26
Jan
2020

Challenging Core Assumptions, Tech Backlash Paves The Way for More Thoughtful HealthTech

David Shaywitz

Digital transformation (as I recently discussed), and the implementation of emerging technologies more generally, is routinely pitched by enthusiasts like Tom Siebel as both urgent and inevitable, something organizations need to embrace or risk irrelevance, if not extinction. 

Yet the “embrace or die” assertion is under increasing, and healthy, scrutiny, as the “techlash” (technology backlash) gains steam. 

“Surveillance Capitalism”: Tech As Force For Harm

Voices of concern have started to coalesce under the banner of what Harvard Business School professor emerita Shoshana Zuboff has termed “surveillance capitalism.” She synthesized and amplified this growing concern in her 700+ page 2019 book The Age of Surveillance Capitalism. For a shorter summary, I recommend reading this recent New York Times essay by Zuboff, and listening to this especially informative interview with her conducted by distinguished technology journalist Kara Swisher (of Recode and the Times).   

The core of Zuboff’s critique can be found in the story of Google itself, a company that (as described in the Recode podcast) initially came to prominence by building a phenomenally effective search engine that users appreciated. But the company struggled to make money in the early days, and “very swanky venture capitalists were threatening to withdraw support,” according to Zuboff. In an existential panic, Google apparently realized that it was sitting on a huge amount of interesting data, far more than was needed to improve the search algorithm. 

At its inception, reports Zuboff, Google had rejected online advertising as a “disfiguring force both in general on the internet and specifically for their search engine.” 

But spurred by the threat of extinction, Zuboff explains, Google declared a “State of Exception,” akin to a state of emergency, that “suspended principles” and permitted the company to contemplate previously shunned approaches. They recognized they had accumulated “collateral behavioral data that was left over from people’s searching and browsing behavior,” data that had been set aside, and considered waste. But upon further review, says Zuboff, Google engineers realized there was great predictive power in the combination of this data exhaust plus computation: the ability to predict a piece of future behavior — in this case, where someone is likely to click — and sell this information to advertisers. 

The result, according to Zuboff, was a radical transformation of online advertising, turning it into a market “trading in behavioral futures,” while claiming “private human experience” in the process.  “We thought that we search Google,” writes Zuboff, “but now we understand that Google searches us.”

As this model caught on, Zuboff explains, tech companies accrued exceptional influence, due to “extreme asymmetries of knowledge and power.” Over time, these companies began to “seize control of information and learning itself.”

These technology companies, asserts Zuboff, “rely on psychic numbering and messages of inevitability to conjure the helplessness, resignation, and confusion that paralyze their pray.” She argues “the most treacherous hallucination of them all” is “the belief that privacy is private.” It’s not, she argues, because “the effectiveness of … private or public surveillance and control systems depends upon the pieces of ourselves that we give up – or that are secretly stolen from us.”

Notably, Swisher strongly shares these privacy concerns, even writing a year-end commentary in the Times last December entitled “Be Paranoid About Privacy,” urging us to “take back our privacy from tech companies – even if that means sacrificing convenience.” She writes, “We trade the lucrative digital essence of ourselves for much less in the form of free maps or nifty games or compelling communications apps.” Adds Swisher, “It’s up to us to protect ourselves.”  

(In contrast to some health tech execs I know, Swisher views Europe’s General Data Protection Regulation [GDPR] and California’s recently-enacted Consumer Privacy Act as positive developments.)

Both Siebel and Zuboff seem to agree on the power of the emerging technology. They vehemently disagree about whether it’s a force for good or ill. 

The Pinker Perspective: Cautious Optimism

But another perspective is that both Siebel and Zuboff overstate at least the near-term power and utility of technology by accepting as a given that the impetus to collect every possible piece of data about every possible thing will soon result in remarkably precise predictions.

This is what Siebel promises, and Zuboff fears.

In contrast, I found myself agreeing with the more grounded viewpoint Harvard psychologist Steven Pinker offered in a 2019 discussion with Sapiens author Yuval Noah Harari (who was making the case for surveillance capitalism).

In recent years, Pinker has attracted controversy by arguing (in his 2018 book Enlightenment Now, and elsewhere) that despite endless lamentations and prophecies of doom, life is actually getting better, and is on a trajectory to improve still more. 

Besides Pinker, this encouraging perspective has been recently discussed by a number of authors including Hans Rosling (Factfulness), Andrew MacAfee (The Second Machine Age, More From Less – my Wall Street Journal review here), and John Tierney and Roy Baumeister (The Power of Bad – my Wall Street Journal review here).

Pinker says he’s not losing sleep about emerging technologies, in large part because he suspects the rate and extent of technological progress has been significantly overstated. Consider human genetic engineering, he says, where frightening concerns had been raised about engineering people with a gene that made them smarter or better athletes. That turned out to be a wild oversimplification, he argues – many genes impact most traits, and since genes tend to be involved in many functions, there’s a good chance any intervention would do at least as much harm as good. The limitations of genetic data is also something Denny Ausiello and I anticipated in this 2000 New York Times “Week in Review” commentary, and something Andreessen-Horowitz partner Jorge Conde thoughtfully reflects on in this recent a16z podcast.

Returning to AI, Pinker notes that “predicting human behavior based on algorithms” is “not a new idea,” nor one likely to immediately destroy the planet.  “I suspect,” Pinker says, “we’ll have more time than we think simply because even if the human brain is a physical system, which I believe it is, it’s extraordinary complex, and we’re nowhere close to being able to micromanage it even with artificial intelligence algorithms. The AI algorithms are very good at playing video games and captioning pictures, but they are often quite stupid when it comes to low probability combinations of events that they haven’t been trained on… even the simple problems turn out to be harder than we think.”

He adds, “When it comes to hacking human behavior – it’s all the more complex. Not because there’s anything mystical or magic about the human brain – it’s an organ – but an organ that ‘s subject to fantastic non-linearities and chaos and unpredictability and the algorithm that will control our behavior isn’t going to be arriving any time soon.”

In a 2018 op-ed, Pinker notes the “vast incremental progress the world has enjoyed in longevity, healthy, wealthy, and education,” and adds that technology “is not the reason that our species must some day face the Grim Reaper. Indeed, technology is our best hope for cheating death, at least for a while.” 

He describes threats such as “the possibility that we will be annihilated by artificial intelligence” as “the 21st century version of the Y2K bug,” which was associated with apocalyptic prophesies, yet ultimately had negligible impact.

In a particularly interesting exchange between Harari and Pinker, Harari expressed concern that the surveillance state was turning our lives into a continuous, extremely stressful job interview, suggesting we’re heading to the point where everything we do every moment of our lives could be surveilled, recorded, and analyzed in a way that could impact future employment.

Pinker, in response, noted that “One of the most robust findings in psychology is that actuarial decision making – statistical decision making — is more reliable than human intuition, clinical decision making.  We’ve known this for 70 years but we typically don’t do what would be more rational.” In this example, it would be rational to scrap job interviews, and use statistically-informed predictors instead.  Even though we know job interviews are subject to bias and error, Pinker points out, we still use them, and don’t “hand it over to algorithms.” 

Of course, many technophiles – and technophobes — would say this is exactly what’s already occurring.

The Taleb Quadrant

There’s actually a fourth quadrant to consider – which I think of as represented by Nassim Taleb, who is critical (as he articulates with particular clarity in Antifragile) of what he sees as our worship of new technology, not because he fears it’s about to immediately lead to the end of life as we know it, but rather because he thinks our increased interconnectivity places us at greater risk of a catastrophic failure – i.e. make us far more fragile. He trusts approaches that have stood the test of time “things that have been around, things that have survived,” and worries about our “neomania – the love of the modern for it’s own sake.”

Implications for Health Tech

While perhaps inconvenient for some health tech entrepreneurs in the short term, the increasingly robust discussion about the impact of technology represents a positive development for the field.

Why positive? Because it creates the intellectual space needed to challenge tech assertions and assumptions, while demanding rigorous proofs of value. 

