Think Clinical Trials Are Working OK? Ask a Cancer Patient

David Shaywitz

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.

Dr. Bess Stillman

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.

So please, read Dr. Stillman’s pieces first, starting with part I, here. Then please read Jake’s companion discussion, here.

Context: Why Clinical Trials Matter

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.”

The Patient Perspective

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.

ClinicalTrials.gov – a Mess

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.”

The Struggle To Enroll

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.

Lessons Learned – Digital and Data

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:

  • Lack of trial awareness by oncology researchers: “Researchers didn’t have their own spreadsheet or databases of active trials,” she writes. “They didn’t know what trials were going on down the street. Sometimes they didn’t know what trials were going on in their own (very large) departments.”  She also cites a damning survey from the Tufts Center for Drug Development, reporting that although “about 90% of physicians surveyed said they feel comfortable discussing clinical trials with their patients,” nevertheless, “less than 0.2% actively refer them to studies. These doctors reported that they lack access to trial information (54%), do not know where to refer patients (48%), or do not have the time to learn about active trails (33%).” 
  • Lack of trial awareness by researchers in the same network. Even within a given research network, such as Sarah Cannon or MD Anderson, there seems to be a lack of awareness of other trials occurring under the same organizational umbrella.  “A researcher at Sarah Cannon in Denver told me that even the Denver site doesn’t know what the Nashville or Florida sites are doing…. there’s no easy internal system between hospitals, or regions,”  Stillman writes, stunned by the degree “information siloing.”
  • Lack of site-to-site referrals within the same study: “If one hospital thinks a patient would be a good fit for a study, but their study arm is closed, wouldn’t it be beneficial to the drug company to recommend other sites?” Dr. Stillman asks. “Shouldn’t sites coordinate with each other? That way, the overall trial can more quickly fill a given study with eligible patients.”
  • Lack of information for patients from sponsors about site availability: “I tried contacting various drug company trial contacts directly, to see if they’d provide me with a list of all sites and openings,” Dr. Stillman writes, adding “I was only able to get contact information. I couldn’t get real-time information about availability, not because it didn’t exist, but because no one would give it to me.”
  • Inability of a PI (“Principal Investigator” – researcher leading a study at a site) to know, in their own study, if spots are available: “Even the PI hosting a trial often don’t know in real-time if a participant spot is available,” Dr. Stillman writes. She shares an example of requesting a spot for Jake in a trial and then finding out two days later the study was closed by sponsor to new participants.
Regulatory Challenges and Opportunities

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.

Glimpsing the Underbelly of Clinical Trial Operations

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.

Aiming To Do Better

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.”


Investing in the Future of Medicine: Reid Huber on The Long Run

Today’s guest on the The Long Run is Reid Huber.

He’s a partner at Third Rock Ventures in Boston.

Reid Huber, partner, Third Rock Ventures

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.


How The Unmet Needs of Patients Made Me A (Grounded) BioTechno-Optimist

David Shaywitz on the Longfellow Bridge

My Ground Truth

Every other week, I stroll across the Longfellow Bridge from Cambridge to Boston. It can be a magnificent walk in the fall and spring, when the weather is temperate and the skies clear. You can see the deep blue of the Charles River, the Esplanade on your left, with the Boston skyline behind it, and the Citgo sign in the distance. 

On the other side, you see the biotechnology cluster of Kendall Square and a large swath of MIT, expanding out from the river’s northern shore. Boston University’s campus lies a bit further upstream, on the south, while Harvard’s campus is a few bridges further down, to the north.

For me, the regular walk to Boston provides me with critical grounding – partly because I’ve spent most of my adult life within the vista before me, but mostly because of the hour-long teaching conference at Massachusetts General Hospital (MGH) I attend, learning about the complicated challenges faced by a patient receiving care on internal medicine service. The discussion is often inspirational and is invariably humbling.

The Charles River, as seen from the Longfellow Bridge

The conference, and the activities that support and enable it, were originally conceived by two MGH medicine residents, Dr. Lauren Zeitels and Dr. Victor Fedorov.  In 2016, they founded the Pathways program, “dedicated time during residency for house staff to connect with scientists and delve into the fascinating biological questions that arise at the bedside.”  Particularly in a busy hospital like MGH, with many acutely ill patients requiring focused attention, such time to reflect can be fleeting, yet remains vital, as Dr. Denny Ausiello and I discussed here.

Tragically, Drs. Zeitels and Fedorov died in 2017, in an avalanche while snowshoeing in Canada.  The pathway program endures as a legacy to the power and urgency of their vision.

Pathways conferences are led by talented medicine residents who spend two weeks focused intently on the biological questions underlying a patient’s illness, with the hope of identifying key scientific questions that, if answered, could point the way to improved diagnosis and treatment.  Like most clinical cases discussions, the conference presentation is oriented around the patient, starting with a review of how the patient came to be in the hospital, and generally ending with a discussion of the patient’s hospital course and anticipated trajectory.  In between these essential bookends, there’s invariably an intriguing discussion around the often-mysterious biology underlying a patient’s illness.

