David Shaywitz

As we head into Thanksgiving, I wanted to share a story that highlights the promise and possibility that can emerge from a devastating diagnosis, and emphasizes what can happen when industry, academia, and — especially — impassioned parents and advocates join forces.

Consider the devasting rare genetic disease, spinal muscular atrophy, or SMA.

According to Cure SMA, SMA is “a progressive neurodegenerative disease that affects the motor nerve cells in the spinal cord and impacts the muscles used for activities such as breathing, eating, crawling, and walking.”  It is caused by mutations affecting the SMN1 gene, which is critical for motor neuron survival and function.

Today, there are several treatments (not quite cures) available for SMA patients, approaches that can be highly impactful in some patients, particularly if administered early in the course of the disease. 

The parents of one child with SMA have been particularly involved in driving the development of treatments: Dinakar Singh and Loren Eng, both high-powered investors whose two-year-old daughter Arya was diagnosed with SMA in 2002. The couple helped established the SMA Foundation the following year. The foundation went to work finding the most promising science in the SMA field, and ultimately securing $150 million to support basic, translational, and clinical research.

Loren Eng, president, SMA Foundation

Their drive to help their daughter was relentless.

As Loren Eng described in this piece from the Stanford Graduate School of Business magazine:

Arya had a milder form of the disease, which meant she would probably survive early childhood. But with no treatment in sight, her life would be a hellish series of hospitalizations and painful, relentless physical attrition. “The doctor said she might live to finish high school,” Eng recalls.

Eng devoted herself to changing that outcome.

As her parents and the SMA foundation fought to accelerate the development of effective medicines, Arya’s condition worsened.

By the time she was five, Arya was in a wheelchair. Each succeeding year brought new challenges as her physical capacity diminished, and the effects of her condition led to serious, sometimes life-threatening problems. As the muscles in her chest weakened, Arya lost the ability to cough, which is critical for clearing the airway during a respiratory illness. As a result, common colds could turn into pneumonia, a leading cause of death among people with SMA. “I missed tons of school because every time I got a cold, it would turn into two weeks of respiratory therapy,” Arya says.

Gradually, her muscles weakened so much that they could no longer hold bones in place. Her hips dislocated. Scoliosis twisted her spine; orthopedic deformities developed throughout her body, requiring multiple corrective surgeries. Pain shadowed her constantly.

Over time, the science advocated by the SMA Foundation advanced, and by the time she was 11, Arya was the second subject in a clinical trial for a SMA medicine. The drug candidate, nusinersen (Spinraza) is an antisense oligonucleotide aimed at the SMN2 gene, a “backup” gene that usually produces only small amounts of SMN because of alternative splicing that results in a truncated non-functional form of the protein. 

Nusinersen (developed by Carlsbad, Calif.-based Ionis Pharmaceuticals and Biogen) works by modulating splicing of SMN2 pre-mRNA, increasing the amount of full-length SMN protein that’s produced. The drug required regular injections into the spinal cord, but miraculously, seemed to halt the inexorable progression of disease in Arya. Her condition stabilized. 

Occasionally, the grueling regimen of operating-room visits and spinal injections tested Arya’s resolve. She was a little girl, and this was not how little girls were supposed to live. When she was scheduled to receive a dose on her 12th birthday, Arya broke down in tears and declared to her mother, “This is the worst birthday ever!” Eng tried to console her. “This is the best birthday present you will ever get,” she told Arya. Recalling the moment, Arya acknowledges that her mom was right. “I didn’t agree with her then, but I do now.”

She later participated in a clinical trial for a different drug, risdiplam (Evrysdi), a small molecule that also aims to increase the expression of full-length SMN2 pre-mRNA, and boost SMN production.  The mechanism of action of risdiplam (developed in a collaboration between South Plainfield, NJ-based PTC Therapeutics, the SMA Foundation, and Roche) is somewhat different than that of nusinersen. 

Most importantly, the drug can be taken as an oral pill. Arya subsequently switched to that medicine, which obviated the need for spinal cord injections. She was not eligible for a third medicine, Zolgensma (a gene therapy approach developed by Chicago-based AveXis, later acquired by Novartis), as it was approved only for much younger patients.

“Thankfully, Arya’s mind and heart have not been touched by SMA,” Dr. Chung says, adding, “She has been a strong advocate for others with disabilities.”

Now, flash forward to this summer. 

Each week, the New York Times features a wedding in their Vows section, a detailed portrait of a particularly interesting couple getting hitched. On August 9, 2024, the featured bride, who had graduated from Yale in 2022, was Arya Singh.

David Shaywitz

“Does the crowd understand?
Is it East versus West
Or man against man?
Can any nation stand alone?”

