental funding agencies.
If we don’t do more to support young scientists, many will continue to leave their scientific dreams behind, opting for more lucrative careers so they can get married, have kids, and afford a home.
When this happens, science misses out.
It supports bold and brave young scientists across the US.
Damon Runyon has a keen eye for talent and a terrific track record. In its more than 75-year history, its grant recipients have gone on to win many accolades, including:
By betting on promising early-career scientists and giving them the freedom to pursue their own ideas, Damon Runyon is a force multiplier for cancer research and biology.
Decades ago, its scientists were the first to cure a solid tumor with chemotherapy. More recently, its discoveries include the first targeted ALK inhibitor for lung cancer and the first demonstration that CRISPR could edit genes in mammalian cells.
In the mid-2000s, before it was cool, Damon Runyon invested in scientists who were exploring cancer immunotherapy with checkpoint inhibitors and CAR-T cell therapy.
I’m committed. I’m personally leading a team of biotech executives and investors who are raising $1 million for Damon Runyon. At the end of the campaign, we’ll gather together to climb Mt. Kilimanjaro, the highest peak in Africa.
Cancer affects almost everyone at some point in life, either personally or through members of our families.
We are living in a moment of tremendous possibility for cancer research and development.
Our support today will pay dividends for generations. This is our chance.
Please go directly to the Timmerman Traverse for Damon Runyon Cancer Research Foundation’s website to see who’s on the team and how you can make a donation today.
Today, I have a dynamic duo of scientific entrepreneurs on the show – Shelley Force Aldred and Nathan Trinklein.
Rondo co-founders Shelley Force Aldred (CEO) and Nathan Trinklein (CSO)
They are the co-founders of San Francisco-based Rondo Therapeutics. The company raised $67 million in a Series A financing announced in March 2022. Shelley is the CEO and Nathan is the chief scientific officer. Rondo is developing bispecific T-cell engaging antibodies against solid tumors.
For those of you who have an active Timmerman Report subscription, see a report on Rondo here.
These two have been working together since graduate school at Stanford University. They are now on their fourth company. Their biggest success together was TeneoBio, a company that developed bispecific antibodies for liquid tumors. Amgen agreed to acquire that one for $900 million upfront in July 2021.
Now, please join me and Shelley Force Aldred and Nathan Trinklein on The Long Run.
I’m going to be in Cambridge Mass on Jan. 23 for an event that supports the Timmerman Traverse for Damon Runyon Cancer Research Foundation. It’s called “Bridging the Gap,” and it’s organized by Soufiane Aboulhouda, a member of my latest team on a mission to raise $1 million for cancer research. An outstanding lineup of scientists and entrepreneurs make this a can’t-miss event. I’m moderating a conversation with Phil Sharp of MIT and Vicki Sato of Denali Therapeutics and VIR Biotechnology.
David Shaywitz
Spurred by several questions I’ve received from students and trainees, today’s year-end column examines some of the ways large biopharma companies are fundamentally different from small biotech companies and startups.
We’ll also ask whether biopharma can overcome new technology’s dreaded “productivity paradox” and learn, quickly, how to apply AI to accelerate drug development.
Very large pharmas (to borrow from Fitzgerald) are different from the rest of us. To appreciate these distinctions, it’s helpful to examine how large and small biopharma companies (including startups) approach key challenges facing the industry.
Arguably, the single most important advantage large biopharmas have is that their size enables them to pursue a portfolio approach and absorb losses that tend to sink smaller companies – it’s that simple. If you are J&J or Roche, with a market cap in the hundreds of billions, you can absorb the inevitable program failures; if you are a startup or small biotech, it’s much more difficult. (Note: this is also a key reason why drug development is so expensive – the calculations need to factor in and account for not only the cost of rare successful program but also the amalgamated cost of the many, many setbacks.)
Another key advantage big pharmas have is that they tend to have deep expertise across a range of areas, from chemistry to statistics to clinical development to marketing. Moreover, their large size (at least in theory) increases the likelihood that big pharma programs get both the attention of vendors (like CROs), and discount pricing (for the same reason a large hospital system can negotiate more effectively with insurers than can solo practitioners).
While many startups are founded on the idea that they’ve identified a key obstacle – for instance, a traditionally “undruggable” target that they’ve figured out how to attack – the startup still needs to do all the other block-and-tackle activities required to make a product. While service providers like contract manufacturing organizations increasingly enable startups (as well as larger companies) to outsource much of this work, operationally, there’s just so much to get right.
One manifestation of the broad focus of large pharmas can be seen in their approach to technology innovation. I learned this the hard way after I arrived at an R&D technology strategy role at a large pharma, spoke in depth to researchers across the organization, and identified a number of unusually precocious digital innovators. Delighted by the talent I identified, I proposed that the organization invest additional resources behind these stars and expand their individual efforts.