I incline towards Pinker’s perspective. Technology, in my view, offers us real hope in our efforts to maintain health and forestall and combat illness. Figuring out how to derive meaningful benefit from the technology will not be nearly as easy nor as rapid as consultants promise. As we work through these challenges, we need to be thoughtful and deliberate, and consider the right kind of guardrails we want to put in place as we bring ever-more powerful technologies to bear in our healthcare system. The hurdles we must clear – technological, social and political in nature – as we create systems that can meaningfully intervene and improve upon what we have in healthcare are enormous. We would be foolish to underestimate the work ahead – and even more foolish not to embrace the challenges and get going.

23
Jan
2020

Incrementalism is the new Disruption, Trust is the New Black, and Positive Change (for now) at FDA: Takeaways from the 2020 Precision Medicine World Conference

David Shaywitz

I had the privilege of serving as emcee for the “Data Science and AI” track on the first day of this week’s Precision Medicine World Conference (PMWC) in Santa Clara, CA, as well as chairing a panel discussion on data mining and visualization. 

I came away with a sense of optimism and need, organized around several key themes.

In Praise Of Incrementalism

In a day focused on technology, and featuring a number of startups, you might have expected to hear a lot about “disruption” and “disruptive innovation” – but I didn’t.  Instead, the watchword of the moment seems to be “incrementalism” – not in the dispirited sense of having minimal aspirations, but rather in the grounded (versus grandiose) sense of seeking to motivate buy-in from existing healthcare stakeholders by demonstrating a discrete and useful (if not super-sexy) benefit. 

Kaisa Helminen, the CEO of digital pathology company Aiforia Technologies (which I’ve written about here), emphasized the importance of first taking small steps, before attempting to make larger strides.  She amplified this point in a follow-up email:

“Labs should start with incremental steps in utilizing AI in digital pathology, e.g. starting with quality control (QC), workflow optimization or with a few applications that are painful for pathologists to count (e.g. counting mitosis) to get them used to the tech and to facilitate adoption.”

Similarly, Vineeta Agarwala, an impressive physician-scientist who recently joined Andreessen-Horowitz from GV, and who was previously a project manager at Flatiron, emphatically and repeatedly stressed the importance of incrementalism, even in the context of AI.  For example, she noted that at Flatiron, which focused on deriving clinical trial-like data from EHR data (see here), a key use of AI at this tech-driven company was…to determine which patient charts to spend time manually extracting the data from!  It seems unsexy, but apparently it delivered immediate benefits in operational efficiency.

Vineeta Agarwala

Grounded Health Tech Investors

A pleasant surprise at this conference was the number of VCs represented who both seemed interested in the nexus of tech and health and appeared to be approaching it in a grounded fashion, led by investors who have relevant domain experience. Greg Yap from Menlo Ventures, and Vijay Pande and Agarwala from Andreessen-Horowitz, particularly stood out. 

Pande emphasized there’s “nothing magical about AI,” and acknowledged that developing new drugs is not a fast process, as even compounds designed with the help of AI require, in his words, “the usual stuff” such as a battery of preclinical assays and extensive clinical trials.

Similarly, Agarwala described AI as simply “technologies to better learn from data,” and emphasized that “progress is going to be incremental.” Yap was perhaps even more cautious about AI, worried that we seem to be “at the peak of the AI hype cycle.”

Many (but not all) of the VC firms gravitating towards the “AI and data science” opportunity in healthcare and biopharma seem to be tech firms (Menlo Ventures, Andreessen-Horowitz, DCVC stand out) that have added domain expertise on the healthcare side, rather than healthcare VCs that have added domain expertise on the tech side; one conspicuous exception, perhaps, is Jim Tanenbaum’s Foresite Capital, a firm with deep healthcare roots that’s deliberately pursuing a technology dimension.

The Calcified Hairball Problem

The most dispiriting panel of the day, by far, was a discussion of interoperability led by Stan Huff of Intermountain, and featuring Michael Waters of the FDA, and James Tchung of Duke, describing (among other challenges) the excruciating ongoing effort required by the FDA SHIELD initiative to create a unifying schema for the representation of laboratory data. 

Hurdles seemed to be everywhere, and the realized rewards appeared uncertain at best.  The problem seemed to me to reflect the “calcified hairball system of care” to which VC Esther Dyson has famously referred. Listening to the panel describe the extensive painful effort involved in even the most basic efforts to extract meaningful information reinforced the sense that the existing system may be a virtually intractable mess; engaging with it seemed likely to result in a huge suck of time and money, with brutal political fights at every turn, and perhaps with little ultimately to show for the effort – the little juice you extract may prove not to be worth the squeeze.

Who could blame investors like Pande, then, who emphasized the value he sees for startups who think from the outset about how to collect data that (in contrast) works well with AI, and is designed from the ground up with that application in mind.  This seems to be the approach that prominent drug discovery startups like insitro (Andreessen-Horowitz-backed) and Recursion are taking, for example. 

While this doesn’t solve the problem about what to do about all the legacy data stuck in existing systems – which Tom Siebel, recall, describes as a (the?) competitive advantage of incumbent companies in an increasingly digital world — it feels like a contemporary example of what happened to factories after the arrival of electricity, as I described in this column last year. While most factories rapidly converted to electricity, established industries (due to sunk costs) were reluctant to extensively rework or reimagine their factories – they kept the design the same, and just substituted electricity for steam-power. The real beneficiaries were the emerging new industries, who had both the need and the opportunity to design work flows from the ground up, unencumbered by existing approaches. This led to the design of the modern factory. 

Similar new opportunities – where entrepreneurs can freshly leverage the power of new technology while minimizing dependency on the limitations of legacy technology – seem to represent the kind of investments that VCs like Pande are seeking out today.

Transparency and Trust

A thoughtful conversation between Atul Butte, a physician-scientist who oversees health data science for the entire University of California (UC) system (you can hear his Tech Tonics episode here) and Cora Han, UC Health’s newly-minted Chief Health Data Officer – explored why interactions with health systems and tech companies are now appearing so regularly in the news (see this WSJ, this WSJ, this WSJ, this FT, this JAMA commentary, and this JAMA commentary).   

Health systems contracting with technology companies is hardly new or unusual, Butte noted, wryly adding that it seems like only when specific names are attached to the two (such as “Ascension and Google”) that this common type of relationship is suddenly  portrayed as “sinister.” Cora suggested that factors contributing to the apparently escalating concern include (a) the potential for staggering scale, and (b) the theoretical intersection of medical and consumer data, which “seems scary.” She emphasized the foundational importance of “trusting the entities with whom you interact.”

Atul Butte

This connects with a related discussion of the role of transparency in increasing trust, a point several speakers emphasized. For example, Butte noted that if a company in stealth mode (meaning no information about it is publicly available) comes to him and asks to explore access to UC information, Butte tells them not to bother; if the company doesn’t even have a website and other basic information easily accessible, he’s not going to refer them to anyone in his organization.

Interestingly, several speakers on my panel – Helminen and Martin Stumpe (now SVP for data science at Tempus and previously the founder and head of the Cancer Pathology initiative at Google) – both emphasized the role of data visualization can play in fostering trust in technologies, especially AI, that can often seem inscrutable. 

At the same time, as Butte astutely suggested, there may be a bit of a double standard here in demanding this of technology since “physicians are also black box,” and can arrive at decisions of dubious quality via an uncertain and impenetrable process, as Atul Gawande and others have eloquently documented.

Regulation and outlook

Michael Pellini, a VC at Section32 (and former CEO of Foundation Medicine) expressed a strong sense of optimism regarding the near-term outlook for both technology itself and the approach to it he’s seen from regulators (more on this below). From a reimbursement perspective, he anticipated the outlook for therapeutics is likely going to get much worse (presumably a comment on the rising concerns around drug pricing), while diagnostics – where entrepreneurs have struggled for reimbursement for a long time, as Pellini presumably knows all too well — may see marked improvement in their future (presumably a comment on their increased ability to guide patients towards demonstrably better outcomes).

