Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
David Shaywitz
Health tech seems balanced precariously between excessive optimism and excessive skepticism, between the promise that emerging technology is poised to disrupt health like it has so many other areas, and the painful recognition that many idealistic technologists misunderstood both the scientific and human dimensions of the inordinately complex problems to be solved in both health care services and the development of novel therapeutics.
It seems like a healthy, motivating tension, provided we can muster both the mental clarity to resist the hype and the intestinal fortitude to outlast the despair.
Technology takes a long time to work through, and figure out how to effectively implement. You can see this in biotech, as I wrote after JPM2018, and discussed recently: today, most leading pharmaceutical companies are aggressively investing in gene therapy and cell therapy, approaches that seemed like fantastical (astounding?) science fiction for years, before a tractable path forward seemed to crystalize before us in just the last decade. Even today, in these areas, we’re still pretty early in the implementation phase; these approaches have demonstrated potential, but generally remain remarkably difficult to execute at scale and successfully commercialize.
What remains clear are the same imperatives that have motivated healthcare innovators for years: the urgent need for profound improvement in the way we practice medicine and discover and develop novel therapeutics.
Clinical Medicine: Crying Out For Improvement
Clinical medicine, as a leading oncologist recently explained to me, remains as much of an art as a science. The aspiration of a learning healthcare system, a perennial talking point, continues to remain an elusive goal; even today, with all our data-gathering and analytic capabilities, so much relevant information is never adequately captured, studied, and fed forward to help the next patient. We need to do a much better job of leveraging the volume of clinical experience to accelerate learning and identify improved approaches to care that could, perhaps incrementally, but in the aggregate, transformatively, improve the care we provide to our patients, which is still largely driven by eminence, intuition, and a litany of cognitive biases.
At the same time, there is also an essential role in medicine for experience and intuition: medicine is the defining example of “fractionated expertise.” For those unfamiliar with the jargon, this is where professionals exhibit demonstrable expertise in some of their activities but not others; I’ve written about this here and here.
The elusive challenge in medicine is figuring how to leverage data without (further) degrading what I continue to believe many patients, especially those with serious and/or chronic conditions, still want (and certainly deserve) from their doctors: an authentic, human relationship, highly attuned to individual emotional subtlety. Such physicians partner with patients in a way that’s responsive to the complexity of their needs – rather than just based on what a coarse algorithm might spit out based on population-level data. The goal is developing the data and the doctors so that we continue to have empathic, inquisitive clinicians with the scientific sophistication to understand the patient’s unique illness, and who are driven to go to the next level, accessing the sort of ready information that can help physicians best tailor treatments to their patients.
Drug Discovery & Development: Crying Out For Improvement
Meanwhile, drug discovery and development seems to be as difficult, and capricious, as ever. Despite the many highly touted advances in biological technology, including the ability to engineer therapeutics with greater intentionality (see here), the failure rate remains staggering. No technology has come along to dramatically improve upon the painful reality that only about one out of 10 drug candidates entering clinical trials (already a steep hurdle) is able to successfully run the gauntlet, and emerge as an approved, commercial product that can be prescribed for patients. Leaders of biomedical R&D teams, appropriately, still regard it as a miracle when a novel drug actually makes it all the way to regulatory approval. Failures at all stages of development continue to abound, challenges I’ve discussed in this space (here).
Every aspect of this process cries out for improvement, from figuring out how to precisely target different conditions at a molecular level to developing suitable candidate molecules and intelligent combinations to precisely matching these candidate therapeutics with the patients most likely to benefit, to identifying these patients and efficiently conducting clinical studies, to, perhaps most importantly, as I discussed in a 2019 Clinical Pharmacology and Therapeutics commentary, understanding how approved drugs are actually functioning in the real world, and learning how to improve and optimize effectiveness.
Given all the concerns about drug prices, there is an urgent need to figure out how to do R&D far more efficiently, or confront the possibility of a devastating slowdown in biomedical innovation if investors decide the rewards from the occasional, rare success no longer justify the high-risk, long-term investment required, and take their dollars to dog food, ad-tech, scooters, or some other less consequential domain.
A definitional question facing pharma companies as they contemplate digital and data science is whether or not to embrace “digital exceptionalism.” This view posits that digital and data approaches are sufficiently distinct that they require a separate locus of expertise. For example, consultancies sending biopharma companies off on a “digital transformation journey” often position the appointment of a chief digital/data R&D officer as an important milestone. Not everyone thinks it should be. As one tech expert with extensive pharma experience recently explained to me, “the world, the science and the market are evolving. If the core technologies are truly quant/technical, the head quant should be the CSO. If not, manage it traditionally via biostats, biomedical informatics and so on,” adding “Digital tools are just tools.” In other words, just as you wouldn’t have a “Chief PCR Officer,” does it make sense to have a chief digital officer, and to consider digital/data as a separate and distinct organizational capability?
On the other hand, you could argue, quite reasonably, that while ultimately digital and data capabilities will be seamlessly integrated, right now, these approaches tend not to be either familiar or intuitive; thus having a core group comfortable with these approaches represents an important and useful temporizing measure.