Yet this turned out to be, from the perspective of top R&D leaders, including the head of R&D, exactly the wrong answer. These innovators, I was told, were obviously on the right track and of course should be acknowledged, but the real strategic goal was to bring everyone in R&D up a notch. A global improvement of even 5% in facility with emerging technologies, I learned, was considered far more useful to the organization than supercharging those who were already ahead.
Incredulous, I asked the brilliant founder of a leading AI-driven biotech startup for a second opinion, and I was surprised to hear a similar perspective. The founder told me that in the tech industry, “a single individual, or even a very small team, can leverage technology to move very quickly… and make huge amounts of progress.”
But drug discovery, the founder continued, “is very much a team sport. A single individual or even a small team are rate limited by the pace at which they can do biology or chemistry experiments. Conversely, if you accelerate the entire organization…then that can be very value creating.”
This mindset (which I understand, though not yet fully embraced as I continue to believe in the value of investing behind pioneers) may also explain why large pharmas are increasingly moving towards shared, enterprise platforms (“foundational enterprise capabilities,” in the words of consultants Lamare, Smaje, and Zemmel), and are leery of isolated tech solutions.
Biopharma remains an exception-based, hit-driven business, largely living off of infrequent, outsized successes. This is a domain ruled by the power law, not the normal “bell curve” distribution.
To the consternation of all, our ability to identify the rare “winners” seems as elusive as ever (see here, here); many blockbusters have come from products that weren’t initially recognized as especially promising. Examples here include Merck’s pembrolizumab (Keytruda) (as I discussed at length here), and also Millenium’s bortezomib (Velcade), acquired in the LeukoSite transaction that was focused primarily on a different product, Campath (see here, also here).
On the other hand, the GLP-1 obesity products so much in the news these days emerged from decades of meticulous and deliberate work in both academia and leading diabetes companies, Lilly and Novo Nordisk. Notably, these pharmas also had the resources and conviction to conduct the essential but often daunting long-term cardiovascular outcome studies that discouraged many other companies (large and small) from investing in the field at all.
Even so, the magnitude of the drug effect – both in terms of weight loss and in terms of cardiovascular benefit – is likely well beyond what most optimists probably imagined. Not surprisingly, many pharmas who largely shunned obesity are urgently now trying to acquire their way into this market.
Given the importance of identifying “winners,” I’ve been struck by how many senior drug developers with whom I’ve spoke have confided to me that they think R&D strategy (in terms of what to go after) tends to be overrated.
One veteran told me that while a strategy can be useful for attracting early investors to a startup, or facilitating communications in a larger organization, in practice, success tends to depend less on any particular strategy, and more on how astutely you respond to what you encounter (see also Challenge 5, below).
This skeptical and pragmatic attitude to strategy may represent the pharma equivalent of U.K. Prime Minster Harold Macmillian’s famous response when asked about “the most troubling problem” he faced during his tenure. Macmillan’s answer: “Events, my dear boy, events.”
A key advantage that belongs to (well-funded) startups and small biotechs is their exceptional focus and agility. Because their aperture is typically so narrow, small companies tend to be exquisitely attuned to challenges their programs face, and can generally respond more rapidly, and adjust more nimbly, than large biopharmas. There also tends to be a remarkable degree of organizational alignment – it’s much easier to get everyone to row in the same direction, since everyone is palpably invested in the same outcome.
Startups and small biotechs often have a relative flat organizational structure, conducive to fluid communication and fast decisions. In the presence of sufficient funding (obviously not a given in the current difficult environment), startup scientists can pursue novel science, and biotech development teams can respond to unforeseen challenges, with an urgency and flexibility that tends to be far more difficult to come by in large companies, with their elaborate decision procedures and rigid processes.
In contrast, large biopharmas are astonishingly complex organizations. They are unimaginably, almost anachronistically hierarchical. Information, like authority, cascades down, rather than diffuses across. Because of their size, there is extensive reliance upon, and deep reverence for process (“trust the process” tends to be an earnest aspiration), and decisions often require not just consensus but also a stultifying number of preliminary meetings to ensure all proposals are thoroughly socialized, and all senior stakeholders are suitably aligned.
One consequence: in large companies, decision-making tends to be both painfully slow and incredibly risk-adverse, as Safi Bahcall in particular has documented (see here, here).
Effectively navigating intricate corporate structures also requires a facility with the sort of organizational power politics that authors such as Stanford’s Jeffrey Pfeffer and USC’s Kathleen Kelley Reardon astutely describe.