Michael Pellini

Similarly, life science VC (arguably the dean of life science VCs) Brook Byers effusively praised the commitment of the FDA to seek out improved technologies, citing two “heroes” – FDA Deputy Commissioner Amy Abernethy (see here, listen here for her Tech Tonics interview, and here on The Long Run) and FDA ophthalmology expert Malvina Eydelman.

His biggest worry, he said (a concern I share) is the sort of sentiment voiced in a recent NYT masthead editorial, urging the FDA to “Slow down on drug and device approvals.”  The Times argued,

“The F.D.A. has made several compromises in recent years — such as accepting ‘real world’ or ‘surrogate’ evidence in lieu of traditional clinical trial data — that have enabled increasingly dubious medical products to seep into the marketplace. [New FDA Commissioner] Dr. Hahn ought to take a fresh look at some of these shifting standards and commit to abandoning the ones that don’t work. That will almost certainly mean that the approval process slows down — and that’s O.K.”

To be sure, regulators have an intrinsically difficult task – if they’re too strict, promising drugs take longer to reach patients (if the medicines reach patients, or are even developed, at all); if regulators are too permissive, then patients can be exposed to harmful products before the danger is recognized.  However, as appealing as it may be to lean into the adage “first do no harm,” as critics such as the NYT are wont to do, invoking this perversion of the precautionary principle as a justification for moving slowly, it’s critical to realize the extensive harm that inaction can cause as well – as I’ve written here and elsewhere.  Regulators need to balance the totality of risk (including the harms of staunching innovation) and benefit; it’s an intrinsically difficult job given the inevitable uncertainty, and requires nuance and customization — “precision regulation” I’ve called it.

What should be avoided, as Tierney and Baumeister argue in The Power of Bad (my WSJ review here), is encouraging regulators to stomp on the brakes reflexively, driven by an outsized fear of risk, as if informed by the credo, “never do anything for the first time.”

Ultimately, what matters most (as I’ve argued) is real-world performance; a randomized clinical trial, where feasible and ethical, is the ideal approach to demonstrate the potential benefit of an intervention. But the most important parameter is what happens to actual patients taking medicines after approval.  Much of the anxiety experienced by regulators reflects the challenges gathering such data – thus once a medicine is released into the wild (even provisionally), it can be difficult to figure out if is working out as anticipated. 

Here is an opportunity. Improved ability to comprehensively gather and continuously evaluate such data as part of routine care would not only improve patient care, but could also make regulatory approvals less fraught. Visibly, we are a long way from this, yet it’s where we ought to be headed, and the direction, I’m increasingly convinced, healthcare is (slowly) starting to go.

22
Jan
2020

False Heroes: Pharmacy Benefit Managers and the Patients They Prey On

Peter Kolchinsky

[Editor’s Note: this is an excerpt from “The Great American Drug Deal.” The book is now available on Amazon.]

By Peter Kolchinsky

It’s hard to know when actual prices for a particular drug really do go up, because there is so little transparency in pricing. A lot of the public discourse on pricing is based on “list prices,” which no one – neither patients nor payers – actually pays.

As is the case with cars and anything on Amazon, everything is always on some kind of sale or subject to discounts of one type of another.

In the world of pharmaceuticals, these discounts are called “rebates” and often take the form of payments from the drug company back to the insurer. The particulars of a rebate that a drug company offers to an insurer – its magnitude and how it varies according to market share – are kept confidential, essentially based on the age-old sales tactic of “Because you’re special, I’ll give you a special price, but don’t tell the other guy.”

Pharmacy Benefit Managers, or PBMs, are the companies who negotiate with drug companies on behalf of payers (and some PBMs are actually owned by insurance companies, so one canthink of them as just agents of payers), and – importantly – retain a portion of the rebates that pass through them. In effect, PBMs profit from the very high list prices they purport to heroically negotiate down. A biopharmaceutical company offering a lower list price without a rebate would threaten the PBM business model, so PBMs discourage the tactic by not rewarding it. Instead they encourage drug companies to keep publicly known list prices high and give an ever bigger confidential rebate to the PBM, from which the PBMs siphon off their own rent before passing on the lower net price to the payer while boasting, “behold what I have negotiated for you!”

Let’s take a closer look at the numbers to see how all this works (or doesn’t).

In 2018, although list prices for branded drugs increased by 5.5 percent, net prices (what drug companies actually get after discounts and rebates) were essentially flat compared to the year before, having come in nominally 0.3 percent higher, though really lower when adjusted for inflation. So increased prices of some drugs were more than offset by the savings from other drugs going generic. Indeed, total spending (what the US is paying, in total for drugs) is increasing, by about 4.4 percent in 2018 from the prior year, but it’s because more patients are being treated. That should be good news. That’s what progress looks like!

Of course, none of that matters if you are a patient who can’t afford what your physician prescribes—and there are all too many people out there who can identify with this. A major part of the solution requires lowering or eliminating out-of-pocket costs, as discussed in Chapter 4, but it’s worth exploring just how much waste there is in the middle zone between drug companies and patients due to payers’ and PBMs’ tactics.

In 2018, US drug spending based on list prices was $479 billion, yet net drug price spending was $344 billion, approximately 28 percent lower. That means that, even if we stuck to “cost sharing” but simply linked what patients pay to net prices that PBMs negotiate instead of list prices, patient costs would be reduced by 28 percent, saving around $17 billion of the $61 billion in out-of-pocket costs Americans paid in 2018.* Insurance companies and Medicare count on that $17 billion extra from patients to pad their own budgets, allowing them to charge slightly lower premiums/taxes, a perverse kind of insurance policy since it means that the sick subsidize the healthy.

Realistically, being able to negotiate secret rebates is a useful tactic for playing drug companies off one another, as PBMs have done with Gilead, AbbVie and Merck to drive down the cost of hepatitis C cures in recent years. However, right now, some patients are increasingly bearing an unfair burden, and most Americans are being misled about the true costs of important medicines.

To understand why and how, let’s begin with a quick rebate primer.

Rebates and How they Impact Patients

Imagine if an agent offered to help you buy a car and promised that you would only need to  pay her 20 percent of whatever she saved you. You buy a car that is listed at $40,000 by the dealership, but you only end up having to pay $30,000 after your agent negotiates on your behalf. Your agent has saved you $10,000 and retains $2,000 as her fee, so really the car cost you $32,000, and you saved $8,000. That’s still good.

Now, imagine that a car dealership decides to cut out the middleman and list those same cars at $30,000, the same amount the dealership would have received after giving discounts to agents. That would be cheaper than going through an agent since you haven’t to pay the $2,000 fee. That agent won’t direct buyers to that dealership because their prices leave no room for the dealership to offer any discounts, which means the agent won’t earn her commission. If anything, agents will encourage dealerships to raise their list prices, either directly or tacitly. If the agent can pressure the dealership to raise the list price of  that car to $50,000, the agent will be able to negotiate it down by 40 percent to $30,000, earn a $4,000 commission, and come out looking like a hero to the buyer, though the car would now functionally cost $34,000!

This is what’s going on in the drug industry, and it is a big reason why list prices are increasing. The question, of course, is why don’t biopharmaceutical companies bypass the PBMs and sell their products directly to insurance companies? Yes, any company that did  so would be ostracized by the agent community, but why should that matter?

The unfortunate truth is that as PBMs have grown, they have amassed wide influence. They have entrenched themselves as middlemen with massive bargaining power, which stems from how concentrated the PBM market has become. The top three PBMs, Express Scripts, CVS/Caremark, and United’s OptumRx, represent 80 percent of the PBM market and serve insurance plans covering half of the US population.

So, what’s the big deal? PBMs keep a piece of the rebate, but at the end of the day, they are saving patients money, and that’s what matters….right? And that’s the problem: saving patients money matters, but this system doesn’t actually do that. Though rebates save money for society as a whole, currently rebates actually increase the true share of costs patients shoulder.

 

*Consider that saving patients 28 percent by lowering drug prices by 28 percent would render the entire biopharmaceutical industry a non-profit and shutter innovation. So pegging patients’ out-of-pocket expenses to net prices instead of list prices is a much more surgical solution, which payers would compensate for with a tiny increase in premiums, less than 1 percent, though could also absorb by slashing their own bureaucracy.