From “Data Science” to “Science”
Ultimately, as I recently discussed, digital and data science will have the greatest impact when these methods permeate the way biomedical science is done. The good news here is that data science is capturing the interest of undergraduates, and beyond. My middle school daughter – in a California public school – recently devised a small data science-type study for a class science project, receiving support and encouragement from her well-informed teacher. I imagine that as data science becomes inextricably part of more and more scientific domains, learning about it will become as routine as learning about the Krebs cycle and molecular biology, and as familiar to tomorrow’s high school students as squinting at plankton under a microscope or dissecting an unfortunate frog was to students in my generation.
Increasingly, we are likely to think about computation in health the routine way we think about it in other domains. A recent, characteristically fascinating Ben Thompson column discussed how to think about a technology becoming pervasive. He noted that at the turn of the 20th century, there was an explosion of new American car companies: 233 new companies were founded between 1900 and 1909, and an additional 168 in the decade after that. However, the number of new entrants then crashed precipitously, and the big three American automakers in the 1920s – GM, Ford, Chrysler – retained their position of dominance for over 50 years.
Critically, as he points out, “Just because the proliferation of new car companies ground to a halt, though, does not mean that the impact of the car slowed in the slightest: indeed, it was primarily the second half of the century where the true impact of the automobile was felt in everything from the development of suburbs to big box retailers and everything in-between. Cars were the foundation of society’s transformation, but not necessarily car companies.” (emphasis added)
The interesting part is that Thompson (perhaps controversially) argues that “today’s cloud and mobile companies — Amazon, Microsoft, Apple, and Google — may very well be the GM, Ford, and Chrysler of the 21st century. The beginning era of technology, where new challengers were started every year, has come to an end; however, that does not mean the impact of technology is somehow diminished: it in fact means the impact is only getting started.”
He adds that consumer startups take the presence of Microsoft, Apple, Google, and Amazon (MAGA?) as “an assumption, and seek to transform society in ways that were previously impossible when computing was a destination, not a given. That is exactly what happened with the automobile: its existence stopped being interesting in its own right, while the implications of its existence changed everything.”
Perhaps it’s not too much of a stretch to suggest we’re at a similar place in healthcare, where key aspects of the computational infrastructure can now be thought of as a given (even though of course improvements will continue to occur), and rather than wait for some future magic tech to descend from the sky (or Silicon Valley) deus-ex-machina style and magically solve all our healthcare challenges, we need to embrace the imperfect but exceptionally powerful technologies of today and really focus on applying them creatively and pragmatically both to care delivery and to pharmaceutical research and development.
Hopefully, this year’s JPM health tech discussions will focus less on audacious future promises about how technology is poised to disrupt/eat/transform healthcare, and provide concrete examples of how emerging technologies are meaningfully engaging with care providers and drug developers to deliver tangible benefits to real world users.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
David Shaywitz
Since taking over as Sanofi’s CEO in September, Paul Hudson has been blunt in his assessment of health technology decisions, and indecisions, made by previous management.
Early in his tenure, Hudson took square aim at his company’s once-heralded $500 million collaboration with Verily on Onduo. This partnership was started in 2016 and intended to help diabetics better manage their condition. This relationship has now been restructured, with Sanofi’s day-to-day operational involvement significantly pared back.
“It was a determined effort to get into the ecommerce component around diabetes and to try and build on the customer relationship with Verily,” Hudson said at a presentation in December coinciding with his first 100 days as CEO.
Paul Hudson, CEO, Sanofi
He went on to explain the reasoning for the decision:
“It’s a much harder nut to crack. It’s a much longer process. And whilst we’re excited about the work being done at Onduo, I think we were over-invested. So we’ve stepped back. We’re still an investor … but we won’t put any more operational expense in above where we are because we have other things to do with the investment.”
In case there was any confusion whether this repositioning simply reflected Sanofi’s retreat from diabetes, Hudson published a commentary in Fortune this week that clearly describes his stance on health tech more generally. It’s an unvarnished, pragmatic vision that will not surprise regular readers of this column, but is nonetheless a welcome public perspective from an industry leader, acknowledging as it does the current state of affairs at the intersection of tech and drug discovery.
While highlighting the great potential of “the transformative power of digital technology,” and acknowledging that pharma “lags behind other highly regulated industries,” Hudson then offers an unusually grounded perspective.
For starters, he invokes a version of the advice med students will remember from House of God (“At a cardiac arrest, the first procedure is to take your own pulse”), advising pharma companies to “pause and develop a strategic vision for adopting new tech.”
His distinctly unsexy recommendations include:
After offering somewhat vague and familiar recommendations about culture (learning from failure, figuring out how to engage more effectively with automation technology like aliquoting robots), Hudson returns in full voice to his anti-hype message.
“Companies are too often rushing to appear to be ahead of the curve, pursuing bold partnerships and investing in ‘trending’ technologies that are undeniably impressive but aren’t necessarily addressing critical medical needs,” he wrote. Hudson holds his fire on Google, but explicitly calls out an Apple Watch study that lacked a control arm, citing a critique by Larry Husten in STAT.
In case anyone missed his point, Hudson observes, “It’s easy to succumb to the temptation to partner with the company that will build us the splashy tool, rather than work with the company whose outcomes align with our own objectives and whose capabilities fill in our gaps. But some businesses are making the right long-term choice.”
