David Shaywitz

Chris Gibson, the CEO and co-founder of Recursion Pharmaceuticals, may have captured the essence of the difference in mindset between tech VCs and life science VCs, writing in a superb recent article (a response to my previous TR post):

“The most consistent thread that differentiates tech and life science VCs is their willingness to project new data onto an industry and imagine resultant changes happening quickly.”

The operative word there is “quickly.” More on that in a minute.

To be sure, the issue isn’t whether or not VCs rooted in healthcare and life science can envision profound, revolutionary change, driven by technology. They see monumental changes coming to drug discovery, driven largely by widely available technologies (CRISPR gene editing, next-gen DNA sequencing, improved viral vectors, induced pluripotent stem cells, etc.)

In my summary of the 2018 JP Morgan healthcare conference I wrote:

“This feels like an unbelievable, almost magical time in biopharma – a colleague described it (in a good way) as science fiction coming to life. Biological technologies, approaches, and ambitions that might have been dismissed as fantasies only a few years ago now are part of the fabric of the industry.”

Century Therapeutics is just one example of a startup embracing several new technologies at once, with an eye toward creating new cell therapies. Century’s chief strategy officer Janelle Anderson discussed the confluence of factors on The Long Run podcast.

Janelle Anderson, chief strategy officer, Century Therapeutics

I reflected on the bold approach below:

“What’s apparent from Anderson’s description of Century, and from the work of other companies in the cell therapy space, is how incredibly audacious – and routinely audacious — biological engineering has become. Even the first-generation CAR-T approaches are astonishing, in that they introduce a genetically engineered fragment into a patient’s own cells – arguably an example of gene therapy, or at least a gene therapy-style technique. Then consider the approaches that Anderson describes – such as precise gene editing using CRISPR or similar techniques, as well as the use iPSCs; and it’s not just Century — aspects of these approaches are typical features of many company proposals my team and I evaluate. Any one of these elements would have been considered beyond fanciful back when I was training, or perhaps at best the sort of zany thing a radical biotech might contemplate. Today, these are the techniques and therapeutic approaches that most large pharmas are pursuing — aggressively.“

In short, life science VCs have no trouble envisioning truly transformational biological technologies. They routinely invest in companies they hope will radically improve the way disease is treated.

But of course, this isn’t what Gibson is saying.  He’s arguing that the revolutionary changes in computation that have radically changed so many other industries are about to radically change the ways drugs are discovered and developed. Recursion, specifically, is wagering on the usefulness of artificial intelligence to navigate some of the classic stumbling blocks in drug discovery. Given the enthusiasm so many life science VCs have for the types of advances listed above, Gibson wonders why so many life science VCs insist on remaining willfully oblivious to the coming AI wave.

At one level, I share much of Gibson’s long-term techno-optimism. New technologies have always driven the way science is done, providing powerful new approaches to investigating and better understanding nature, and revealing important new questions.   (See my recent discussion of the life cycle of technology revolutions here.)

But what sits less comfortably is the idea that VCs and their AI-wielding engineers have showed up to biopharma like the pros from Dover, expecting to “quickly” (to use Gibson’s word) transform drug discovery, then, presumably, move on to the next benighted industry.  (To be sure not to shoot the messenger here, let me emphasize Gibson has always impressed me as approaching his own work at Recursion with exceptional humility, and has consistently embraced a deliberate and long-term vision – listen to his interview this summer with Lisa Suennen and me on our Tech Tonics podcast, for example.)

The problem with thinking computation is poised to quickly change drug discovery and development rests partly in your definition of quickly. Does that mean next week, six months from now, or 5 years from now?  (My guess: much longer.)

There’s also the nagging question of where to begin on a problem as knotty as drug discovery. Many life science veterans would suggest that tech startups are focusing on relatively small aspects of relatively large and often quite wicked (as described by Hogarth, and later Epstein) problems. The engineers, while well-intentioned, often seem to lack a basic humility about the complexity of biology, the messiness of disease, and the extent to which complex human issues of health and illness often resist reduction to convenient bits and bytes.

Healthcare veterans, like many in biopharma, also feel like they’ve endured the promises of disruption from confident software entrepreneurs who are coming off of incredible successes in other domains, and, wanting to do good as well as do well, have decided to take their talents to healthcare. 

As former Facebook executive and newly-minted VC Chamath Palihapitiya explained in 2013: “Every interaction in every area related to health is just so sh*tty.  The software is crap, the services are crap, the people are crap. So there is a lot of value that people like us [i.e. engineers] can add because you have a very different perspective on how the system should work.” 

Unfortunately, while Palihapitiya is no longer a VC, healthcare has continued to persist pretty much unchanged, largely impervious to the disruptive efforts of Palihapitya and others.