21
Jan
2020

Seeking to Understand Dr. King’s Vision of Unity, at Our Divided Moment

Rob Perez

[Editor’s Note: a version of this essay was published on Martin Luther King Jr. Day on LinkedIn, and has been edited and republished with permission of the author.]

On this day where we celebrate Dr. Martin Luther King, I thought I’d share a few thoughts about race.

I’m fascinated with how race/ethnicity impacts how we interact with each other. Always have been, from my upbringing in Los Angeles, to my career as an executive and investor in biopharmaceuticals, to my role as founder and chairman of Life Science Cares, an organization that tries to make the world a better place for people of all races who are impacted by poverty. Regardless of our life’s journey, we all have our racial biases, so please indulge me this chance to share some of mine.

My perspective is an unusual one. I’m kind of a racial undercover agent. I’m a person who identifies as African American, mainly because my mixed-race parents originate from the south (New Orleans) which was segregated at the time. In those days, if you had even the slightest bit of “negro” blood, you were classified as “colored”. That meant segregated schools, bathrooms, movie theaters, institutional racism and economic disadvantages, racist police…the whole nine. The genetic facts say that I am actually ethnically mixed, more of a gumbo (the dietary staple of Louisiana Creoles) of genetic roots, with ancestors from Africa, Western Europe, and a little of just about everything else (including Ashkanazi Jew…L’chaim!)

While my parents experienced overt racism during their formative years, my experience was different. More subtle, but still significant in shaping my point of view on my place in society, and how I relate to others who are different. 

Although my family has always proudly identified as black, I don’t look it. I’m light skinned, with hazel eyes, married to an extraordinary woman who is a child of first generation Italian/German immigrants, and I have a Spanish surname. People usually mistake me for Cuban, Puerto Rican, Caucasian. Rarely (if ever) do people see me as an African American.  

So to say I have an unusual perspective into how race is lived in America is an understatement. I’ve often said that my life is like the old Saturday Night Live skit, where Eddie Murphy goes undercover as a white person, to get a behind the scenes look at what real life is like in white America. There’s the moment when he’s bewildered that the mortgage broker he’s meeting insists on giving him money to buy a house. No need to check credit history for a white guy! Just take the money! 

It’s funny, but more like tragicomedy. Like some of the best comedy, it tells us something true about our world that is otherwise hard for us to see.

Like the Eddie Murphy character undercover, I’ve seen Americans as they really are…with their guard down. Suffice it to say that, on occasion, it can be really ugly. Not all the time, or even most of the time, but enough to give me a sense that what many Americans (even educated Americans) of all ethnicities want us to believe about their views towards people of different races, are not always what they seem.

That’s why on this day of seemingly everyone posting their love and admiration for Dr. King, I admittedly pause and wonder how much of his philosophy they really appreciate, accept, or even take the time to understand.

For example, in his famous Letter From a Birmingham Jail, Dr. King writes about his disappointment in the “white moderate, who is more devoted to order than to justice.” And that “shallow understanding from people of good will is more frustrating than absolute misunderstanding from people of ill will.”

I wonder what Dr. King would think of the present day “moderates” of all colors, who disapprove but ultimately accept the racist, xenophobic and divisive principles espoused by some in power, in order to benefit from policies that serve their own economic, religious or social interests.  

America, since its founding, has been at best a contradiction, at worst outright hypocrisy, when it comes to race. It is well documented that when Thomas Jefferson wrote the historic words of the Declaration of Independence, “…We hold these truths to be self-evident, that all men are created equal, that they are endowed by their Creator with certain unalienable rights, that among these are life, liberty, and the pursuit of happiness,” he not only owned slaves, but is believed by some to have been served by one of his slaves at the very time he was committing these words to paper! 

That’s like finding out the author of the definitive text on being a vegan was holding a Big Mac as he wrote the book. 

Our willingness to excuse, overlook or trade-off the oppression of others solely because of the color of their skin, especially if it benefits us economically or socially, is as American as baseball and apple pie.

Many people remain comfortable with this cafeteria approach to racial oppression. As Jefferson said, “Justice is in one scale, self-preservation in the other”, or in today’s words, “I don’t like some of this leader’s words and actions, but I vote for him because I think the country is better off on the whole.”

For many people of color, there is no trade-off/choice. The country isn’t better off when one of the consequences of that choice calls for the inequality and dehumanization of an entire race. Those in power can claim it’s not personal, it’s just politics. The person who swings the baseball bat may not think it’s personal, but to the person who takes the Louisville Slugger to the head, it’s hard to see it any other way. 

We have many issues of great import in our country which people can debate, analyze and compromise. Fiscal policy, health care, even gun control, are all issues that allow for nuance and tolerance of different views. To me, bigotry, on the other hand, is more of a litmus test issue. If even a small part of your political agenda calls for treating me and others as less of a human because of the color of my skin, my country of origin, whether I have a penis, or the gender of the person I love, there is no way I can look past that point of view to find common ground with you on other issues. 

In this time of extraordinary division in our country, and with an election season looming, I fear that identity politics and subtle bigotry will continue to be used to garner support. Fear of the inevitable restructuring of our society as a more diverse, brown, ethnic US is, IMHO, inherently threatening to many who have enjoyed the historical (“natural”) order of things, even if they did not participate actively in the ugliness that made it that way. It is my great hope that we will take more time to seek to understand each other on issues of race, and appreciate the deeply personal nature of its impact on all of us. 

Since it’s the order of the day, I’ll leave you with one of my own favorite quotes from Dr. King.

“We must learn to live together as brothers or perish together as fools.”  Rev. Dr. Martin Luther King Jr., March 22, 1964, St. Louis

16
Jan
2020

Understanding The Ideology Of Digital Transformation

David Shaywitz

The phrase resounding in corporations these days is “digital transformation.”

What does that really mean?

According to proponents, digital transformation reflects the assertion that in order to remain competitive in the modern era, organizations need to radically rethink their approach to how they collect, manage, and analyze information. 

Change is clearly afoot, but the ideology informing this hasn’t been entirely clear, beyond the vague sense that it seems to be driven by an energized alliance of technology and management consultants.

Recently, on the recommendation of a former colleague (DNAnexus CEO Dick Daly), I finally got my hands on what feels like the sourcebook for digital transformation, or at least a clear, contemporary expression of what digital transformation is and why consultants are pushing it.

The 2019 book – appropriately entitled Digital Transformation – is written by Tom Siebel. He’s a billionaire tech entrepreneur who has spent his career developing enterprise technology, and is currently the CEO of c3.ai, a firm that (besides sponsoring NPR), provides enterprise AI. That puts him in position to both support and benefit from companies undergoing digital transformation. 

So of course it’s easy to dismiss Siebel’s book for being exactly what it is – an elaborate white paper that seeks to create a burning platform, motivating executives to urgently adopt the the sort of changes that would clearly benefit Siebel’s business. (Proceeds from the book itself apparently go to charity, according to the jacket cover.)

However, it would be a mistake to reflexively dismiss the book as a self-serving exercise. Much of Digital Transformation rings true, and resonates with so much I’ve seen and heard in multiple organizations. It feels like an extremely relevant and timely read, written by someone who understands both business and technology, and speaks to issues that every organization I know is trying to manage. 

Having said that, there’s very little in the book specifically about biopharma and healthcare, and much of what’s there seems unlikely to resonate with many domain experts. I suspect this disconnect reflects the lack of progress to date in these industries, combined with Siebel’s limited first-hand experience here.

The Burning Platform

First, the burning platform. According to Siebel, the intersection of four significant “technology vectors” – cloud computing, big data, artificial intelligence (AI), and the internet of things (IOT) – is driving such profound change in the environment in which organizations live that businesses face as “mass extinction event.” Companies are fading from relevance at unprecedented rates, CEO tenures are growing ever shorter, and private equity firms are piling up increasing amounts of dry power, ready to pounce on corporations perceived as laggards.  Companies, argues Siebel, “are facing a life-or-death situation.”