As difficult as it can ordinarily be for anything to get real momentum in sprawling bureaucratic biopharmaceutical companies, their ability to execute effectively on a global scale when they actually hit upon someone promising is extraordinary. Pfizer’s development of the COVID vaccine is one compelling example; Merck’s exploitation of pembrolizumab (Keytruda) is another.
In these and other cases, once a large biopharma decides to go “all in” on something, and the opportunity seems authentically compelling (rather than desperate), the ability of these massive organizations to execute on a global scale is extraordinary to behold. Everyone in the organization understands the opportunity and the imperative, and the result can be mind-blowing.
Of course, the pursuit of promising data motivates and energizes biopharma companies of all sizes. The difference is that large pharmas are uniquely positioned to drive these programs forward at scale.
I couldn’t have asked for a better way to wrap up 2023 than to listen to Microsoft’s Peter Lee discuss GPT-4, and generative AI more generally, earlier this week at a Dean’s Lecture at Harvard Medical School.
Peter Lee, Corporate Vice President,
Microsoft Research
Lee, readers will recall, co-wrote the book The AI Revolution in Medicine: GPT-4 and Beyond, together with Harvard professor Zak Kohane and veteran journalist Carey Goldberg, who were both in attendance.
A year or so into the GPT-4 era, Lee seemed as excited by the promise of GPT-4, and as mystified by its mechanism, as he was when he first wrote the book (and when all three authors discussed it with me in May at Harvard’s Countway Library – video here, transcript here). It’s abundantly clear that although we’re still in the earliest days of generative AI, the technology holds exceptional promise, and of course significant risk.
Perhaps Lee’s most enduring message was one of the last points he made, citing a poignant and personal example that Kohane offered in the book.
“My first patient died in my arms,” Kohane wrote. “I was a freshly-minted doctor in a newborn intensive care unit, and despite maximal efforts with the best that medicine had to offer at the time, I had to hand a baby boy’s lifeless body to his parents within 24 hours of his birth.”
Kohane observed that “At the time, the death was an unavoidable tragedy.” Yet within a year, a new treatment approach was found to be effective in similar patients.
“It became standard practice a year later in the very same nursery where my first patient died,” Kohane writes. “He would likely have survived if he had been born just a little later.”
Or if the therapy arrived a year earlier.
Kohane acknowledged the many different steps required to bring a therapy forward. If AI can be applied productively to even a few of them, he wondered, how big a difference might that make in accelerating a treatment’s evaluation and approval?
The story of the baby boy who was one year away from a lifesaving intervention is a highly resonant example. It points to the importance of all the different tasks that medical product approval requires – and accordingly, all the opportunities for optimization and improvement. It reminds us of the importance of saving time – months matter, and a year or more of process improvement can be the difference between life and death.
Phrased differently: we tend to hope AI somehow comes up with new brilliant treatments. But even if AI “just” accelerates paperwork and increases process efficiency, that boost could still meaningfully hasten the delivery of improved therapeutics to patients.
Given the many areas of opportunities for improvement in both healthcare and biopharma, the pressing question is whether AI will actually drive rapid improvements in productivity?
Top management consultants, naturally, tend to say “Yes, and leading companies have already demonstrated this, why are you lagging?”
In biopharma at least, these assertions lack credibility.
For example, when consultants enthuse aspirationally that “a GenAI model can be applied to a massive pharma molecule database that can identify likely cancer cures,” most experienced drug hunters and scientists will just roll their eyes.
Those with a historical perspective on technology remind us of the “productivity paradox” and say it’s always taken longer to achieve technology’s promised benefits than anticipated – i.e. think about Kahneman and consult your priors.
With this in mind, I’ve explicitly discussed why, based on previous experience, we should cautiously manage our expectations for AI in the context of biopharma.
Nevertheless, many experts hope and expect that this time will be different. Such earnest optimism was expressed for AI in healthcare delivery by UCSF’s Robert Wachter and Stanford’s Erik Brynjolfsson in the latest JAMA.
These authors argue that “the ability of the digital tools to rapidly improve and the capacity of organizations to implement complementary innovations that allow IT tools to reach their potential—are more advanced than in the past.”
They also emphasize (as I’ve described in detail here and here) the importance of reinventing processes, noting that “great gains will only come when implementation is coupled with significant changes in the design of the work.”
Lamare, Smaje, and Zemmel also explicitly emphasize the need for companies to “fundamentally rewire” how they operate.
I appreciate the optimism of Wachter and Brynjolfsson, and recognize the extraordinary promise and rapid improvements in AI. At the same time, I am mindful of the magnitude of the intrinsic biologic, human, and organizational complexities that must be addressed in biomedicine.