Burning Heart, by Survivor – Rocky IV

In national politics, the culture wars may pit the coasts against the rest of the country.  In biotech, however, the AI culture war seems to pit the coasts against each other.

Consider this recent LinkedIn post by Chandana Haque, Executive Director of Recursion Pharmaceutical’s startup incubator, Altitude Labs (because they’re based in the mountains of Utah – get it?)

After back-to-back trips to Boston and SF this month, I’m reflecting on some key impressions about the differences between the coasts—especially when it comes to trends in early-stage biotech investing. 🚀

Where did Boston investors get excited? Core biotech: engineering cell machinery, new modalities, and regenerative medicine. This is their strength, and they know it well. But bring up machine learning, and you’ll see a shift; ML just isn’t on the radar for most of New England’s investment crowd, which sticks closely to traditional biotech.

In SF, it’s a different story. AI/ML is seen as critical to understanding complex biology, and it’s almost assumed that every startup will evaluate how ML fits into their approach. SF investors are also intrigued by ADCs, structural biology, and even spatial biology—areas where I found Boston often disengages. The West Coast’s interdisciplinary approach, mixing tech and bio, seems ingrained.

Time will tell how each approach shapes the field, but if you’re traveling coast-to-coast, you’ll feel the difference. What are you seeing?

In short, in the Bay Area, AI is what’s captivating everyone. This passion inevitably finds expression in the contemplation of biotechnology.

Chandana Haque

The attitude in Boston is, for the most part, rather more reserved. While there are obviously high-profile exceptions, including investors like Flagship Pioneering (which has embraced AI as tightly as any Bay Area fund), and young companies like nference, on balance I think Haque is spot on.

At a recent Boston area healthcare conference (which I can only discuss in general terms because it was held under Chatham House rules), I was struck by the code-switching I observed on a venture panel where a prominent West Coast investor at a firm championing the transformative potential of AI offered a conspicuously understated perspective.

It was not so much the substance but rather the emphasis and affect that was different from what I’ve heard him say in other contexts. Here, speaking to somewhat buttoned-up East Coast business audience, his tone was more guarded, his perspective more grounded, and the changes he foresaw more gradual. 

It’s also possible that in California, even VCs focused on biotech are bankrolled by limited partners who are captivated by the promise of technology and are drawn to VCs and startups that speak the language of radical transformation.

In Boston, the focus tends to be different – not least because of the overwhelming presence in Boston of biologists, physicians and other life science experts whose lived experience has reminded them (as it has reminded me) of the staggering complexity of biology and messiness and inherent humanity of medicine. 

Exhibit A for East Coast early-stage biotech investors (as it has been for many previous years) is the latest iteration of Atlas Venture’s annual Year In Review (watch it here), presented by Bruce Booth, summarizing the state of the biotech industry and ecosystem.

Bruce Booth, partner, Atlas Venture

As in previous years, the entire presentation should be required viewing for everyone in the life sciences.

Booth doesn’t spend all that much time on AI, but he calls it out as an area with “great investor interest.” The challenge, he says, is that because of the “incredible amount of buzz and hype” in this space, it’s been “difficult to figure out where the reality is.”

He points out that in 2014, “Recursion said they wanted 100 clinical programs in 10 years” (an aim that I imagine struck tech investors as admirably bold and biotech investors as hopelessly naïve). 

“Unfortunately,” Booth deadpans, “they missed that,” achieving only five clinical programs.

Yet even that represents an important achievement, Booth says, pointing out that “any biotech company that’s just ten years old that has five drugs in the clinic is actually incredibly productive.”

Booth believes AI isn’t just wispy and aspirational. “The reality is there,” he asserts. “AI and machine learning will have targeted impacts up and down the R&D process.”

Preclinical examples he cites include the use of AlphaFold to predict protein structure, and the potential of AI “to predict toxicities or create better routes for manufacturing drugs.”

On the development side of R&D, he suggests AI may contribute to patient selection and enrollment in clinical trials. Moreover, given the established capabilities of large language models, he thinks AI is also likely to provide assistance with regulatory documentation and writing.

But he struggles to find what might be termed West Coast conviction:

“Fundamentally, in the drug R&D space, given the complexity of human biology and the lack of massive datasets, we think that the impact of AI machine learning on R&D is going to be more likely around evolution than revolution. We’re not going to see drug discovery go from a multiyear process to just weeks or days.”

Consequently, Atlas plans to continue its existing (and, Booth says, proven) strategy of leveraging cutting-edge biomedical science to generate promising novel therapeutics.  If AI contributes to the development of a promising target or approach, they’re all for it, but they’re not looking to AI itself as the basis for a platform, nor do they see AI as the transformative unlock for biomedical discovery.