In case this is still too subtle, Siebel writes, in a chapter on AI in the defense industry:

“AI will fundamentally determine the fate of the planet. This is a category of technology unlike any that preceded it, uniquely able to harness vast amounts of data unfathomable to the human mind to drive precise, real-time decision-making for virtually any task.” He adds that as the US and China engage “in a war for AI leadership,” the “fate of the world hangs in the balance.”

Of course, motivating change requires not just a reason to change (unambiguously provided here), but also a direction forward – in this case drawing inspiration from a transformational event in the earth’s history:

“Recall how the Great Oxidation Event’s cyanobacteria and oxygen resulted in new processes of oxygenic respiration. Today, cloud computing, big data, IoT, and AI are coming together to form new processes, too.  Every mass extinction is a new beginning. Changing a core competency means removing and revolutionizing key corporate body parts. That’s what digital transformation demands.”

Siebel reviews the distinction noted by organization theorist (and author of Crossing the Chasm) Geoffrey Moore. Moore draws the distinction between a company’s core – what creates differentiation, e.g. Tiger Woods’s golf skill – and a company’s context – everything else, such as marketing. Thus, Woods may make a lot of money from marketing, but his core, his competitive advantage, is how he plays golf. At a level of simplification, says Siebel, core is often viewed as intellectual property, while context is often outsourced. Siebel argues that many companies have digitized their context competencies, but not their core – but that is exactly what’s required, he argues. 

Such change constitutes a difficult process that often requires a strenuous re-thinking of the underlying business, creating “something faster, strong, and more efficient that can do the same job in a totally different way – or do entirely new things.” 

The key opportunity, Siebel argues, is for companies to “use data to reinvent their business models.”  The change required is profound – and, argues Siebel, it must be driven by the CEO, rather than by the chief information officer or anyone else.

According to Siebel, “implementing a digital transformation agenda means your organization will build, deploy, and operate dozens, perhaps hundreds or even thousands, of AI and IoT applications across all aspects of your organization, from human resources and customer relationships to financial processes, product design, maintenance, and supply chain operations. No operation will be untouched.”

The Four Technology Vectors

The four technologies shaping our future, according to Siebel, are cloud computing, big data, AI, and IoT. In a nutshell:

  • Cloud computing provides convenient access for all businesses to essentially unlimited compute and storage, with major providers (Amazon Web Services [AWS], Microsoft’s Azure, Google Cloud) routinely providing robust security and continuously improving resources, characterized by the “rapid innovation of microservices” such as Google’s TensorFlow designed to “accelerate machine learning.” Adds Siebel, “not a week goes by without another announcement of yet another useful microservice” from a leading cloud vendor.
  • Big data refers not so much to the raw quantity of data collected, managed, and analyzed, but really to the mindset towards data – the idea of collecting everything, versus just a sample; in other words, “complete data.” As Siebel nicely puts it, the “significance of the big data phenomenon is less about the size of the data set we are addressing than the completeness of the data set and the absence of sampling error.“ (Whether this is achievable, or impossibly hindered by either technical or social/political barriers, is a topic we’ll return to shortly.)
  • AI involves computers tackling problems that normally require human intelligence. Machine learning (ML) is a subset of AI that involves teaching computers to learn from experience, rather than pre-defined rules. ML might be used to train an algorithm to assess whether an image has a cat or not; this process tends to require a lot of “feature engineering,” where data scientists and domain experts determine what are the key parameters to feed into the algorithm to help it become more accurate.  Deep learning is a subset of ML where “the important features are not predefined by data scientists but instead learned by the algorithm.”
  • IoT is the idea of connecting “any device equipped with adequate processing and communication capability to the internet, so it can send and receive data” – essentially, the “convergence and control of physical infrastructure by computers.”

These four technologies, Siebel observes, present “powerful new capabilities and possibilities. But they also create significant new challenges and complexities for organizations, particularly in pulling them together into a cohesive technology platform.” Not surprisingly, “many organizations struggle to develop and deploy AI and IoT applications and scale and consequently never progress beyond experiments and prototypes.”

Digital Transformation: Implications For Healthcare

Digital transformation, Siebel asserts, will “improve human life.” How? Though “very early disease detection and diagnosis, genome-specific preventive care, extremely precise surgeries performed with the help of robots, on-demand and digital health care, AI-assisted diagnoses, and dramatically reduced costs of care.”

Skeptical about whether healthcare – characterized famously by Esther Dyson as “calcified hairball” system of care – can be disrupted? Siebel’s rejoinder (cited multiple times) is that in January 2018, when Amazon, Berkshire Hathaway and JP Morgan Chase announced their intention to enter the market, “$30B of market capitalization was erased from the 10 largest U.S. healthcare companies” in a single day of trading. While these stocks recovered almost immediately, the market reaction, according to Siebel, emphasizes the industry’s vulnerability.

Cloud

While Siebel doesn’t offer specific examples of healthcare and the cloud, he shares his view that executives who less than a decade ago proclaimed “our data will never reside in the public cloud” – something I personally heard from a number of healthcare leaders even five years ago – are now delivering a very different message that is “equally clear and exclamatory: ‘…we have a cloud-first strategy. All new applications are being deployed in the cloud.  Existing applications will be migrating to the cloud. But understand, we have a multi-cloud strategy [to avoiding vendor lock-in].’”  While healthcare was among the last to the cloud, it seems many health organizations have finally gotten the message.

Big Data

Siebel highlights the potential value to precision medicine of being able to access “the medical histories and genome sequences of the U.S. population.” His point, it seems, is that “big data” thinking enable us to contemplate considering the data of each person, rather than generalizing from a sampling of people.  Actually acquiring anything approaching such a complete data collection, of course, is a non-trivial real-world challenge, as most in biopharma and healthcare recognize — and often lament. In biopharma, technical (as well as financial) limitations may stymie efforts to collect and subsequently analyze all possible information in human beings and other complex biological systems.

AI

Siebel is clearly taken by the potential of AI in healthcare, while acknowledging “the health care industry is just starting to unlock value from AI. Significant opportunities exist for health care companies to use machine learning to improve patient outcomes, predict chronic diseases, prevent addiction to opioids and other drugs, and improve disease coding accuracy.”

He suggests machine learning algorithms can be used “to predict the likelihood someone will have a heart attack, based on medical records and other data inputs – age, gender, occupation, geography, diet, exercise, ethnicity, family history, healthy history, and so on – for hundreds of thousands of patients who have suffered heart attacks and millions who have not.” (This again assumes it’s possible to get one’s hands on enough of the relevant data to train the algorithm. That’s profoundly difficult in today’s environment, beset by the problems of data interoperability, patient data hoarding by hospitals, proprietary EHRs that can’t/won’t talk substantively to each other, and an ecosystem of stakeholders who aren’t inclined to share data.)

Applications for deep learning in healthcare, according to Siebel, include “medical image diagnostics, automated drug discovery, disease prediction, bone-specific medical protocols, preventive medicine,” though additional detail isn’t provided.

Perhaps especially relevant to medical practitioners, Siebel suggests that “the ability to apply AI to all the data in a dataset” means that “there is no longer the need for an expert hypothesis of an event’s cause.  Instead the AI algorithm is able to learn the behavior of complex systems directly from data generated by those systems….The implications are significant…An experienced physician [in Siebel’s future world, presumably] is no longer required to predict the onset of diabetes in a patient.”  Instead, this information can be gleaned “from data by the computer – more quickly and with much greater accuracy.” I am aware of glimmers of progress in this area, which has been discussed for over a decade.

IOT

Siebel suggests that connected devices “give doctors the opportunity to track patient health remotely in order to improve health outcomes and reduce costs.  By harnessing all these data, IoT supports doctors in predicting risk factors for their patients.”  He notes that pacemakers “can be read remotely and can issue alarms to doctors and patients, warning if a heartbeat is irregular.” He reports that the “wearable industry has given people the ability to easily track all sorts of health-related metrics.” Combining wearable information with clinical data, he observes, “can create a holistic view of the patient, allowing doctors to deliver better care.”