He applauds “using analytics and A.I. to match patients to clinical trials, potentially reducing the time to find patients from many months to days or even minutes,” noting such efforts “may not seem like a breakthrough innovation, but it is a critical contribution to accelerating the process of getting medicines to patients.”
Not only did Hudson’s message resonate with me, it’s consistent with what pharma R&D executives have been saying behind the scenes for the last several years. A few brave tech executives, like Jim Manzi, have been saying this more recently (listen here) – but Hudson’s willingness to offer such a grounded perspective in some ways gives permission for others in the industry to engage tech more realistically and usefully without the risk of appearing to be a Luddite or curmudgeon.
When a leader of a company like Sanofi stops spouting platitudes in public about digital transformation, it throws the brakes on an unproductive series of interactions that stem from the hype cycle. No longer should we expect untethered promises buttressed by dubious partnerships with marquis tech brands, with minimal internal buy-in from the researchers in the trenches actually tasked with discovering and developing new impactful medicines.
What’s really remarkable is just how far Sanofi, under Hudson, seems not to be leaning on tech as either a proxy or a vehicle for innovation. In a December press release pegged to the 100 Days announcements, and entitled “Sanofi CEO unveils new strategy to drive innovation and growth,” there are exactly zero mentions of either “digital” or “technology.” The only health tech mention in the entire release I could find was a collaboration with Aetion around real-world data (“an enterprise-wide collaboration that will integrate Sanofi’s real-world data platform, DARWIN, with the Aetion Evidence Platform® to advance more efficient use of real-world evidence,” the release said.)
(For more on Aetion see this piece from 2018 featuring co-founder Sebastian Schneeweiss, and our recent TechTonics interview with CEO Carolyn Magill; for more on real world evidence see this 2018 overview and this 2019 commentary).
Perhaps Sanofi’s apparent pull-back from tech is an overreaction, but I tend to see it as a useful and much-needed recalibration, emphasizing the prioritization of palpable impact versus championing tech for tech’s sake.
This is a perspective that startups aspiring to sell into the pharma ecosystem will do well to understand. The gist is pretty simple. If you want to succeed with health tech for pharma, buzzwords aren’t going to cut it – tangible impact is required.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
David Shaywitz
With the New Year, I’m very excited to share a professional update: as of January 1, I’m the proud founder of “Astounding HealthTech,” providing advisory services to R&D-driven biopharma organizations and health tech startups striving to engage each other more effectively.
The mission of Astounding is to catalyze drug development by aligning the specific capabilities and distinct needs of individual health technology startups and R&D-driven biopharmaceutical companies to elicit the best of both.
Emerging health tech opportunities are:
Tech and Pharma: Not There Yet…
As the head of R&D at a leading U.S. pharmaceutical giant recently told me, “everyone is convinced of the importance of applying more contemporary information technology to healthcare, but the impact thus far has been modest,” adding “meaningful applications [of AI] in my world remain elusive.” A colleague at another large pharma said of his company’s dalliance with digital, “it’s like the transplant didn’t take.” Hype about the “digital transformation journey” aside, tech still seems to be struggling for traction within large pharma R&D organizations, and it appears some prominent pharma companies that have embraced digital the most enthusiastically are suffering from the most severe institutional indigestion.
…But Looking At Each Other For Good Reason
Despite the challenges healthcare systems and biopharma companies are encountering with tech today, it’s clear why they’re curious: not only do emerging technologies hold exceptional promise, as demonstrated in a range of other domains, but there’s a profound need to dramatically improve how we go about delivering care and developing therapeutics – especially given the escalating concerns about costs.
Tech Already Permeating Science
Ultimately, digital and data science will have the greatest impact when these methods permeate the way biomedical science is done, and R&D leaders are as fluent in these approaches as they are in molecular biology today; it will be an additional, not an alternate, competency, but one that will matter only when it’s clear such understanding is critical for delivering scientific results. This future may not be far off; already, data science is filtering its way into curricula and course selections, from medical school to high school.
Aligning Tech & Pharma To Deliver Best Of Both
A historical look at technology cycles reveals that transformative approaches tend not to transform immediately or evenly; it takes time to figure out how to leverage new technology, and deep domain expertise to determine where specifically to aim it. If you believe, as I do, that:
then you can appreciate both the irresistible draw of this interface and the emerging need for pragmatic insight informed by fluency with emerging technologies and experience making medicines. This is the motivation for Astounding: to guide health tech startups and R&D-driven biopharmaceutical companies through this period of profound uncertainty and enormous change, and collaboratively reimagine the future of drug development.
Today’s guest on The Long Run is David Baker.
David is a biochemistry professor at the University of Washington, a Howard Hughes Medical Institute investigator, and the director of The Institute for Protein Design at the University of Washington. Just like the name suggests, this institute works on designing proteins with special properties. Sometimes these proteins are designed on computers, from scratch, with what researchers think are optimal characteristics for therapeutics, or industrial enzymes, but which aren’t presently found anywhere in Nature.
David Baker
This is heady stuff. Baker’s group is “creating an entirely new field of chemistry” in the words of one prominent Caltech scientist. One of Baker’s colleagues in UW’s genome sciences department, Jay Shendure, has said this institute is working on things scientists will still be talking about 100 years from now. A handful of new companies have come out of the lab.