By design, the team focuses on complex or confusing cases – at times, it feels like it might be called the “idiopathic conference” since many of the diagnoses featured tend to fall into this category (“idiopathic” is the fancy medical term for “we don’t understand what’s going on”). 

Unfailingly, these deep dives into biology expose a central truth that has long impressed me about medicine: how little we still understand about how the body works, about what causes many diseases, and how limited are our arsenal of tools to diagnose and effectively, precisely treat many ailments.  I felt this profoundly when I was a medical student seeing patients on the wards at MGH, and again when I was an internal medicine resident and later endocrinology fellow at MGH.  I continue to experience these exact emotions today.

When you look at the examples of progress in medicine and biology, the list is impressive.  The modalities available to diagnose and treat disease continues to expand, while the terrible diseases that we’ve learned to prevent (like polio) or more effectively manage (like cystic fibrosis) speak uncontestably to the promise of science. 

Yet when you take your measure by your ability (or lack of ability) to diagnosis and treat the patient in front of you – much less prevent the disease from occurring in the first place – you are quickly overwhelmed by profound humility. 

But you also feel something else.  Not only do you leave these conferences with a deep appreciation for the limitations of our existing knowledge, you invariably emerge with a palpable sense of urgency, a determination that we must do better, and drive harder to ensure that nascent technologies, both biological and digital, are brought to bear, relentlessly as well as thoughtfully, in service of patients.

Level-setting expectations for emerging tech

If the MGH Pathway conferences regularly remind me of how urgently improved diagnostics and therapeutics are required, recent events have reminded us how difficult the journey will be.

Several recent examples stand out:

Precision Medicine

Consider this question from Andreessen-Horowitz general partner and Bio+Health lead Vijay Pande, during his podcast interview of Olga Troyanskaya, a computer scientist and geneticist at Princeton. 

With remarkable candor, Pande asks,

“There was so much excitement about precision medicine with genomics, right? I think the idea was that, okay, we could sequence a patient and maybe sequence a tumor and from that we would be able to do so much. We’d be able to figure out which cancer drug to give or which drug in general to give. And that hasn’t quite come to fruition yet, it feels like — or maybe it has happened so gradually that we’ve lost track. What’s your take on precision medicine?”

The idea that precision medicine hasn’t quite lived up to the admittedly extravagant expectations is a familiar perspective on the wards and in lab, but to hear it from within the reliably exultant investment community feels like real progress.

Olga Troyanskya, professor, Princeton University

Troyanskaya’s response offers similar candor.  While pointing out that “we have seen a lot of successes,” she also recognizes that “a lot of it was hype,” adding “None of us, I think, quite appreciated quite how complex it is. We sort of thought we’ll find these few genes, they’re the drivers, we’ll develop targeted therapies, we’re done.”

To be sure, she points out, there are “true revolutionary successes of precision medicine.” She cites our increasingly sophisticated approach to breast cancer and some subtypes of lung cancer as two examples.  

But, Troyanskaya adds, “What I think we underestimated is that it’s not enough to actually just know the genome, that it’s not, you know, it’s not just a few causal genes.”  We also need to understand the non-coding 98% of the genome, she says, and to capture other types of data, including proteomics and metabolomics.

AI for Drug Discovery

Coming up with impactful new medicines is remarkably difficult, and the idea that AI can improve the odds offers obvious appeal. 

But at least some of the bloom is off the rose, as it’s now clear that many of the so-called “AI discovered” drugs have not made it through development. 

“The first AI-designed drugs have ended with disappointment,” writes Andrew Dunn in Endpoints. 

He continues,

“Over the last year-plus, the first handful of molecules created by artificial intelligence have failed trials or been deprioritized. The AI companies behind these drugs brought them into the clinic full of fanfare about a new age of drug discovery — and have quietly shelved them after learning old lessons about how hard pharmaceutical R&D can be.”

He also points out that while the last few years have been notoriously challenging for small biotechnology companies (the XBI biotechnology stock index has dropped by 50% over the last two years), it’s been even worse for companies focused on AI drug discovery, dropping by 75-90% over the same time.  

[Chart courtesy of A. Dunn, Endpoints News]

(It’s also been a challenging time for digital health, see this report from Rock Health, and for healthtech – see this report from Bessemer Venture Partners. Interestingly, investor focus in these areas seems to be shifting from the aspiration of radically disrupting healthcare and biopharma to the appreciably more modest goal of making existing processes, especially non-clinical or back-office, somewhat more efficient.)

As many commentators have pointed out, the obsession with “AI drugs” is a little silly, in that AI is a tool that may inform a complicated, multi-step process. Moreover, a range of computational techniques are often involved in the development of medicines. Thus, the idea that one product is an “AI drug” and another isn’t feels more than a little arbitrary.