So far so good, right? But Siebel isn’t through. “Soon,” he contends, “humans will have tens or hundreds of ultra-low-power computer wearables and implants continuously monitoring and regulating blood chemistry, blood pressure, pulse, temperature, and other metabolic signals. These devices will be able to connect via the internet to cloud-based services – such as medical diagnostic services – but will also have sufficient local computing and AI capabilities to collect and analyze data and make real-time decisions.”

I’m not sure even most quantified selfers would embrace such a future; if anything, this vision seems to evoke folk singer-songwriter Arlo Guthrie’s memorable description of his military physical examination during the Vietnam War era, where “they was inspecting, injecting every single part of me, and they was leaving no part untouched.”

Siebel points out that large sets of IoT-generated data can “uncover insights and make predictions,” such as using “AI predictive analytics to find potential barriers to medication treatment and identify potential contraindications. This gives doctors the tools to more effectively support patients, improve outcomes, reduce relapse, and enhance quality of life.”

He continues, “Imagine pill bottles that track adherence to prescribed medications, alerting doctors and users when patients fail or forget to take their medication. Also in development are smart pills that can transmit information on vital signs after being ingested.” (I’m sure Otsuka can envision this quite clearly….)

Finally, if you’re not creeped out yet by this degree of monitoring, Siebel, in pointing out that “data generated everywhere through an organization can have value,” reports that today, “Insurance companies…work with mining and hospitality companies to add sensors to their workforces in order to detect anomalous physical movements that could, in turn, help predict worker injuries and avoid claims.”

In this vision of digital transformation the future of both work and health apparently involves, and certainly aspires to, ever-more detailed monitoring and assessment of every facet of existence. It’s a vision that sounds like total, continuous surveillance.

Not only is this approach exceedingly, absurdly, invasive, but it may not even deliver the cost-savings Siebel repeatedly promises, as my Tech Tonics co-host Lisa Suennen points out:

“Tech can only reduce healthcare costs when financial interests are aligned,” Suennen reminds us.  “Digital products for early diagnosis can just as easily lead to excessive testing and treatment when the impetus is to increase utilization (which increases cost).  It is true that technology such as AI and robotics have the potential to lead to cost-reductions in healthcare, but there is far more to it than technology alone.  As with all technology, it is a tool, not a solution.  When the solution one is solving for is to increase revenue, the tool can work just as well in the hands of someone who benefits from increased cost.”

In short, Siebel’s perspective on the ideal future state of healthcare feels both dissonant (I’m not sure most people wanted to be constantly monitored for failure, like IoT-enabled equipment constantly surveyed by a technician) and elusive (based on the challenges of gathering even modest amounts of integrated health data in one place); moreover, as Suennen argues, it may not even deliver the beneficial economics Siebel anticipates.

Digital Transformation: Implications For Organizational Change

In contrast, Siebel’s observations on barriers for organizations contemplating digital transformations seem thoughtful and highly relevant, particularly regarding data, people, and prioritization.

Data

Siebel premise is that “successful digital transformation hinges critically on an organization’s ability to extract value from big data,” and a key initial challenge is how to organize all the data in the first place.  But the good news, argues Siebel, is that large established companies are starting on their journey with one key advantage: they’re already sitting on a lot of data (though unlocking value from these data might be another story).

Argues Siebel, “incumbent organizations have a major advantage over startups and new entrants from other sectors. Incumbents have already amassed a large amount of historical data, and their sizable customer bases and scales of operations are ongoing sources of new data.”

He acknowledges, “Of course, there remain the considerable challenges of accessing, unifying, and extracting value from all these data.  But incumbents begin with a significant head start.”

The challenge is what to do with all these legacy data.  The temptation is to put it all in one place, a so-called data lake or data swamp. Not smart, Siebel argues.

“Storing large amounts of disparate data by putting it all in one infrastructure location does not reduce data complexity any more than letting data sit in siloed enterprise systems. For AI applications to extract value from disparate data sets typical requires significant manipulation such as normalizing and deduplicating data,” Siebel observes, adding “the key big data challenge “is to represent all existing data as a unified, federated image.”

People

To operate in this brave new world requires comfort with both the data and the emerging ways of thinking about data. Writes Siebel: “Generating value requires individuals in the enterprise who are able to understand all these data, comprehend the IT infrastructure used to support these data, and then relate the data sets to business cases and value drivers. The resulting complexity is substantial.”

Interestingly, and (based on my experiences over the years) perceptively, Siebel calls out what he describes as a common mistake: overconfident CIO’s who mistakenly (in his view) believe they can assemble the required data and analytics structures on their own, DIY-style. Siebel says he’s observed this sort of misplaced confidence from the time he was at Oracle, selling enterprise application software, and realized that their biggest barrier wasn’t competitors, but the CIO who wanted to solve the problem DIY, and, according to Siebel, generally failed. (Again: take with a grain of salt, given Siebel’s obvious interest in selling enterprise software.)

Siebel notes that companies obviously require more than just data experts – they also need “translators” who “can bridge the divide between AI practitioners and the business.  [Such translators] understand enough about management to guide and harness AI talent effectively, and they understand enough about AI to ensure algorithms are properly integrated into business practices.”

But what it seems like companies need most of all, according to Siebel, is a ton of consultants – or as he politely refers to them, partners: “In a digitally transforming world,” he says, “partners play a bigger role than in the past.” He explicitly writes companies should involve management consultants for strategy, software partners for technology, professional services to build apps, and change management partners to get people to use to the new tech.  Suddenly, you can begin to understand why “digital transformation” is so broadly embraced: it’s like an Oprah giveaway but for consultants (YOU get more consulting work, and YOU get more consulting work, and YOU get more consulting work…).

Priorities

While Siebel’s advice regarding consultants feels a bit self-serving, his advice about prioritization seems spot-on, and certainly aligns with what I’ve been suggesting, as well as with the advice that experts I admire, like Jim Manzi, seem to be offering.

Above all, says Siebel, focus on business needs, not abstract, highfalutin aims. “Work incrementally to get wins and capture business value,” he emphasizes. Much as Vizzini, in The Princess Bride, famously advises “never get involved in a land war in Asia,” Siebel’s counsels (perhaps for similar reasons) “Do not get enmeshed in endless and complicated approaches to unify data. Build use cases that generate measurable economic benefit first and solve the IT challenges later.” He also suggests adopting a “phased approach to projects,” seeking opportunities to “deliver demonstrable ROI one step at a time, in less than a year.”

He notes that “Many organizations get hopelessly mired in complex ‘data lake’ projects that drag on for years at great expense and yield little or no value.” There are many examples, he says, of companies wasting big money on such projects. He cites multiple examples of companies wasting years with “outside consultants to build a unified data model, only to see no results at all.”

While the use-case first approach “may sound like heresy to a CIO,” Siebel says that this approach “allows for focus on the value drivers.”  The emphasis of a digital transformation strategy, he argues, should be “creating and capturing economic value.”  Fulfilling this value mandate requires thoughtful roadmap and prioritization, “identifying and prioritizing functions or units that can benefit most from transformation.”

Finally, counsels Siebel, use common sense. “If a project does not seem to make sense, it’s because it doesn’t make sense. If it appears incomprehensible, it is likely impossible. If you do not personally understand it, don’t do it.”

Figuring out how to apply this admonition to use common sense to areas like healthcare and biopharma – where the benefits touted by technologists often don’t seem sensible (as both Derek Lowe and I observed this week), but in some cases, could be truly transformative — represents both the challenge and the opportunity of our moment.

15
Jan
2020

Atomwise and EQRx: Two Contrasting Strategies for the R&D Inefficiency Problem

David Shaywitz

Pharma innovation expert Bernard Munos captures the inherent inefficiency of drug development with two fascinating statistics he recently shared with me. 

First, for large pharmas, the average cost of developing a new drug (simply based on the total R&D costs divided by the total number of new drugs approved for sale) works out to about $5B per drug. It’s an astronomical number, and one that keeps growing to a worrisome degree. The Munos analysis encompasses both the cost of failures and what he calls the cost of scale. In contrast, the actual cost to get a single drug approved for smaller companies – an analysis that omits the cost of failure because it doesn’t look at the many small companies that tried to advance drugs and failed – works out to a bit over half a billion dollars, or about 10-fold less.