In this episode, we talk about the factors that have given rise to this opportunity in de novo protein design. We also talk about Baker’s work habits and management style, and how he’s had to adapt over time as the work has gained momentum.
He’s a fascinating guy, and his story will resonate for anyone who aspires to be a changemaker in academia or industry.
Now, please join me and David Baker on The Long Run.
The Long Run is sponsored by:
David Shaywitz
Last year, at about this time, I shared my experience losing 80 pounds.
I achieved this goal through a low-carb diet and coaching, guided by the Virta program, along with regular exercise.
The overarching concern I expressed in that article, one year ago, was my recognition of how fragile weight loss can be. Most people who lose significant weight soon gain it right back, often putting on even more than they took off. As a seasoned yo-yo dieter, prior to adopting my recent lifestyle changes, I was acutely aware of this threat, and terrified, if not consumed, by this possibility.
So what happened?
First, the good news: in 2019, I successfully kept the weight off. If anything, I may have lost a few more pounds over the course of the year. That’s worth celebrating.
Now, the less good news: achieving this weight maintenance was a constant struggle. It felt like a battle every single day. In the back of my mind, I hoped, and perhaps presumed, that as I adjusted to life at a lower, healthier Body Mass Index (BMI), I would achieve a new, stable, happy equilibrium. The dream was that my new lifestyle would result in a kind of autopilot, where I wouldn’t need to think much about eating properly. Rather, it would just happen – it would be the new normal.
To be sure, a lot has become normal: I’ve not had pasta, pizza, cookies, pastries, bagels, cake, or candy in about two years. What I’ve found is that since these are hard exclusions (absolute contraindications, you might say), I’m not especially bothered by them. There isn’t a choice to consider about whether to eat these things, ever.
Far more challenging, it turns out, is the food you can have, but in moderation – nuts and cheese, for example. A few nuts are ok for a snack, but without thinking, a few becomes a few more, and all of a sudden you’re backtracking; such small but progressive indiscretions seem to be the most difficult challenge. Plus there’s the constant attention to portion size. The maximum recommended size for a steak, for example, is 6 oz – 3/8 of a pound (and keep in mind, the recommended amount of protein-containing food per meal is around 3-6oz, so 3/8 lb is truly the upper end). Steak is delicious, and mustering the discipline to eat what can feel like a constrained portion, every single day, is an abiding challenge.
Over the past year, I have continued to check both my blood ketones and weight every day (tracking both via the Virta app). I try not to get frustrated by the noise in the data. When I first started eating carefully, my ketone levels were relatively high (squarely in the middle of the target range for nutritional ketosis) and my weight loss was rapid, but after about six months, while keeping to the same basic diet, my ketones were significantly lower (though generally still within the target range). Weight loss plateaued at a reasonable BMI, albeit stubbornly a few pounds above my intended goal.
In some ways, it felt like I was on one of those ships in sci-fi movies that finds all its navigation equipment failing as it nears a black hole. There was a time when I could easily appreciate the correlation between careful eating and solidly elevated ketones, and weight loss. When the connection became less clear, I felt confused and adrift. Even with careful eating, my ketones seemed generally less responsive – and highly variable – and my weight seemed to fluctuate as much as a pound or two up or down daily, often with little apparent correlation with anything I ate or did. Even though I rationally knew such fluctuation was likely random noise, I remained concerned every time I saw an uptick that it might be the beginning of a yo-yo cycle, and was motivated to eat even more carefully, much as a random fluctuation down sometimes led me to let down my guard for a bit, which I would later regret.
I found it quite frustrating (if not surprising) that the same general approach to eating that had initially seemed to result in significant weight loss, in half a year, stopped resulting in any meaningful weight loss – even though I was still working really hard to continue the healthy eating. Like Lewis Carroll’s Red Queen, it seemed like I needed to run fast just to stay in the same spot. The lack of consistent correlation between daily activities (eating, exercise) and measured ketones and weight the following day was also (and remains) utterly maddening. While the coaching provided by Virta seemed helpful during my initial weight loss phase, it seemed less helpful after that – not for lack of availability, or interest, but rather, because I think it’s harder to know what to say. To their credit, I found that the coaches tended to be extremely honest, acknowledging the challenges of this phase; my sense was that finding a way to keep on keeping on, after the rapid weight loss phase is over, is something that represents an ongoing struggle for even the most successful participants, including many of the coaches.
The consequences of all this disciplined eating have been a mixed bag. On the one hand, I’ve really enjoyed and appreciated the results of this lifestyle – showing up for meetings feeling fit rather than fat, and ordering clothes online relatively confident they’ll actually fit and look ok. On the other hand, in addition to the continued focus and mental discipline that’s unrelentingly required, there are other effects as well. Meals, predictably, are much less enjoyable – it’s fun to chow down with family, friends, or colleagues – especially around special occasions. When you’re eating in a hyper-disciplined fashion, meals are intrinsically much less fun – to say nothing of watching football without pizza, beer, or nachos.