There’s also the issue of expectations – even if AI is able to contribute to the efficiency of early activities in drug discovery, at best this might only improve the overall process slightly given the multiple downstream hurdles that remain (especially since it’s not clear, as we’ve discussed, that AI can offer particularly useful predictions around late phase clinical studies, where most of the time and dollars are spent). 

VC Patrick Malone (cited by Dunn) may be exactly right when he says,

“If you take the hype and PR at face value over the last 10 years, you would think [the probability of creating a successful drug] goes from 5% to 90%. But if you know how these models work, it goes from 5% to maybe 6% or 7%.”

In a separate post on LinkedIn, Malone (citing Leonard Wossnig, Chief Technology Officer at LabGenius) riffs on a key underappreciated challenge in drug discovery, particularly drug discovery that involves algorithms: figuring out what to optimize for – the so-called “objective function.” 

For instance, you might try to identify a compound that binds incredibly tightly to a specific receptor, say. Yet it turns out that in many cases, such an approach isn’t quite right – empirically, you may discover you need a molecule that binds with moderate affinity to multiple receptors. (I’ve discussed this nuance in the context of phenotypic screening and the discovery of olanzapine, a medicine for schizophrenia, for example; see also this captivating recent commentary about phenotypic drug discovery, penned by researchers at Pfizer).    

Generative AI in the Enterprise

Generative AI captured our collective attention with the November 2022 release of ChatGPT, as this column has frequently discussed.

Despite the insistence of consultants that if your company is not already leveraging generative AI, it’s behind, I’ve not seen many examples of generative AI in actual use at large companies, particularly biopharmas.

It turns out, this perception is well-founded.  According to Andreessen-Horowitz co-founder Ben Horowitz – a great champion of AI – “We haven’t seen anybody [involved in generative AI] with any traction in the enterprise.”  (“Enterprise” in this context refers to the information technology systems and processes used by large companies, like pharmas.) 

According to the technologist Horowitz was interviewing — Ali Ghodsi, co-founder and CEO of the big data analytics company, Databricks – there are at least four reasons generative AI hasn’t yet entered the enterprise:

  • Big companies invariably move slowly and cautiously.
  • Corporations are terrified that they will lose control of high-value proprietary data.
  • Companies typically need output that is exactly right, not sort of right.
  • There is often a “food fight” (as Ghodsi puts it) at companies about which division controls generative AI.

From what I’ve seen across our industry to date, I can only say that Ghodsi seems to deeply understand his enterprise customer base.

Ali Ghodsi, co-founder and CEO, DataBricks

I was also struck by how Ghodsi – whose business embraces and enables generative AI – readily acknowledged the limitations of this technology.  “It’s stupid and it makes mistakes,” Ghodsi says, adding “it quickly becomes clear that you need a human in the loop. You need to augment it with human. Look, there’s no way you can just let this thing loose right now.” (The need for a human in the loop was also a key assertion Harvard’s Zak Kohane made in his recently-published book about ChatGPT and medicine – see here.)

Ghodsi was also skeptical about performance benchmarks cited for generative AI – including around their supposedly high scores on medical examinations.  He suggested that the models might have picked up the test questions during the course of their training (training that’s shrouded in mystery, as Harvard’s Kohane has pointed out), and thus, their high scores may be deceptive — like an exam where you got the answers the night before.

To be sure, Ghodsi remains excited about the potential of generative AI; he’s just realistic about its present limitations.

III – BioTechno-Optimism?

The talk of tech these days is the latest manifesto penned by Andreessen-Horowitz co-founder, Marc Andreessen, this time on “Techno-Optimism.” In case you missed it, his simplified thesis is that technology=good, and anyone who would constrain it=bad.

The piece has triggered an energetic and largely critical response from writers like Ezra Klein in the New York Times (“buzzy, bizarre”), Steven Levy in Wired (“an over-the-top declaration of humanity’s destiny as a tech-empowered super species—Ayn Rand resurrected as a Substack author”), science fiction author Ted Chiang (“it’s mostly nonsense”), and Jemima Kelly of the Financial Times (“is the billionaire bitcoin-backing venture capitalist OK?”).

Levy’s analysis was perhaps the most resonant:

“[Andreessen] posits that technology is the key driver of human wealth and happiness. I have no problem with that. In fact, I too am a techno-optimist—or at least I was before I read this essay, which attaches toxic baggage to the term. It’s pretty darn obvious that things like air-conditioning, the internet, rocket ships, and electric light are safely in the ‘win’ column. As we enter the age of AI, I’m on the side that thinks that the benefits are well worth pursuing, even if it requires vigilance to ensure that the consequences won’t be disastrous.”

But if I’m being honest, I thought there was something visceral in Andreessen’s screed – or perhaps more accurately, in the impulse behind it — that really touched a chord for me, emphasizing both the value emerging technology can bring and the incredible challenge of nurturing new technologies through their growing pains (what Carlota Perez might describe as the Installation phase), to the point where the demonstrated value is unassailable (the Deployment phase). 