One implication of these data is that in large pharmas, drug discovery seems terribly inefficient, with huge amounts of money going into products that never become approved drugs. Another implication, says Munos, is that large pharmas are, theoretically, quite vulnerable to disruption, since they “need every day of their patent life to recover that cost and fund an ever-growing R&D budget that keeps producing the same output.” That’s another way of saying their existing operating model requires extracting all available revenue from existing approved products.

It hasn’t escaped anyone’s notice that it would behoove pharma to make R&D more efficient, as even small increases in the rate of success at any stage would be expected to translate into improved overall R&D efficiency. However, achieving such efficiency gains has remained remarkably elusive, despite the hundreds of millions of dollars that have been spent on management consultants, and despite the execution of continuously refreshed restructuring initiatives generally driven by said consultants.

Two very different companies making news at JPM20 say they have an approach that could make a dent in the R&D statistics: Atomwise, the AI-for-drug discovery company led by Abraham Heifets, and EQRx, former VC Alexis Borisy’s ultra-buzzy, on-Zeitgeist fast-follower newco. Both seem to be focused on dramatically different aspects of drug development, yet they share a commonality in their approach that’s worth a closer look.

Atomwise

San Francisco-based Atomwise, founded in 2012, seeks to use AI to accelerate the identification of promising molecular compounds, with a particular emphasis on drugging the undruggable.   In the last week, they’ve announced a new partnership with the accelerator BioMotiv, and the extension of a 2017 collaboration with Bayer.

Atomwise’s thesis is that while the overall probability of success (POS) for any early stage compound is quite low, the actual POS is naturally much higher if you remove a key aspect of the risk; one way of accomplishing this is by targeting something you are certain will have an impact on disease, if only you could access it.  The thinking is that often, new disease targets represent, at best, hopeful, educated guesses, but still involve a huge amount of biological risk – as well as the many other risks (such as safety, tolerability, clinical efficacy) associated with getting a new chemical entity all the way to the point of FDA approval. 

Heifets argues that his platform, like CRISPR, is valuable precisely because it enables drug developers to physiologically manipulate established targets in a way that was previously unachievable.  As he writes, “the excitement around CRISPR, protein degradation, and RNA-targeting techniques is justified because these techniques offer us the chance to drug fundamentally new targets that were not otherwise attainable by other methods,” adding “The future of drug discovery is in using new technologies to drug the undruggable.”

Munos, for his part, worries that the targets Atomwise is attacking are not as de-risked as the company may assume. “There is no such thing as a validated, undruggable target,” he notes, explaining “the only validation that can be trusted is that which comes with a drug approval. Before that, targets may be interesting or promising, but they are not validated.”  He adds, “Most of the clinical trials that fail aim at targets that are thought to be validated.  Yet toxicity and insufficient efficacy are the most common causes of trial failure.” Munos’s comments echo the old pharma saw that the definition of a validated target is one where there’s already a drug with $1 billion in sales.

EQRx

Cambridge, Mass.-based EQRx, announced this week, represents a response to the problem of costly drugs. Borisy, a former partner at Third Rock Ventures, says he sees a market opportunity in pursuing established targets, and essentially undercutting pricey first-to-market products. His thought is that by focusing on established mechanisms, you can make new drugs for much less money, because you anticipate a far lower failure rate (you know the target is both relevant and targetable) than the typical innovator company. This first requires making a new chemical entity that eludes the innovator’s original patents. Then, presumably, EQRx can perhaps also design more efficient clinical studies by leaning on established examples. 

You can think of Borisy’s approach as “pre-generics,” perhaps (with apologies to the pre-cogs of Minority Report), although he aspires to make drugs that are somewhat better than first-in-class products. The economic argument is that his reduced costs and development time will enable him to get new molecules onto the market before the first-in-class product goes generic, and to sell this fast-follower at an aggressive low price, but that still allows for significant gross profit margins. Borisy expects to be able to do this for multiple products. As Luke described it earlier this week, “the idea at EQRx is to use the bursting knowledge of biological targets and new treatment modalities to make fast-follower patented drugs that are sold at radically cheaper prices – maybe 50, 60, 70 percent cheaper than others in a given class.”

While noting the profound transformative potential EQRx would have if successful, by cutting deeply into pharma’s anticipated revenue over the patent life of an approved drug, Munos nevertheless remains skeptical:

“Given the long lead time of drug R&D, in order to reach the market before the pioneer drug becomes generic, the ‘fast-follower’ must get going long before the drug it follows gets approved. And if the lead drug stumbles, so does the fast-follower. EQRx apparently thinks it can tweak the fast-follower model by waiting until a drug has been approved — thus validating its mechanism — before it gets going and still reach the market long enough before the lead drug loses its patent. This would require an improvement in the speed of drug R&D that has never been seen before despite pharma’s decades of relentless efforts at process improvement (e.g., six sigma). It would be a monumental achievement.”

A Shared Focus on De-risking

While Atomwise and EQRx are focused on very different problems, both are leveraging a similar strategy: improve the overall probability of success by attacking something that’s already (somewhat) de-risked.  For Atomwise, this means creating a new compound for an established target that no one’s been able to drug, and drug it for the first time; for EQRx, this means creating a new molecule for an important target that’s already been drugged, and doing it faster/better/cheaper. 

Each is betting that while the overall economics around new drug development are dispiriting, the value proposition for a candidate drug that’s derisked can be far more promising.  Both companies, as Munos points out, face real challenges as they strive to deliver at the scale necessary to make the still-difficult math work. 

In some ways, Atomwise may have the easier lift.  Even if only a few compounds are ultimately successful, the individual drugs could support the growth of the company (assuming the company retains adequate economics in the products, which will apparently be developed by partners – this is a critical consideration). Atomwise could succeed even if the platform doesn’t meaningfully alter the grim R&D statistics for the industry as a whole. 

EQRx has not gone into significant technical detail about how, exactly, it will go about achieving its needed gains in speed and cost. But whatever technologies it brings to bear will have to be remarkable to achieve its founding promise. EQRx has to deliver multiple fast-followers through all phases of compound development and clinical testing, with enough speed, enough economy, and a high enough success rate. That’s a very high hurdle, though also a worthy ambition.

13
Jan
2020

A New Cholesterol-Lowering Drug at a Low Price: Tim Mayleben on The Long Run

Today’s guest on The Long Run is Tim Mayleben.

Tim is the CEO of Ann Arbor, Michigan-based Esperion Therapeutics.

Tim Mayleben, president and CEO, Esperion Therapeutics

Esperion is bucking a few of the trends you’ve seen in biotech the past decade. It has developed a cholesterol-lowering drug, bempedoic acid. The drug is currently under review by regulators in the US and Europe. It is expected to be cleared for sale in 2020, likely on its own, and in a combo form with generic ezetimibe (once known under Merck’s brand name Zetia).

Instead of aiming the new drug at a targeted niche of patients with a rare disease, or certain genetic characteristics – the popular thing over the past decade — this is a drug being aimed at the masses. We have a lot of people in the US with high LDL cholesterol who are at high risk of heart attack, stroke, and death from cardiovascular disease.

Esperion is entering a crowded marketplace. On one end, are the cheap, convenient, generic, orally-available statins. These drugs were once Big Pharma’s bread and butter. On the other end, with a greater ability to bring down LDL cholesterol – but also with higher, brand-name price tags – are the PCSK9-directed antibody drugs. The overpricing of the PCSK9 class was a disaster (which I anticipated in a column back in 2012).

Esperion has studied that tale, and has sought to learn from it.

Heading into the 2020s, how does Esperion seek to carve out a niche for itself and compete? It does have a different scientific mechanism than others in the class of cholesterol-lowering drugs, but that’s not the main feature here.