During the last year, I’ve continued to exercise regularly (my choice, not a core aspect of Virta program by the way), and by regularly I really mean just about every single day – I’m not sure I skipped one day in the last year, and if so, it wasn’t very many. I’ve continued to do about 45 minutes of elliptical each day, and some weights every other day, as well as a two-minute plank each day (inspired by AliveCor founder Dr. Dave Albert). While “burning calories through exercise is a pretty inefficient process,” as the hosts of Freakonomics recently put it, I’ve embraced the daily routine, perhaps as much for its centering effect as anything else; I’m at the gym when it opens at 5am, and it’s terrific at 6am to feel that I’ve already done something positive for the day. Plus, since (as readers of this column know) I use the opportunity to listen to podcasts or audiobooks, it generally feels like a twofer, starting the day by doing something for my mind and my body. As I wrote last year, there are data pointing to the strong influence of personal social networks on obesity and fitness, and this year, I again found myself influenced by colleagues dedicated to daily exercise, including Andy Plump (head of R&D at Takeda), Tachi Yamada (former head of R&D at Takeda, former head of the Gates Foundation, and currently a venture partner at Frazier), and Mike Joyner, a physician and physiologist at the Mayo Clinic, and a twitter buddy (@DrMJoyner) before I quit engaging with the platform. Joyner was also a featured researcher in the Freakonomics podcast cited above.
Deeper Life Lessons
My two years of reflective consumption have delivered improved fitness but not psychological ease or comfort – and in this I suspect there is an important lesson, which relates not only to weight loss and diet, but also to illusions of success in most any domain. And the key visual for this is what I’ll call the “Stanford Duck,” a phenotype introduced to me by a Stanford grad as I was preparing to interview him for a Tech Tonics podcast episode.
Stanford students strive for academic excellence, of course, but apparently they are equally guided by a concept promulgated during the Renaissance called sprezzatura – effortless grace. The idea is that not only do you want to succeed brilliantly in whatever you do, but you don’t want to appear that you’re even trying; your performance is to be seen an as expression of your exceptional natural talent and ability. The catch is that to achieve, and maintain, this success, you need to work incredibly hard. Hence the analogy of the duck: above the water, calm and placid, but below, paddling like mad. It is also not unique to Stanford.
In so many domains, there’s a seductive idea of professional success, where it’s situated geographically, a place, difficult to get to but once you’re there, you’ve “made it.” What has struck me about so many successful people I know is how incredibly hard they continue to work, every single day, to remain where they are, and hopefully accomplish still more; without this drive to continuously strive, professional success may be short-lived, a superstar may lose relevance with surprising speed, a process that, a la Twain, may be imperceptible (as well as inconceivable) at first, but then occurs with cruel rapidity. The most enduringly successful entrepreneurs, academics, investors, and corporate leaders I know are characterized far more by fear than complacency, operating as if they are just starting to climb the career ladder, rather than sitting on top of it. They constantly press, constantly think about what’s next for their scholarship, their business, their art.
Dieting is much like this. You need constant vigilance and positive daily habits, both to get to a good place and, especially, to stay there.
I suspect the idea of success as a comfortable destination may represent a necessary delusion, the sort of thing that initially emboldens you to begin to move in the right direction. Perhaps by the time you realize that success is less stable and more dynamic than you originally assumed, you’re sufficiently caught up in the flow of it all, and sufficiently allured by the taste of success that you can’t let it go.
It’s a high-class problem to have, of course – one I wish upon all of us in 2020.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
David Shaywitz
I listen to a lot of audio, spoken word content that edifies (or at least distracts) me during daily workouts and when traveling. Traditionally in December, I like to share with readers my annual podcast recommendations. But in reflecting on my listening habits of the last year, I realized that I’ve probably spent at least as much time listening to audiobooks, so I’m going to include some of those recommendations as well.
For HealthTech Entrepreneurs
Podcasts
Let’s get the two most obvious, and most obviously conflicted, recommendations out of the way first: Tech Tonics and The Long Run.
Tech Tonics: Since 2015, every other week, Lisa Suennen and I have shared the stories of “the people and passion at the intersection of technology and health.” Our remarkable guests this year have included Susan Desmond-Hellmann, CEO of the Gates Foundation; Jerry Harrison, keyboardist and guitarist for the Talking Heads and now co-founder of the healthcare crowdfunding platform Red Crow.; impassioned physician-scientists Kari Nadeau (food allergy expert at Stanford), David Altshuler (human genetics, Vertex), Glenn Pierce (hemophilia entrepreneur, Third Rock), Allison Kurian (clinical cancer genetics, Stanford), and Calum MacRae (reinventing medicine from within, at Harvard); health care innovators Shami Feinglass (Danaher, when not BMX bike racing); Tanisha Carino (GSK, FasterCures, and now Chief Corporate Affairs, Alexion); Megan Callahan (head of healthcare at Lyft), Rebecca Kaul (chief innovation officer at MD Anderson), and Andy Coravos (CEO/co-founder of Elektra Labs) and many others – including Recursion Pharma’s engaging CEO Chris Gibson, who by this point is likely well-known to readers of this column (see here, here). We also spoke with the brilliant and remarkably grounded data scientist Imran Haque, who I wrote about here; he subsequently joined Recursion.