Not only is it intrinsically difficult to figure out how to implement new technology effectively, necessitating all sorts of incremental innovations (see here), but there’s exceptional resistance all along the way (at least outside of public relations, where new technology is championed relentlessly). 

Incumbents – particularly in healthcare — resist technology for years before eventually (perhaps) absorbing it.  In part this reflects inertia – we’re most comfortable with what we already know. 

Another factor, as I’ve written, and as Andreessen observes (with his usual bluster), involves the  precautionary principle.  Stakeholders (in my view) are appropriately concerned about doing harm with novel technologies, but routinely overlook the harm that’s done by inhibiting the adoption of promising new approaches.

Moreover, new technologies often bring significant uncertainties, as Dr. Paul Offit has nicely described in You Bet Your Life (my WSJ review here). This is true not only for new medical treatments, as Offit nicely discusses, but also in how high-stakes research is conducted. 

Consider a typical example from the world of clinical trials, the lifeblood of biopharma and academic clinical researchers.  Imagine that you hear about a new tech-enabled approach for a key aspect of this process — patient recruitment, say, or decentralized data collection, an approach which is claimed to work better than traditional methods. 

If you are leading a clinical development team in a pharma company, where everyone in the business is already breathing down your neck with advice and suggestions and telling you that you’re already behind and reminding you how important it is to be successful, the last thing on earth you want is to take on more risk. 

If you use established procedures, at least you know what you’re getting into.  But if you try a new approach (even an approach perhaps piloted under far more forgiving conditions), you could get yourself in deep trouble.  This actually happens.  Not surprisingly, faced with these sorts of decisions, many rational actors within pharma prefer to be “fast followers” rather than adventurous pioneers, a mindset Safi Bahcall has described in Loonshots (see my WSJ review here, and this additional, biotech-focused discussion in Forbes, here).   

Concern about assuming new risk (beyond intrinsic risk of new molecules) is also why Gregg Meyers, the Chief Digital and Technology Officer at Bristol-Myers Squibb, says “this is an industry that will not adopt something unless it is really 10x better than the way things are historically done.”  See also this generally accurate perspective on the challenges of selling innovative tech into pharma, written from perspective of a startup that’s developing the technology.

(Bio)Technology: Critical Enabler of Medical Progress

Ultimately, for me, it comes back to the patients we discuss every two weeks at MGH.  We need far better diagnostics, far better therapeutics, and we need to understand health and disease at a far more sophisticated level.

Historically, improved tools – the microscope, ultrasound, MRI, DNA sequencing – have radically redefined the way we understand and approach disease.  Today, emerging modalities and tools — like antibody-drug conjugates (which are enjoying a banner year), cell and gene therapy (enduring real growing pains but with demonstrated successes and tantalizing possibilities), spatial biology techniques (quite exciting), and of course artificial intelligence (which is getting more capable by the second) offer exceptional promise, even if in many cases we’re just beginning to contemplate how to leverage these new technologies effectively.  Safety and ethics – appropriately — remain paramount consideration as well; see my recent discussion of Ziad Obermeyer’s important research revealing hidden bias in medical algorithms, and my WSJ review of Brian Christian’s essential The Alignment Problem, here.

While I disagree with Andreessen’s denunciation of skeptics and naysayers, I feel his frustration, and share his belief that technology (guided, I would add, by inquisitive researchers, practitioners, and other “lead users” who are passionate, thoughtful, and deeply empathetic) offers the most promising path forward in our drive to improve the human condition in the area of health and medicine.

Sometimes, it seems, we get so caught up in our clever criticism and overwrought angst that we get in our own way, stall our own momentum, and impede the progress upon which the health of our patients, and of our future patients, will so critically depend.


Catching Cancer Early, Investing in Data and AI: Jeff Huber on The Long Run

Today’s guest on The Long Run is Jeff Huber.

Jeff Huber, co-founder and general partner, Triatomic Capital

He’s the co-founder and general partner at Triatomic Capital. It’s a relatively new venture fund that seeks to back “century-defining” businesses in Engineered Biology, New Materials, Next Gen Compute, New Energy, & Automated Economy.

Jeff and his fellow partners are looking at startups that collect and analyze data to address big challenges of the 21st century, where deep learning / AI could be useful.  

Jeff was the founding CEO of Grail, the company that sequences DNA from blood samples to detect early signs for 50 different types of cancer. Before that, he had a long and storied career in Silicon Valley, including 15 years at Google. Jeff has his fingerprints all over some of the most common apps that Google developed in those rapid growth years, and which are now used by people around the world every day.

Jeff has certainly come a long way. He grew up on a family farm in northwest Illinois, about 20 miles away from where I grew up. I had no idea we had this much in common until we were hiking together on the trails of the Himalayas in 2022, on a fundraising expedition for the Fred Hutchinson Cancer Center.

In this conversation, Jeff expanded on some of those early life experiences and how they affected his life and career trajectory.