Bob Kocher, a physician and VC at Venrock, puts it like this:

Bob Kocher

“Many entrepreneurs and non-healthcare investors look at healthcare and see a $3.3T sector growing faster than the rest of the economy using fax machines for communication, legacy software, and lots of expensive labor and can’t imagine that it cannot be disrupted quickly.  The thing they often fail to understand is that the economic incentives in healthcare may not reward the disruption or disruptor and that the bar for quality and reliability is super high since people’s lives are risk.”

Even so, and despite (and in a sense, because) of the many complexities associated with both drug discovery and with healthcare, I believe there’s a tremendous opportunity for emerging technologies to deliver profound impact.  Perhaps if some of these novel tech-driven approaches were introduced with a tad less triumphalism (the tidy manifestos, the “software is eating the world and now it’s time to eat healthcare” proclamations, the “prepare to welcome your new tech overlords” sensibility permeating so many of these endeavors) and a greater sense of authentic inquisitive engagement, it would be so much more constructive, and more effective.

I’m not sure the way technology changes everything is by arriving and saying “it’s time to change everything.” 

An enduring challenge – as I’ve discussed here and elsewhere – involves implementation, figuring out how to thoughtfully leverage technology (like improved computation) to make things better, and really improve productivity. 

Consider the classic example of the factory, described by Brynjolfsson and McAfee in The Second Machine Age (an example originally described by Stanford Professor Paul David in his 1989 paper, “Computer and Dynamo,” and expanded in a useful 1990 discussion, here).  

In the days before electricity, a steam engine-driven plant was built around the main (and only) power source, exploiting all three dimensions given the need for all equipment to run off of this.  With the arrival of electricity, Brynjolfsson and McAfee explain, it replaced the steam engine, but the architecture of the factory initially changed very little.  Thus, in one sense, the factory had been “modernized” by electricity, but had actually realized very little gain in productivity as a result of upgrading from steam power. 

What really changed things, it turned out, was when, over time, innovators figured out a radical new layout for the factory — the one we know today — where the equipment is spread out along a single floor, and each machine is individually powered, facilitating what Brynjolfsson and McAfee call “the natural workflow of materials.”  The impact of these so-called “complementary innovations” resulted in huge gains in productivity, which doubled or even tripled as a result. 

In a similar vein, perhaps, the arrival of cloud computing was hardly noticed, much less embraced, by healthcare and biopharma.  Yet consider the journey of one of the most successful and least widely appreciated tech companies that has made a fortune in biopharma: Veeva, a cloud software company founded in 2007.  It sports a market valuation of over $20 billion today. 

Veeva was created by former Salesforce engineers who approached biopharma companies with a relatively modest proposition: improve the efficiency of your sales team.  The idea was that  (a) the cloud platform would be especially helpful, given the geographic dispersion of sales reps; (b) the users, and designers, would benefit from almost instant feedback – salespeople can feel and can quantify the impact of improved information right away, and can help further improve the technology.  (In contrast, even if you improved the way drugs are discovered, it could take years to demonstrate this impact, and it could be challenging to attribute this directly to one factor or another. This is a key part of the rub when “quickly” trying to evaluate a new technology like AI for drug discovery.) 

Over time, Veeva has moved from sales into additional corporate functions, now offering a vast array of core services to many top tier pharmas, while managing to stay largely under the media radar. 

To me, this stepwise problem-solving model seems more relevant than the “this changes everything instantly” mindset.  Veeva’s approach is more similar to the way many transformative technologies actually find their way onto the scene. Success, in this case, was not born from some notion of Veeva as a self-declared disruptor of industry. Rather, it was an innovative young company initially offering something specific and useful, and then expanding opportunistically. 

Google, readers will recall, started off as simply an improved approach to search.  It didn’t start with much of a business model – certainly without the cost-per-click advertising model that has driven the company since. While Google has disrupted many industries, such as newspapers, this was hardly the original intention, which was simply to improve the way search is done.  

Biopharma and healthcare are positively replete with opportunities to do a great many things much, much better. Emerging technologies should be able to help. Ultimately, I suspect these technologies will come to permeate our organizations, and become as much a part of the firmament as PCR and Excel spreadsheets are today.

My guess is that companies that ultimately manage to deliver these transformative technologies to healthcare and life science will arrive upon the scene not with the pomp and circumstance we often see today from well-heeled startups and high-flying venture backers so adept at fomenting FOMO (fear of missing out). Instead, I would anticipate the future winners will approach the space more like Veeva, with little messianic bluster, and laser-focused on solving a critical, high-value problem. They will also be poised to progressively broaden their scope over time as they come to better understand the problems to be solved and are increasingly trusted by existing stakeholders and partners. More and more, they will be called upon to deliver the solutions life science and healthcare requires, and that patients both demand and deserve.