The Atlas view, perhaps not surprisingly, also represents the mindset of most senior pharma R&D executives that I’ve met. 

Yet, this skepticism may start to evolve if — or I suspect, when — AI’s impact is more palpably felt.

As I discussed in my last column, young physicians have quickly embraced an AI clinical app to help guide them through care of individual patients. This should serve as a reminder that while steps enabling change may be imperceptibly small, when change finally comes, it can be rapid. Before long, even big pharmas may find themselves persuaded to embrace their inner Californian.

Meanwhile, leading AI-first biotech companies seem to have become more aware of the essential expertise and implicit knowledge across a range of capabilities that veteran drug developers have accrued and are increasingly recognizing the importance of tapping into this vital experience, found in such abundance along the Charles River.

Perhaps, the next great biotech successfully integrating the approaches represented by the coasts will arise somewhere in the middle.

“If I can change,

And you change,

Everybody can change.”

Rocky Balboa, Rocky IV

David Shaywitz

In 2011, we were experiencing the ascension of technologies like the cloud and the smartphone.  Apps had become a thing: social network apps like Instagram (the iPhone “App of the Year” in 2011) and Twitter, utility apps like Evernote and Dropbox, navigation apps like Google Maps and Waze, and game apps like Angry Birds.

Yet in medicine, as I wrote that year in Forbes, the “killer app” was…a comparatively old-school e-textbook known as Up-To-Date. The company that created it was founded in 1992.

Written and reviewed by medical experts, Up-To-Date was where everyone in medicine, from earnest med students to overworked residents to seasoned clinicians, turned in the 2010s to find current, reputable information about medical conditions they required to most effectively care for their patients.

Ten years later, in 2021, Up-To-Date was still the go-to app; same for 2022 and 2023.

Yet today, this may be changing.  When a colleague recently mentioned that young doctors now seemed to be using an AI-based resource called “Open Evidence,” I was surprised and somewhat skeptical. 

But when I asked clinical colleagues who work with young doctors every day, I learned that the rumors seemed to be true. 

As UCSF Chief of Medicine Robert Wachter wrote on X, “I think [Open Evidence] is becoming go-to resource for residents. It handles complex case-based prompts, addresses clinical cases holistically, & really good references.”

Harvard clinical colleagues shared similar experiences; one told me the uptake has been “viral,” adding, “I’ve NEVER seen anything like this.”

I know that my academic colleagues will be examining closely both the use and the impact of Open Evidence, with particular emphasis on the effect on patient care. 

Lessons About Technology Adoption

For the biopharma-focused readers of TR, the Open Evidence example serves (or should serve) as a vivid reminder that things don’t change — until suddenly they do. A year ago, everyone was using Up-To-Date; today, many young doctors are embracing Open Evidence.

For emerging technologies, change tends to be driven by “lead users” (to use MIT professor Eric von Hippel’s term) – front-line workers who are focused on solving a pressing problem, and are glad to utilize whatever approach seems most effective. 

When you are a medical resident, your pressing problem is the overwhelming number of things you are dealing with, coming at you from everywhere, all at once. You desperately want to provide the best care to your patients, and you are motivated to turn whatever resource seems most useful. 

That Open Evidence seems to have met this threshold (at least for a number of early-career physicians) is strong testimony to its perceived value. Harried residents, presumably, are not using Open Evidence merely because they are curious about AI, or because there is a department initiative to utilize AI; they’re using it because they see the Open Evidence as the best solution for their problem.  It’s a tool that’s been adopted because of the palpable value it provides.

To these busy young doctors, AI through Open Evidence isn’t a proverbial “solution in search of a problem.” It’s a customized tool addressing their immediate, pressing needs.

There’s an analogy from the field of genetics. For years, I remember hearing endless criticism of physicians for their reluctance to leverage genetics in clinical practice; the urgent need to better educate clinicians in genetics was a familiar, oft-repeated plea.   

Yet, when a genetic diagnostic test (non-invasive prenatal testing, or NIPT) became available that could evaluate reliably specific fetal chromosomal abnormalities from a sample of peripheral blood, and in many cases obviate the need for an amniocentesis, the adoption was both rapid and widespread. Patients, doctors, and payors all seemed to embrace it – because the benefits were palpable.

Implications for AI in Pharma

Which brings us, predictably, back to AI in pharma.

In my last three pieces, I argued that:

• Picking winners consistently in R&D is a vexing challenge.
• Human causal biology, if leveraged rigorously and thoughtfully (eg Vertex, Regeneron) may be able to help.
• While many readers clearly remain skeptical, human causal biology, as leveraged by Vertex and Regeneron, is driving distinct value, and there will be further advantage to be found by thoughtfully leveraging AI.