Part of Jeff’s story involves personal tragedy. His first wife, Laura, died of cancer in 2015. This experience was part of what motivated him to work on Grail, to help more people detect cancer earlier. Although we didn’t discuss it during this conversation, I should note that Jeff is now happily re-married and has a blended family. I asked some about his oldest daughter Grace because I got to know her on that expedition to Nepal.

This conversation ran longer than usual, but I think it will be absorbing for people thinking about the intersection of tech and biotech, where so much potential for innovation resides.

Now, please join me and Jeff Huber on The Long Run.


Fulfilling the Promise of Sickle Cell Gene Therapy

Dr. Jingyi Liu, clinical fellow in medicine, Brigham & Women’s Hospital

Two gene therapies for sickle cell disease, CRISPR/Vertex’s exa-cel and Bluebird Bio’s lovo-cel, are being reviewed by the FDA and are likely to win US approval for sale by the end of the year.

While it will take more time to fully establish long-term safety and efficacy of these therapies, they offer a glimmer of hope for a cure.

This is the culmination of a decades-long scientific journey. In 1957, molecular biologist Vernon Ingram’s discovery revealed the genetic basis of sickle cell disease — a single amino acid substitution in the hemoglobin beta chain. Since then, investments in sickle cell disease research trailed behind other genetic disorders, resulting in limited progress and treatment options. Current treatments like hydroxyurea, Endari, Adakveo, and Voxelotor manage but don’t cure sickle cell disease, leaving patients with an average life expectancy of just 50 years. Bone marrow transplants offer a cure, but they come with life-threatening risks.

Exa-cel and lovo-cel offer the potential for transformative change. These therapies target the genetic root of the disease, potentially functioning as cures that would alleviate fatigue, excruciating pain crises and cardiovascular complications.

Recent data releases suggest that these therapies are breakthroughs from standard of care.  31 of the 32 evaluable patients who had received lovo-cel had resolution of severe vaso-occlusive crises through 24 months of follow-up. Similarly, 16 of 17 evaluable patients who had received exa-cel were free of severe vaso-occlusive crises for at least 12 consecutive months.  For context, the median number of severe vaso-occlusive events among these patients in the 2 years preceding enrollment was 3 to 4 events per year.

However, approval is only the initial step towards realizing the promise of sickle cell gene therapy.

Physicians and patients are about to confront some thorny issues around cost and access. Prices are sure to be in the sticker-shock range of $1 million and up.

Sick Cells, a patient advocacy group, asks a pressing question: “Will eligible patients have full access to coverage to utilize these therapies?”

Historical barriers to care for sickle cell disease are complex, encompassing socioeconomic, geographic, informational, and racial factors. About 100,000 people in the US are thought to have sickle cell disease, but only one in four patients has access to standard oral medications.

The cost of sickle cell gene therapy will be substantially higher than with oral pills. Gene therapy will also require substantially more monitoring and support. Patients must commit to a multi-month process that involves hospitalization, chemotherapy, and an extensive recovery process. While there is the potential for a long-term cure with gene therapy, this isn’t exactly as simple as a one-and-done procedure.

Through interviews with the SCD community, including patient groups, physicians and payors, I compiled a few ideas on how to improve the accessibility of sickle cell gene therapy post FDA approval:

  1. Care Navigation and Coverage of Supportive Services. Individuals undergoing gene therapy treatment bear significant physical, mental and financial burdens during the treatment course. Patients and physicians in the sickle cell disease community believe that that addressing associated challenges like fertility preservation, mental health support, transportation and lost income during the treatment process are vital to helping patients complete the gene therapy treatment course. According to Sick Cells, “A key factor to accessing gene therapies will be the consideration of wrap-around services such as mental health services, transportation, and fertility preservation, which at this time, is still not accessible to many. Although there are organizations dedicated to ensuring access to fertility preservation for sickle cell patients, those resources are not yet widespread.” To navigate the multidisciplinary care pathway required for gene therapy, sickle cell disease patients also mentioned the importance of care coordination programs staffed with case managers and patient advocates. Historically, care coordination programs for rare diseases have improved outcomes and been cost effective. Coordinators can also help streamline the administrative burdens of applying for gene therapy through developing prior authorization standard operating procedures.
  2. Establish National Coverage Standards. Another potential hindrance to gene therapy access may be varying criteria set by different payors. Most individuals with sickle cell disease receive healthcare through disabled or . While Medicaid is mandated to cover all therapies (unlike commercial payors), each state’s Medicaid agency establishes its own eligibility criteria. This variation in coverage is already present for bone marrow transplants and will likely continue for sickle cell disease gene therapy. Dr. Mohammad Dar, Senior Medical Director at MassHealth (Massachusetts’ Medicaid) says that establishing a national perspective for evaluating and accessing rare therapies can offer a more equitable solution for access. A national eligibility standard would be particularly beneficial for patients residing in states that have historically enacted stricter eligibility criteria and to prevent undue financial burdens on individual states. For rare and costly therapies such as sickle cell gene therapy, federal legislation payment policy and support, and on access eligibility criteria may be the most equitable solution.
  3. Data, Data, Data. Unsurprisingly, the sickle cell disease community yearns for more extensive, long-term follow up data concerning the safety and efficacy of gene therapy from the initial pivotal trials. Patients seek reassurance against the risk of secondary malignancy. Additionally, anticipation builds for data from upcoming trials in expanded patient populations. More concrete data from expanded populations will help payers widen the circle of who they can approve the medication for in otherwise gray spaces.