In biopharma, a question of particular interest in whether AI can help us become not only fail more efficiently but succeed more frequently – i.e. increase our probability of success by improving how we select targets, indications, and patient populations. Already, there are seemingly hundreds of startups all claiming they can help with this (I’ve spoken with several in just the last few days).
These assertions – that an algorithm or model can impact the overall probability of success – can be tricky to evaluate. Given the many ways a drug can fail, it’s going to be challenging for early adopters of AI methodologies to critically assess the impact (if any) the AI is having.
Yet, how exciting to consider the possibility that at least in some cases, it might be possible to leverage existing data to make better decisions than the typical eminence-based approach.
More generally, the challenge and opportunity for R&D leaders of today is figuring how to effectively integrate emerging biological modalities with powerful but still nascent digital and data tools, in a fashion that leverages these methods without fetishizing them.
Amy Abernethy
A final note on the challenge of developing health technology solutions: the brilliant Amy Abernethy (well-known to regular readers of this column) announced this week that she’ll be stepping away from her role as the president of product development and chief medical officer at Verily, essentially to approach the challenge of evidence generation from a different perspective.
The departure of Abernethy represents a tremendous, possibly catastrophic loss for Verily and their aspirations to demonstrate the ability to deliver concrete solutions in healthcare, including biopharma. Despite a preponderance of super smart engineers, the company just can’t seem to covert this brilliance into tangible commercial healthcare products.
As one health tech leader tartly told me, “Verily is such a hot mess. Never has a company been so well funded for so long with no clear mission as to why it even exists.”
And now it feels like the key experiment has been done, seeing if the transplantation a new visionary nucleus — Abernethy — into the existing structure could help the organization at last become a competitive health product company.
The answer, sadly, seems to be: No.
Nevertheless, both Verily and Abernethy are right to recognize the promise of emerging technology to address enduring challenges in healthcare delivery and drug development.
Let’s hope that in 2024, we spend less time fantasizing, catastrophizing, and rhapsodizing about the extent to which AI ethereally “changes everything,” and instead use our energy to develop more tangible examples of AI palpably improving something in the way new medicines are discovered, developed, and delivered to patients.
Best wishes for a creative, joyful, peaceful, and impactful 2024!
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Today’s guest is on The Long Run is Jeff Jonker.
He’s the CEO of San Diego-based Belharra Therapeutics.
Jeff Jonker, CEO, Belharra Therapeutics
It’s a startup that came out of stealth mode in January 2023 with a $50 million Series A financing from Versant Ventures, and a partnership with Genentech. I wrote about it at the time on TimmermanReport.com and am providing a link for subscribers in the show notes.
The investment is supporting a new method for discovering traditional small molecule chemical compounds that make up the majority of drugs. Belharra is based on scientific work at Scripps Research in San Diego. Scientists there envisioned a way of discovering new targets on proteins that small molecules can hit.
Small molecules sometimes tend to get upstaged because they have been around for so long. Scientists tend to get excited about new treatment paradigms, like cell therapy, gene therapy, gene editing, antibody-drug conjugates, targeted radiotherapies and more. But when scientists can discover a new way of binding with a diseased protein, and they can do it with a convenient pill that patients can take by mouth, that can be a compelling thing. Advances in small molecule chemistry like have opened up new targets, like KRAS, a protein found in many types of cancer that was long considered “undruggable” but is now quite druggable.
Jeff comes to this opportunity after a long career in biotech business development. We talk about the circumstances that allowed him to get into the industry with a legal background, how he thinks about partnering, and some of the current challenges in developing this type of treatment.
Now, please join me and Jeff Jonker on The Long Run.
I’m going to be in town Jan. 23 for a big event for the Timmerman Traverse for Damon Runyon Cancer Research Foundation.
It’s called “Bridging the Gap,” and it’s organized by Soufiane Aboulhouda, a member of my next Kilimanjaro team that’s on a mission to raise $1 million for cancer research. I’ll moderate a conversation with Phil Sharp of MIT and Vicki Sato of Denali Therapeutics and VIR Biotechnology. This event is a fundraiser for Damon Runyon’s national network of bold and brave young cancer researchers. An outstanding lineup of scientists and entrepreneurs make this a can’t miss event. Get your tickets now!
Angelos Georgakis, executive coach to biotech leaders
[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, president and chief scientific officer, Regeneron Pharmaceuticals
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.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
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.
PROGRAM
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 bridgingthegapevent@gmail.com.
Thank you for your support.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Today’s guest on The Long Run is Nancy Stagliano.
She’s the CEO of South San Francisco-based Neuron23.
Nancy Stagliano, CEO, 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.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
David Shaywitz
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.
Bruce Booth, partner, Atlas Venture
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.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
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.
Diana Brainard, CEO, AlloVir
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.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