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[Editor’s Note: this is a new column called “Astounding HealthTech” by TR contributor David Shaywitz.]

David Shaywitz

If you want to know the difference between tech venture capitalists and biotech VCs, look at their respective views on AI applications for drug discovery.

Many of the most prominent AI drug discovery startups boast exceptionally rich valuations, driven by the enthusiastic participation of tech VCs. 

While many life science VCs view these opportunities as intriguing, they struggle to make sense of the often-stratospheric valuations.

For example, Salt Lake City-based Recursion Pharmaceuticals was valued this July at $699M, according to Bloomberg, while UK-based BenevolentAI was recently said to be valued at over $1B – a staggering figure that actually represents a haircut of about 50% from its April 2018 valuation, according to the Financial Times.

The thesis of tech VCs seems to be that these sorts of approaches will revolutionize biopharma, and anyone seeking to be competitive will need to acquire such a platform or risk becoming obsolete. 

Life science VCs, of course, recognize the limitations of contemporary biopharma drug discovery.  Successful drugs are so rare, and so hard to predict in early R&D, they are considered “a miracle,” according to the Novartis CEO and the head of research at Merck. Most life sciences VCs and research executives are wary that AI, however useful, may not represent the silver bullet for what they’re up against.

As David Weitz, the head of Takeda’s La Jolla site, who has thought deeply about this issue, recently told me in the context of this piece (disclosure: I work at Takeda’s corporate VC arm):

“The value of AI is demonstrated when its predictions are validated and the combined AI prediction / validation effort is catalytic. Do AI predictions provide insights not otherwise attainable by other methods?  Are the predictions actionable and have a reasonable probability of success to merit experimental follow-up? Taken as a whole, was a meaningful roadblock more efficiently overcome than traditional methods?

David Weitz

Even if AI supports a step in the drug discovery workstream, success only comes when the remainder of the drug discovery efforts are executed effectively. That may be outside an AI solution provider’s core expertise.  The ultimate measure is the efficient delivery of high-quality discovery candidates. The ‘how’ is secondary.

So build vs buy?  Neither.  Collaborate smartly where the partners synergize their respective capabilities.  Measure the program’s holistic success and secondarily the extent to which AI provided a breakthrough or an efficiency.”

This view seems to align with cautions raised by Derek Lowe in his recent critique of the breathless excitement around AI purportedly discovering a drug class via virtual screening. Lowe asserts this effort was not much better, if better at all, than traditional approaches. Even if the claimed AI breakthrough is found to be a legitimate reproducible advance over time, “the costs at this point are but tiny little roundoff errors in the total cost of a real drug development project,” he wrote.

This perspective seems generally echoed by noted life science investor and commentator David Grainger (as I recently quoted here).

“When you get under the hood, there isn’t really anything special [about the AI approach]… I think more data and better data science can revolutionize some human endeavors, but drug discovery is not one of them – it can help make better the parts of the process we do adequately now, but it doesn’t address the real rate-limiting step on innovation, which is understanding biology. Here, what is needed is not data and data science, but education about complex systems (a la the Santa Fe Institute). Current ‘AI’ approaches are so linear and reductionist, it makes me shake my head in disbelief at all the hype.“

As another well-regarded life science VC recently pointed out to me about one prominent AI-for-drug-discovery company, “What’s the business model?  I don’t think it’s there.”

What’s been demonstrated thus far hasn’t persuaded most of the “Where’s the Beef?” crowd that tends to populate life sciences VC firms. The tendency of life science investors is to especially prize assets that have generated some interesting data from experiments (i.e., they look like potential drugs). It isn’t unusual for even a so-called “platform” biopharma to be valued essentially based on the asset furthest along in clinical testing. Often, little, if any, value is placed on earlier-stage assets or the platform itself (see this relevant discussion of Nimbus model by Bruce Booth of Atlas, who describes a potential solution).

Tech VCs, by contrast, tend to break out the checkbook for exciting ideas that have seemingly boundless potential – especially ones that address large total addressable markets. AI for drug discovery can be put in that category.

Of course, extreme skepticism and extreme optimism aren’t the only ways to look at emerging technologies such as AI for drug discovery.

There’s always the hope – realized infrequently but not never – that a platform will truly prove to be the “gift that keeps on giving,” that a new approach will generate a series of attractive assets that will provide sustained value and ultimately revenue over time.