As we celebrate the imminent approval of sickle cell gene therapy, it’s vital to remember that the true relevance of this milestone will depend on what comes next – how many patients receive the treatment and its benefits. If some of these roadblocks aren’t smoothed out, it could create more healthcare disparities, said Dr. Maureen Achebe, a hematologist at the Brigham and Women’s Hospital and the Dana Farber Cancer Institute. 

Science has made great advances against sickle cell disease. But realizing the potential of gene therapy for sickle cell disease will require a fundamental overhaul of our healthcare system’s approach to financing and distributing these groundbreaking treatments. We have more work to do to fulfill the promise of sickle cell gene therapy for eligible patients.


  • Dr. Achebe’s opinions do not necessarily reflect those of the Brigham and Women’s Hospital or the Dana Farber Cancer Institute.
  • Dr. Achebe has been a consultant for GBT (Pfizer), Fulcrum Therapeutics, Forma Therapeutics.
  • Dr. Dar’s opinions do not necessarily reflect those of any Medicaid or other government agency or any provider system.

Targeted Radiation for Cancer: Lori Lyons-Williams on The Long Run

Today’s guest on The Long Run is Lori Lyons-Williams.

Lori Lyons-Williams, CEO, Abdera Therapeutics

She’s the CEO of Menlo Park, Calif. and Vancouver, BC-based Abdera Therapeutics.

Abdera is a startup developing targeted antibody drugs loaded with potent microdoses of radiation to give them extra tumor-killing punch. This type of treatment modality has been around for decades. Targeted radiation hasn’t lived up to the full potential scientists have long imagined, but Abdera is part of an emerging crop of new companies that are working through some the classic technical challenges. Abdera announced a $142 million combined Series A and B financing, which I wrote about at the time for subscribers of Timmerman Report.

In this conversation, we talk about Lori’s early career experiences in sales and marketing, a couple key turning points for her growth, the opportunity she sees for targeted radiopharmaceuticals as an emerging class of therapy – especially for tough-to-treat solid tumors. Toward the end, she has some advice for women in biotech.

Now, before we get started, a word from the sponsor.

In the 10 years it takes for a new biopharmaceutical or device to be developed, more than 304 million people die waiting for treatment. Don’t settle for slow, ineffective patient engagement — not when people are counting on you! Elligo Health Research gives you immediate access to known, diverse patients so you can quickly get your product to the people who need it. Go to ElligoHealthResearch.com and get the patients you need, right now. Because they’ve waited long enough.

Learn more at Elligohealthresearch.com


Now, please join me and Lori Lyons-Williams on The Long Run.


Turning the Tables: Rob Perez Interviews Me on The Long Run

This episode is unusual.

Rob Perez, operating partner, General Atlantic; founder, Life Science Cares

Many listeners know of Rob Perez. He’s an operating partner at General Atlantic, the former CEO of Cubist Pharmaceuticals, and the founder of Life Science Cares.

Rob and I have gotten to know each other better the past couple years through our shared passion for mobilizing the biotech community to fight poverty. The Timmerman Traverse for Life Science Cares campaigns have raised $2.9 million over the past three years.

Luke Timmerman, founder & editor, Timmerman Report

Longtime listeners may recall Rob was a guest on this show five years ago, and he spoke about Life Science Cares then. This time around, Rob wanted to turn the tables. He asked the questions, and I was the guest.

We discuss how I grew up on a small family farm in southwestern Wisconsin, some early career influences in newspapers, and how I adapted to the market forces that upended journalism in the 21st century. Those experiences all combined to lay the foundation for this new chapter as both journalist and social entrepreneur.

Now, before we get started, a word from the sponsors.

With Elligo Health Research, a proud sponsor of The Long Run, clinical trial patient engagement takes just two simple steps. First, Elligo searches through their network with access to millions of diverse patients — who are pre-vetted for protocol inclusion through HIPAA-compliant identified healthcare data — to determine the best intersection for optimized engagement. Second, Elligo collaborates with you to assess feasibility, ensuring your protocol works in a real-world healthcare environment so you easily get the patients you need when you need them.

Learn more at ElligoHealthResearch.com


Look forward to face-to-face interactions this October at the BIO Investor Forum—or BIF—in San Francisco.

Accessible and intimate, this conference is designed to accelerate growth within the biopharma industry. BIO’s One-on-one partnering system seamlessly brings together emerging biotech companies with industry partners, investors, and bankers.

Don’t miss your chance to network, learn from experts about what’s on the horizon for biotech, and pitch your unique story to potential partners. Held October 17th and 18th at the Hilton San Francisco Union Square.