The Long Run: In this biotech version of “Inside the Actor’s Studio,” Luke Timmerman, our own James Lipton, sits down with a number of different top biomedical innovators for an extended interview. I loved this podcast from the first episode in September 2017 (featuring Alnylam’s John Maraganore). It remains a personal favorite. This year, I especially enjoyed the episodes featuring strategist Janelle Anderson (as I discussed here); MIT’s incomparable biomedical engineer Bob Langer; and President of Novartis Institutes for Biomedical Research (NIBR), Jay Bradner.
A podcast that I discovered this year (though it’s not new) carries the dubious title, Invest Like The Best. Don’t be put off. The interviews, by seasoned investor Patrick O’Shaughnessy, are outstanding – thoughtful, nuanced, and engaging. I have particularly enjoyed his conversations with Benchmark’s Bill Gurley, Lux’s Josh Wolfe, and Blue Mountain’s Michael Maubaussian – who I think of collectively as the “SFI Cabal,” because outside of their (busy) day jobs, they all seem to be connected to the Sante Fe Institute, an organization focused on the study of complexity and of complex adaptive systems. (Maubaussian is also a talented writer; I discuss one of his books, The Success Equation, here.)
I’ve continued to enjoy many episodes of A Healthy Dose podcast, hosted by Steve Kraus of Bessemer Ventures, and Trevor Price of Oxeon Partners; I would especially recommend their discussions with physician and serial entrepreneur Tom X Lee (co-founder, ePocrates; founder, One Medical Group; now CEO of the stealth newco, Galileo), and with Kate Ryder, founder and CEO of Maven.
Venrock’s venerable dynamic duo, Bryan Roberts and Bob Kocher, consistently find outstanding health policy guests for the Running Through Walls podcast. This year, I enjoyed their discussions with former Secretary of Health and Human Services and former Kansas governor Kathleen Sebelius, as well as the two-part interview with the brilliant Park brothers (here, here).
Two limited-run podcast series also merit consideration:
Moonrise, a fascinating series from the Washington Post’s Lillian Cunningham discussing the history of the space program, and the role of narrative in driving technology adoption, as I wrote about here.
StartUp – This was the first podcast series from Gimlet, co-founded by Alex Blumberg and Matthew Lieber. The first season was about the process of creating Gimlet; subsequent seasons were about other startups. The latest, and last season (start here) is especially interesting because it describes what was going on at Gimlet when it was approached, and ultimately acquired, by Spotify.
Audiobooks
Range, by David Epstein – my favorite book of the year (and like several other audiobook favorites, I re-read it after I listened to it), Range focuses on the underappreciated value of integrative, lateral thinking (versus the hyperspecialization that seems especially cherished today). I’ve discussed this important and captivating book (for parents as well as entrepreneurs) here.
The Second Machine Age – by Erik Brynjolfsson and Andrew McAfee, this engaging book is widely regarded as the bible of the current digital revolution. The authors explore how new technologies are adopted, and what may be the impacts – mostly, but not entirely, positive – of the technologies on work, culture, and society. (McAfee’s book explores how some of these themes relate to sustainability, in his conspicuously optimistic recent book, More From Less – my WSJ review here).
Elephant In The Brain – by Kevin Simler and Robin Hanson, this is an enjoyable exploration of social signaling, which turns out not only to be pervasive, but also, critical to resolving many behavioral paradoxes in the world around us.
Charlie Munger – Munger is one of American’s most admired, and most quoted value investors. In this book, Tren Griffin, a business strategist perhaps best known for his thoughtful, intellectually engaging writing about leading investors, seeks to help readers better understand Munger by offering explanations of Munger’s greatest hits, in a fashion reminiscent of the way ancient Talmudic scholars provide learned reflections on the Bible. You may not emerge from Griffin’s book spiritually enlightened, but you will certainly have a deeper sense of the primary text, and the author behind it.
Nature of Technology – I discovered this Brian Arthur book by following a citation in The Second Machine Age. While initially somewhat academic and slow going, it gains momentum over time, as he shows how technology arises and evolves, and highlights the role of combinatorial innovation.
Additional Audio Recommendations (Not Health-Related)
Podcasts
My regular podcasts (i.e. I listen to most every episode) include:
The Sub-Beacon — Squarely in the “middle-aged dudes chatting” genre (hard to understand the appeal, I know), this enjoyable weekly podcast featuring DC-area dads Jonathan V. Last, Sonny Bunch, and Victorino Matus is ostensibly focused on film reviews, but with long, delightful, detours into parenting, bourbon, steak, and The Gout.
The Bulwark – A centrist daily podcast offering thoughtful political discussion, with guests from across political spectrum; hosted by Charlie Sykes.
The Secret Podcast – This is an irregular, supplementary podcast associated with The Bulwark, available with a minimal payment; similar in content but slightly rawer and more intimate, co-hosted by Jonathan V. Last and Sarah Longwell.
Commentary — A center/right, twice weekly podcast offering thoughtful cultural and political discussion led by Commentary editor John Podhoretz, who instills the program with a bit of a McLaughlin-Group vibe.
I’d also recommend these podcasts:
While I’m not quite interested in detailed legal analysis to sustain an interest in The Lawfare Podcast in general, I was captivated by a special multi-part series they put together, called “The Report” – essentially, a surprisingly successful effort to make Mueller Report more accessible and digestible. You’ll need to dig the individual episodes out from within the Lawfare feed (start here), but it’s worth it.