Unfortunately, this rarely materializes. A few disappointing examples I remember vividly, because they occurred at around the time I was there, was Merck’s acquisition of the GlycoFi platform (which was going to leverage a yeast-based platform to accelerate the development of biologics including “biobetters”) and Merck’s acquisition of Sirna Therapeutics, an RNAi company, which it acquired in 2006 for $1.1 billion and subsequently unloaded, eight years later, for $175 million to Alnylam Pharmaceuticals, with little if anything to show for either of these efforts.

Pharma executives have long memories for episodes like this. Battle scars from those disappointing acquisitions tend to remain sensitive for many years after the fact. They reinforce a sense of skepticism that the next shiny object will blow open biology or radically change drug discovery and development.

Tech investors, perhaps embolded by their experiences seeing tech deliver results in industries where this was said to be impossible, see in biopharma a high value problem that technology could help solve. 

My sense is that many tech investors view AI-enabled drug discovery as a “missing link” in drug development, and, tactically, believe pharma companies will be so smitten by the results of the approach that someone will acquire the underlying platform at an attractive premium. Meanwhile, at least right now, biopharmas seem to believe (as apparent in the comments earlier) that AI can perhaps slightly improve small aspects of drug development, but are unlikely to really move the needle.  Hence, as I discussed in a recent HBS podcast (listen here, read my summary here), pharma companies would much rather pay market value for individual assets (early-stage drugs), which the pharma feels comfortable assessing, versus paying for a platform of uncertain significance.

This creates both an opportunity and a challenge for AI-driven drug development companies.  The opportunity is that if the AI startup actually can create 10 (or even two) promising early stage drugs in the time it would typically take to create one, they could in theory sell each molecule and pocket a huge amount of revenue.  But the challenge is that many of these companies, while adept at AI, often have far less experience in making drugs, which requires an enormous amount of tacit knowledge and on-the-job experience. 

Once you’ve created a molecule, putting it through rigorous preclinical and clinical trials is also expensive. It’s non-trivial for an AI company to legitimately advance and adequately validate a number of candidate molecules. The pot of gold at the end of the rainbow, of course, is that if an AI-driven company could actually do this, and generate several promising candidates, then I imagine (as I said on the HBS podcast) either they’d make a lot of money supplying compounds to biopharmas, or they’d be in a position to acquire a biopharma for its late-phase clinical development capabilities, plus sales and marketing teams. I imagine this is the dream towards which many AI-driven drug developers are driving.

Presently, it’s difficult to know how the story will end; I am a huge fan of the phenotypic screening which is a key aspect of the efforts for a number of AI drug discovery companies – it was actually what led me to pursue my PhD in yeast genetics, as I was captivated by the power of screens and the opportunity they presented to discovery new biology.  (I suspect a similar excitement is behind the massive CRISPR screens that are driving both Maze and the GSK collaboration with UCSF and UC Berkeley that GSK’s research chief Hal Barron has set up; notably, both efforts involve pioneering UCSF biologist Jonathan Weissman, a former yeast biologist [disclosure: we attended high school together, he was a year ahead of me]); UC Berkeley’s Jennifer Doudna is co-leading the GSK partnership from the Berkeley side.

While it’s easy to see how the slew of AI-for-drug discovery companies might flounder and then fade, I can also envision – very easily – an upside scenario where one biopharma company takes the plunge, and acquires one of the high-profile AI-for-drug discovery startups. It’s exactly the sort of dramatic investment in innovation that CEOs love to do. 

If and when this happens, then other pharmas are likely to follow; as reluctant as so many are to be first movers in an uncertain new area, there’s a strong fast-follower reflex. Executives want to demonstrate involvement in this area before their boards start to press them on why they’re behind. 

If this occurs, the tech VCs investing in these AI-driven drug discovery companies are going to make out like bandits, even as many life science VCs continue to shake their heads, wondering what the fuss is all about.

[Author’s Note: I am thrilled to join the contributor roster of Timmerman Report with my Astounding HealthTech column. The title is a tribute to the iconic, mind-expanding, occasionally prescient (as I discuss here) science fiction magazine of the 1930s and 1940s. I expect to focus on the transformational opportunities and implementation challenges presented by emerging technologies in healthcare, biomedical discovery, and biopharmaceutical development, from next-gen sequencing and cell therapies to digital biomarkers and the meaningful application of AI. As part of this transition, I have now stepped away from Forbes, though my legacy posts will of course remain available on that platform. I have admired Luke for years, and have always appreciated his insight, instincts, and integrity, and it’s especially exciting and meaningful for me to have the opportunity to contribute to what he’s creating. I hope over time to deliver the insight and occasional delight that subscribers have come to expect from Timmerman Report.—David Shaywitz]

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