Register Now at bio.org/bif

Now, please join me and Rob Perez on The Long Run.


Sterile Information: Early Forecasting Not The Answer To R&D Productivity Woes

David Shaywitz

Two recent Wall Street Journal deep-dives nicely bookend a critical, and unresolved, tension faced by large pharmaceutical companies: how can their R&D organizations discover, develop, and deliver the new medicines patients await, and the growth and return on investment that shareholders demand?

Early this year, I discussed an April 2023 profile of Lilly by journalist Peter Loftus, who described how the company, led by a physician-scientist named Daniel Skovronsky, revitalized Lilly’s R&D, after a decade of documented struggles. 

Skovronsky’s pivotal insight, as I wrote, was “recognizing that key decisions were being driven by commercial needs, rather than the best science.  Marginal products were advanced (only to later fail) because they targeted a specific commercial need.”

Poor decision making driven by non-scientific considerations – particularly commercial desiderata—is a frequently-encountered challenge in drug development, as VC David Grainger, in particular, has highlighted (see here).

But other large pharma companies (I suspect many if not most large pharma companies) are concerned that they’ve provided excessive latitude to their R&D organizations, rather than insisting on a greater commercial focus.

Wishful Thinking

For instance, last week, Journal reporter Jared Hopkins described how Novartis’s CEO had decided the company’s early efforts were excessively driven by science, and needed a greater emphasis on commercial prospects.  Consequently, future sales will now be forecast for products before they enter clinical trials, Hopkins wrote.

Associated with this change in emphasis, the early research arm of Novartis, the Novartis Institutes for BioMedical Research (NIBR), “will soon simply be known as Novartis Biomedical Research,” according to STAT.

The new Novartis mindset contrasts, directly, with the approach instituted by former NIBR head Mark Fishman, who deliberately excluded such forecasts from early-stage decision-making (as I discussed a decade ago in Forbes).  A decade later, Novartis brass clearly became concerned that NIBR had become “too academic” – pursuing interesting questions without sufficient regard for commercial applications.

While appealing in theory, Novartis’s proposed remedy – essentially, use early commercial forecasting to “pick winners” – makes sense only if you have the actual ability to do this.  Otherwise, you have just an exercise in false, if comforting, precision. 

A classic study by BCG found that there was essentially no correlation between predicted peak sales for a drug at the time it was approved (i.e. with complete phase 3 data) and actual peak sales.  Several years later, as I discussed in Forbes, McKinsey confirmed the fragility of sales forecasts.  Consequently, the notion that one can predict sales before human studies in any meaningful way is purely wishful thinking – the sort of “sterile information” (to use Nassim Taleb’s term) that corporate planners typically love, but which is functionally worthless.

As Taleb testified before the Financial Services subcommittee of the U.S. House of Representatives in 2014,

“Some may use the argument about predicting risks equal or better than nothing; using arguments like ‘we are aware of the limits.’  Risk measurement and prediction —any prediction — has side effects of increasing risk-taking, even by those who know that they are not reliable. We have ample evidence of so called ‘anchoring’ in the calibration of decisions. Information, even when it is known to be sterile, increases overconfidence.”

Good Translational Models

How to pick projects, then? 

One view – as analyst Jack Scannell has argued, as I discussed here — is to prioritize areas with good translational models, as slightly better models contribute more impactfully to the odds of success than increasing by several orders of magnitude the number of molecules screened (which is the canonical large pharma approach).  In fact, Scannell describes this excessive reliance on scale vs smarts as the “mis-industrialization” of science, and this description seems spot on.  (Taleb and I made a similar point in a 2008 commentary in the Financial Timeshere.)

Focusing on areas with good translational models is explicitly the approach Vertex is taking, and arguably Regeneron as well.  Both Vertex and Regeneron are compelling (and remarkably rare) examples of established drug discovery companies that are still dominated by their powerful R&D cultures; more commonly, the business of running a drug development company is considered too complex to be left to the scientists (in the same way that running a hospital is increasingly considered too complex to be left in the hands of doctors, rather than corporate managers).  It’s also true that Vertex and Regeneron are both comparatively small; Regeneron, the larger of the two, employs about only about a tenth the number of people that Novartis does, for example.

Without question, there are many areas of biopharma that arguably benefit from the rigor of traditional corporate industrialization – in particular, the focus on consistent, repeatable processes that operate on a global scale.  Manufacturing, engagement with regulators, sales and marketing, even late phase clinical trials largely fall into this category, and tend to be areas that big pharmas do particularly well, and with which corporate executives are fairly comfortable. 

Process-driven Mindset vs. Novel Discovery

But coming up with new products?  Not so much. 

While pharma companies aspire to develop new products with a range of defined characteristics – the so-called “target product profile” – you generally can’t discover on command. 