Unorthodox – eclectic, hamish, Jewish-themed podcasts with excellent and often unexpected guests, some Jewish (like writers Bari Weiss and Andrew Marantz, and reporter Jodi Kantor), others not (eg pastor Henry Brinton, chef Edward Lee, reporter Clare Malone, and writer Sarah Blake).
99% Invisible – consistently delightful, engaging podcast about underappreciated aspects of design impacting the world around us; the show is a long-time favorite of mine, and more recently, my favorite podcast to listen to with my kids.
Audiobooks:
Age of Wonder – Richard Holmes takes us on a highly enjoyable romp through British science of the late 1700s, sharing the excitement of botanist Joseph Banks’ voyage with Captain Cook to Tahiti, astronomers William and Caroline Hershel’s revealing exploration of the heavens, and chemist Humphrey Davy’s discovery of laughing gas, among many other highlights.
The Compatibility Gene – This is a story (really, a collection of stories) about the history of the discovery of the major histocompatibility complex, or MHC, a critical concept in immunology. It’s told in an engaging and scientifically rigorous fashion by British immunologist Daniel J. Davis. Davis is also the author of The Beautiful Cure, about immunotherapy, which I reviewed for the Wall Street Journal last year).
Deep Work – A worthwhile self-help book by Cal Newport about the value of avoiding distractions, it was a significant influence in successfully extracting me from Twitter, as I discussed in Timmerman Report, here.
Dreamland – This account by California journalist Sam Quinones is the best book about the opioid crisis I’ve read; it’s moving, captivating, horrifying, astonishing – yet also (in a welcome contrast) nuanced, and not overly reductive in describing the historical context and potential solutions.
Factfulness – When you look at the data, it turns out that everything doesn’t actually suck. That’s the point of this Hans Rosling book, which joins an expanding “positivist” subgenre also featuring Steven Pinker (The Better Angels of Our Nature), Andrew MacAfee (More From Less – see above), and others.
How To Fight Anti-Semitism (read by the author) – Motivated to write this heartfelt and brilliant book by the horrific October 2018 terror attack at the Tree of Life synagogue in Squirrel Hill, PA, where she was bat mitzvahed, New York Times columnist Bari Weiss characterizes the different sources of contemporary anti-semitism, and emphasizes the importance of recognizing and repelling such odious sentiment.
Antisocial (also read by the author) – This captivating account by New Yorker writer Andrew Marantz focuses on one of the sources of hate Weiss identifies: far right extremism, and takes readers insiders this movement, highlighting along the way the enabling role technology has played (see this excerpt).
American Carnage – This comprehensive, engaging, and deeply disturbing book by Politico’s chief political correspondent Tim Alberta takes us inside the 2016 campaign and documents the astonishing transformation of one of America’s major political parties.
The Secret Life of the American Theater – My guilty secret might well be my lifelong affection for musical theater, despite finding myself so bereft of talent that I failed to land even a chorus role in a high school production of Grease, where everyone was ostensibly guaranteed a part. Growing up, I was fortunate enough to see a number of classic shows on Broadway, including The King and I, Camelot, Annie, City of Angels, and many others. Similarly, the many Saturday mornings I spent working in the lab during my training were tolerable, if not enjoyable, due to the weekly airing of “Standing Room Only” on Emerson Radio’s WERS. About a year ago, I was delighted to hear about producer Jack Viertel’s book on American theater, and was even more delighted to listen to it (expertly read by noted actor David Pittu). In The Secret Life of the American Theater, Viertel breaks down the canonical musical into its core elements, and discusses how classic shows fulfill these elements (sometimes with remarkable creativity), or fail to (sometimes disastrously). You can see how fixing a single, critical element can save a show – like the last-minute introduction of the “Comedy Tonight” number to establish the perfect tone for A Funny Thing Happened On The Way To The Forum. The entire book is a treat, from start to finish – encore!
Astounding – The title of my column is a tribute to the pioneering science fiction magazine, Astounding Stories. The real-life stories of the editor (John W. Campbell) and writers for Astounding (like Isaac Asimov, L. Ron Hubbard, Robert Heinlein, and others) turn out to be nearly as fantastic as the fiction they published, as Alec Nevala-Lee reveals in this deeply-reported account. You’ll never be able to think of Asimov or Hubbard the same way again.
Today’s guest on The Long Run is Jigar Raythatha.
Jigar is the CEO of Cambridge, Mass.-based Constellation Pharmaceuticals. This company is built to develop drugs against epigenetic targets. Simply put, this is a way to turn genes on or off without altering the underlying DNA. The pharmaceutical industry fancied this idea about a decade ago, as a way to shut down specific disease processes, but by binding with enzymes that can be reached with classic small molecule chemical compounds the industry knows well.
Jigar Raythatha, CEO, Constellation Pharmaceuticals
This concept eventually fell out of favor. Some of the early compounds scooped up by Big Pharma never lived up to the hype. Exciting new modalities like gene editing and cell therapy emerged. When Genentech, its big partner, walked away from an option to acquire Constellation in 2015 – the little company had a lot of explaining to do.