Readers turned out to be even more skeptical about the application of AI to R&D than they were about the application of human genetics – and impassioned geneticists were often the most critical.   

As one reader (not from the Boston area, incidentally) and genetics enthusiast wrote,

I also think you are far too bullish on AI – I really dislike statements like: “Emerging technologies like AI will help improve scientific understanding and enable better decisions”. We have no idea yet exactly how transformative AI will (or will not) be, and professing with certainty that it will provide value fans the flames of hype that drive so many scam companies to slap a branded faceplate on GPT4 or raise money from VCs with not real vision beyond “AI+$$$$$=awesomeness”.

The AI Chasm in Pharma R&D

I appreciated the candor and perspective, which were certainly familiar, and speak to the sizeable chasm that exists in pharma R&D between AI optimists and skeptics.

On the pro-AI side, there seem to be two largely distinct cohorts: a small group of scientifically sophisticated enthusiasts who are really excited to explore the promise of AI across R&D, and a larger group of “digital transformers.”

Aviv Regev

The AI-curious scientists, from what I’ve seen, tend to have very little status and organizational clout  in most large pharmas, although there are exceptions (Aviv Regev at Genentech/Roche comes to mind).  More often, at best, they seem to be viewed as adorable (a word I’ve actually heard used by digital transformers).

The mission of the digital transformers is to execute broad corporate initiatives that are launched from the C-suite, driven by management consultants and focused on operational efficiency, typically assessed by near-term process metrics.  These organizational ambitions, invariably emphasizing the infusion of AI across the enterprise, are trumpeted by CEOs at Davos and by big pharma execs at industry conferences like HLTH.

But turning a means into an end can be problematic.  Goodhart’s Law (see here)observes that “When a measure becomes a target, it ceases to be a good measure.”  Similarly, when the mere use of AI becomes the goal, rather than a tool, the result can be a perfusion of performative AI and a dearth of thoughtful application to address the most critical problems a pharma faces: discovering and developing the next original, impactful medicine.

Consequently, it’s understandable why the vast majority of pharma R&D veterans remain generally skeptical about AI in R&D, since it seems to bear all the stigmata of The Next Great Corporate Initiative that needs to be endured in the process of actually doing great science and coming up with impactful new medicines.

The wild hype around AI doesn’t inspire confidence either.  While most startups aspire to lofty goals and tend to launch with brash promises, the extravagant expectations offered by AI startups may be in a league of their own. 

As industry chemist and distinguished “In the Pipeline” blogger Derek Lowe recently reminded readers, in 2014, Recursion Pharma “stated back then that they were going to develop 100 drugs in ten years” – an outlandish proposition that made it difficult for many experienced drug developers to take them seriously.

Derek Lowe

My concern is that understandable skepticism can easily bleed into reflexive cynicism (I’ve discussed the “cynicism trap” here), that might lead R&D teams to overlook early but authentically promising opportunities that could be truly transformative. 

It’s especially disappointing to me to sense some of this cynicism emanating from geneticists in particular, since at the time that many these geneticists were leaning into the tools and technologies of large-scale genetics, they were on the receiving end of critics who doubted the promise of the approach. 

A representative article, from Stephen S. Hall in Scientific American in 2010, was titled “Revolution Postponed: Why the Human Genome Project Has Been Disappointing.” 

The subheadline to Hall’s piece reads: “The Human Genome Project has failed so far to produce the medical miracles that scientists promised. Biologists are now divided over what, if anything, went wrong—and what needs to happen next.”

Yet over time, and with a huge amount of effort (and financial resources), the value of the Human Genome Project and related endeavors (like the UK Biobank) started (arguably) to prove themselves.  (See, for example, this 2020 article by Richard Gibbs.)

True, genetics has perhaps not lived up to some of the most hopeful early expectations (see the thoughtful comments of Princeton geneticist and computer scientist Olga Troyanskaya here), but by any reasonable estimation, the efforts have proved extraordinarily enabling for science, medicine, and biopharma R&D. 

Bottom Line

I expect AI will ultimately prove similarly transformative, and, when developed wisely and utilized thoughtfully, will be viewed as an essential tool for managing the burgeoning complexity of biopharma R&D. Less certain is when such palpably useful AI tools for advancing R&D science will start to arrive: this year? This decade?   

Like the young doctors now relying on Open Evidence, pharma R&D scientists may soon discover – perhaps sooner than you think – that the use of AI has become second nature for us, part of the fabric of our work, and we may wonder how we managed to survive so long without it.