Moreover, many commercially successful products were developed without any initial interest or support from commercial teams.  Merck (see here) was days away from out-licensing pembrolizumab (Keytruda), a drug that had been kept alive by researchers despite the best efforts of successive management to stop the program.  Similarly, when Millenium acquired Leukosite, they had no idea that the transaction included a molecule, bortezomib, that would become the most important commercial product in their portfolio.

Big pharmas are built around establishing repeatable processes…but there simply isn’t a playbook for serial creative success.”

Big pharma brings considerable resources to discovery, as well as an extremely high degree of rigor, and often an exceptional amount of implicit knowledge, particular in areas like medicinal chemistry.  But large pharma (as Safi Bahcall has nicely described – see here and here) also tend to be exceptionally bureaucratic and process-driven, and I’m not optimistic you can process and administrate your way to creativity and insight.  I’d argue the process-driven mindset that serve other aspects of drug development and delivery so effectively (enabling the distribution of safe and effective medicines to patients around the world) have, on balance, an adverse impact on novel discovery.  Moreover, for all the efforts of big pharma to enable research groups that are more “biotech-like,” the incentives and cultures of most large corporations just doesn’t seem to allow for this, in practice.

This isn’t an argument for aimless discovery research.  Rather, it seems inevitable that an ever-greater number of future first-in-class medicines will come from the focused and high-performing teams in the start-up world.  Big pharmas are built around establishing repeatable processes, and driving incremental refinements.  But original discovery – to the dismay of most large pharma – often isn’t about turning the crank faster. 

Rather, originality in drug discovery, as in other areas, requires, in addition to an intended destination, a high degree of agility, freedom, and imagination; the capacity to embrace and live with uncertainty, rather than obscure it with sterile forecasts; and the humility and ambition to imagine a future different from and beyond what we might anticipate, and extrapolate to, today.  Perhaps most difficult for large, process-driven corporations, it requires a recognition that there simply isn’t a playbook for serial creative success.  These are principles that exceptional leaders like Ed Catmull, the CEO of Pixar in its heyday, have intuited, and that most large corporate cultures struggle to sustain.

Recent Astounding HealthTech columns on Pharma R&D


Freeing the Biotech Founders: Zach Weinberg and Alexis Borisy on The Long Run

Today there are two guests on The Long Run: Zach Weinberg and Alexis Borisy.

Zach Weinberg, co-founder, CEO, Curie Bio

They are co-founders of Curie.Bio.

Curie is a venture capital fund with $520 million, mostly for seed investments and Series A rounds in biotech startups. It also is also building up in-house R&D expertise which it uses to help the entrepreneurs it backs.

Curie pitches itself as different from other venture firms partly because it allows the CEO/founders it backs to hold onto a greater percentage of ownership than traditional VCs have been willing to hand over. Curie also says it wants to allow entrepreneurs to retain more control over decision-making. This boils down to a battle cry of ‘Free the Founders.’

Alexis Borisy, co-founder, operating chairman, Curie Bio

They are tapping into the zeitgeist. Many biotech entrepreneurs feel they sweat bullets for years, shouldering too much of the hard work and risk, without reaping enough of the rewards.

It’s still early days for Curie Bio, but this is conversation worth having about the terms of engagement in biotech startups.

Now, before we get started, a word from the sponsors.

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Visit ElligoHealthResearch.com to get started.


One of the most respected, independent investor events is returning to San Francisco! In person for the first time since 2019, the BIO Investor Forum will be held on October 17th and 18th at the Hilton San Francisco Union Square. The conference showcases drug development programs that are ready for partnering or venture funding.

Enjoy limitless networking, on-point sessions crafted by impressive industry experts, polished company presentations, and making important connections powered by BIO One-on-One PartneringTM.

Register Now at bio.org/bif


Now, please join me and Zach Weinberg and Alexis Borisy on The Long Run.


A Return to Kilimanjaro: Timmerman Traverse for Damon Runyon Cancer Research

Welcome to the next big biotech expedition — Timmerman Traverse for Damon Runyon Cancer Research Foundation.

A team of executives, investors, and scientists are coming together on Kilimanjaro, the highest peak in Africa, in February 2024.

We’re on a mission to raise $1 million for young scientists across the US.

Damon Runyon gives promising young researchers the freedom to pursue brave and bold ideas. Its grant recipients have opened up entire new fields with targeted therapies, CRISPR gene editing, and cancer immunotherapy.

The biotech community knows the value of this work and is stepping up to support it.

Who’s on the Team?

I’m happy to announce that PPD Biotech Solutions, part of Thermo Fisher Scientific, is our lead sponsor.




Corporate sponsorship opportunities are available for this special expedition for cancer research. See Elyse Hoffmann, senior director of partnership strategy at Damon Runyon, to brainstorm ideas. elyse.hoffmann@damonrunyon.org.

Please see our team fundraising page to learn more and donate.

We’re pushing ourselves to do something difficult. We’ll enjoy Mother Nature. We’ll form meaningful relationships on the trails.

We’re going to make a positive impact for young scientists.

Thank you for your support!

Timmerman Traverse