Jigar entered this situation as CEO in May 2017. He raised money, crafted a new development strategy, brought in some new blood, and took the company public. This year, Constellation burst back onto the biotech main stage with some preliminary clinical data for a drug candidate for myelofibrosis.
The compound, CPI-0610, is a bromodomain and extraterminal domain inhibitor. It has been tested in a Phase II study known as Manifest, as a single agent, and in combination with ruxolitinib, the JAK inhibitor marketed by Incyte. Constellation has looked at treatment-refractory patients, as well as people getting their first treatment.
The results are striking, as I discuss with Jigar in the latter part of the show. More than 90 percent of patients are seeing improvements in spleen volume reduction, and in total symptom scores, while also seeing their hemoglobin counts (which were depressed) come back up closer to normal. Results were even better in the first 4 treatment-naïve patients. You can see the abstracts published on the American Society of Hematology website, in advance of that medical meeting Dec. 4-7, 2019 in Orlando. Constellation will be presenting updated data there.
Constellation stock touched a lot of about $4 a share this year. As of this recording, the stock is worth $46.56 a share – a market valuation now exceeding $1.5 billion.
This is a turnaround.
Listen to Jigar Raythatha talk about it on The Long Run.
The Long Run is sponsored by:
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Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
David Shaywitz
Biopharmaceutical and healthcare executives increasingly find themselves attending conferences and presentations featuring the evangelistic selling of AI by self-assured VCs, energetic entrepreneurs, and earnest consultants. The promise is that AI will change everything.
Then the executives return to work, face the quotidian reality of their operation, and wonder whether AI will change anything.
Enter Jim Manzi.
Manzi, depending on your view, is a business operations guy with an affinity for math, or a math whiz who’s taken his talents to business operations. Manzi, as Lisa Suennen and I found out on a recently-recorded Tech Tonics podcast, scheduled to air later this year, enrolled at MIT at the age of 16 because he was bored with high school. Soon, he discovered he loved everything about the place, ultimately pursuing advanced math and physics.
After MIT, he started a prestigious graduate program at Wharton, but then decided against academic life and dropped out after a year. He wanted to get to work, and soon found that he loved thinking his way through practical business problems.
At first, he pursued this through a quantitative management consulting firm. Later he started his own company, called Applied Predictive Technologies (APT), which enabled consumer companies to set up randomized controlled studies to empirically address real-world choices, like how best to position candy on a convenience store shelf. The company was acquired by MasterCard in 2015, for $600 million.
Jim Manzi
Manzi’s experiences at APT led him in 2012 to write a fantastic book, Uncontrolled, about both the utility of such experiments but also about the dangers of overgeneralizing from these sorts of studies to draw unfounded policy conclusions. As he told Lisa and me, “Knowing the confidence interval is as important as knowing the estimate.“
More recently, Manzi has started a new company, Foundry.ai, which essentially seems to offer “AI business solutions as a service.” The company seeks out pressing business problems that could potentially be solved by AI, and designs a fit-for-purpose solution. Projects that gain traction can potentially evolve into stand-alone businesses that might be offered by Foundry.ai to new customers with similar problems. Foundry.ai benefits by working closely with an initial customer to develop and refine a solution to a known high-value business problem, and the original customer benefits from having a pressing business problem effectively solved in a customized fashion.
As Manzi increasingly finds himself meeting with life science and healthcare delivery companies, I thought his perspective on AI might be especially relevant.
For starters, Manzi views AI as “a new name for an old thing” – as well as “the most overhyped term in the history of the world” (the irony of this description isn’t lost on him).
As Manzi sees it, “AI on television may be robots playing Jeopardy” but when “applied in real business settings to improve performance,” AI is basically “data plus math used to create statistical improvement in some repeated business decision process.” He tends to frame problems as “Is there a real opportunity to drive performance improvement?”
Consequently, Manzi tends to be on the lookout for “repeated processes.” This is pretty much the opposite of asking AI to conjure up the creativity required to make a new drug from scratch. But Manzi notes there are “business processes that happen repeatedly within drug discovery, within financial operations of a company, within the execution of randomized trials – solving those sub-problems is actually a much more appropriate use of AI.”
As Manzi puts it, “The idea that AI is going to solve a problem that a group of extremely smart expert people cannot solve is generally a mirage; where that’s happened is in very defined problem spaces like Chess or Go.”
He adds, “When you get into an extremely unstructured problem space, doing something that no human can do, to me is a really heavy lift for an AI system.” What seems more suitable, he suggests, are tasks that “an expert human can do with reasonably good reliability” but with AI you can do “at way lower cost, way faster, and with greater reliability and lower error…. You have a more narrowly defined problem space.”
Manzi worries about the way AI is currently portrayed by some enthusiasts. “When I look at current AI technologies that I’ve seen up close and write code around, I think that there are extravagant promises being made and assertions being made about what it can do. It can do really valuable things – I build companies around it – that are enormously economically important and actually can improve clinical health outcomes.” But these solutions, Manzi says, do not involve wicked problems.
Still, by improving repeatable processes and focusing on important sub-problems that end up consuming considerable amounts of people’s time and energy, you can free up some human ingenuity to work on the more wicked aspects of drug discovery. In this way, AI “may make it feasible to get you to a drug you might not have gotten to otherwise – a lot faster and a lot cheaper.”
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