The big idea — wait for it – is by offering a potent, brand-name cholesterol-lowering drug at a low price. At least by today’s standards. It’s best to listen to Tim explain his thinking on price, which he does toward the end of the show. But without giving too much away, he believes it’s the right thing to do for patients, and for society. It’s also going to allow Esperion to make plenty of money and reward its investors – all of these goals can be achieved simultaneously. Maybe, just maybe, this is a drug that could still compete in a new world governed by something like Medicare-for-All.

Before we go into all of that, you’ll hear about Tim’s story. He’s not a scientist. He encountered some real challenges to get where he is today. Clearly, some of the values he picked up early in life have an influence on the decisions he and Esperion are making today.

Now, please join me and Tim Mayleben on The Long Run.

12
Jan
2020

EQRx Taps Zeitgeist, Raises $200m For Innovative Drugs at Aggressive Low Prices

Alexis Borisy is smart. As in high IQ.

But there’s more to biopharma than that.

“As my grandmother used to say, ‘You can be smart, smart, smart…but dumb,’” Borisy said.

Alexis Borisy, chairman and CEO, EQRx

Some old wisdom is part of what’s driving Cambridge, Mass.-based EQRx. The company, started by the former Third Rock Ventures partner and backed by $200 million of “smart money” in a Series A venture capital round, has plenty of IQ. The industry has no shortage of that.

What’s different is that EQRx sees – and is seeking to attack — the weak underbelly of biopharma industry EQ, as in emotional intelligence quotient.

Not only has the industry committed many egregious pricing offenses over the past couple decades, there are standard operating practices (opacity on prices, de facto permanent patenting strategies, shady stalling of generics and biosimilars to name a few) that scream of arrogance and amorality.

Tone-deaf for too long, the industry needs to do better by patients. Pitchforks are out, rightly.

The founders, and investors, in this startup are well aware that drug discovery is entering a golden age. But price gouging has damaged the industry’s standing so much that it just might kill the golden goose.

That would be dumb.

The idea at EQRx is to use the bursting knowledge of biological targets and new treatment modalities to make fast-follower patented drugs that are sold at radically cheaper prices – maybe 50, 60, 70 percent cheaper than others in a given class.

Borisy, 47, the former partner at Third Rock Ventures, believes the latest science and technology tools can be connected to a new biopharma business model that society can live with. He has the credibility to take on this task. (See July 2018 analysis of the TRV public portfolio). He was the founding CEO of Foundation Medicine and Blueprint Medicines, and has his fingerprints on more portfolio successes.

There’s a window of opportunity to undercut first-movers on price to that extent, and still make a lot of money “a ton of money” for investors, Borisy said. There’s reason to believe a pharma company making what he calls “equivalars” – new chemical entities that are equally good, or slightly better, than first-movers in a category – but which could still fetch 80 percent gross margins.

During an interview last week, Borisy was enthusiastic – more passionate than I’ve ever heard him in the dozen or so years I’ve known him – about creating new drugs with fast and lean development plans. About forming productive working relationships with payers and providers and cost-effectiveness research outfits like ICER. About recruiting smart drug hunters, computational people, and business thinkers willing to reinvent musty old models. About raising tons of capital beyond the original $200 million to accomplish an audacious objective of rolling out a first drug in five years, then 10 drugs in a decade, and dozens and dozens more within 15 years.

He kept making analogies to JetBlue and Amazon. These are companies in quite different industries. Both found flab in their industries, ruthlessly cut into it, passed on savings to consumers, lived on narrower margins, and still built thriving businesses.

For a guy who loves science, and has thought about traditional company-building for so long, low-cost drugs may sound like heresy. If even faintly successful in the next five years, it would invite the full competitive wrath of entrenched companies with billions of dollars on the balance sheet. There are easier things to do in life.

But while taking some time off at his Cape Cod house to chill out after his run at Third Rock, Borisy said this was an idea that he couldn’t shake.

Melanie Nallicheri, president and COO, EQRx

“I tried to put it down, but couldn’t,” he said. He invited friends over to talk, to flesh out the idea, think about who would need to be involved, and what it would take. Melanie Nallicheri, formerly the chief business officer at Foundation Medicine, was one of those people. She’s now the president and chief operating officer.

Why, I asked Borisy, would he go all-in on this?

“It’s the right thing to do,” he said.

A few of the key decision-makers involved are:

  • Alexis Borisy, chairman and CEO; former Third Rock Ventures partner
  • Melanie Nallicheri, president and COO; former Foundation Medicine chief business officer
  • Robert Forrester, CXO; former CEO of Verastem Oncology
  • Sue Hager, SVP of corporate affairs and citizenship; former chief communications officer, Foundation Medicine
  • Peter Bach, co-founder and advisor
  • Sandra Horning, co-founder and advisor
  • (Borisy hinted that more people in the payer and cost-effectiveness community will be getting involved, but aren’t yet ready to be announced).

Investors include:

  • GV
  • Arch Venture Partners
  • Section32
  • Casdin Capital
  • A16Z
  • Nextech
  • Arboretum Ventures

When I asked Borisy if there was hesitancy among investors about putting money into a company that’s explicitly about showing self-restraint and charging less than it could for new medicines, the answer was No. Some investors who wanted in couldn’t get in. At least for the A round.

Essentially, VCs are watching the political tea leaves carefully, and see candidates having a lot of success railing against drugmakers, as part of the rationale for a Medicare-for-All program. The kind of price controls that would be necessary for such a program are anathema to many biopharma investors.

But a market-based company that undercuts complacent incumbents who are overcharging?

It’s unorthodox, but conceivable.

“We are fitting the zeitgeist of the moment,” Borisy said.

Borisy wasn’t saying exactly which sub-category has jumped to the top of the development list, but oncology is one category ripe for price competition. Rare diseases is another possibility. Small-molecules and certain kinds of biologics can be made quickly at miniscule costs, making these the modalities of choice for disruption.

To get further perspective, I corresponded with GV partner Krishna Yeshwant and Peter Bach, the director of the Center for Health Policy and Outcomes at Memorial Sloan Kettering Cancer Center in New York, and a prominent critic of pricing abuses.

To give TR readers a little extra context, I’ll run these exchanges in mini Q&A form below.

Timmerman Report: Was this an obvious investment for you, or did you have to get over some hurdles before writing the check?

If so, what persuaded you to go in?

Krishna Yeshwant, partner, GV

Krishna Yeshwant: I think when many people think of GV investing in healthcare they think of Health IT and Digital Health working in the payor/provider world (which we are active in of course), but as you know we have additionally been very active in biotech.  I’ve long found that the people in each of these groups don’t know each other. The people working in biotech venture and entrepreneurship don’t go to the same conferences as the payor/provider oriented investors and entrepreneurs and, with a few exceptions, generally wouldn’t be able to identify one another if they were in the same room.

I think that fracture is core to one of the large issues in the healthcare industry. Namely that the therapeutics and the payor/provider worlds are polarized. Therapeutics companies often think payors are being unfair by not reimbursing their products, while payors are often frustrated that therapeutics companies don’t clearly define the value of the drugs they are trying to bring to market.

I was excited by EQRx because I loved the idea of connecting Alexis to the payors and providers who we’ve worked closely with for years via the payor/provider side of our investment activity. I wanted to be part of brokering the conversation between these two huge parts of the healthcare industry. My hope is that through this company we can move what has been a zero sum negotiation towards a more productive partnership.

Basically, it was obvious because we wanted to work with Alexis, and wanted to work to realign these parts of the industry. But [there are] many controversial points as well – as is the case for all of our most exciting companies.

Timmerman Report: Why did you decide to get involved?

Peter Bach: A lot of factors, but I think the twin theses behind EQRX are promising.  New drugs in proven classes can plausibly be developed pretty efficiently nowadays, and although the current drug distribution and payment system is mostly upside down (higher prices can lead to greater market share), I think we are at a potentially transformational point, because the people actually paying the bills (taxpayers, patients, employers) are singling out specialty drugs as a pain point. So if we can get a compelling economic opportunity in front of them then maybe this creates the demand the rest of the system will require to change into one focused on delivering cost savings when they are available, and reward lower cost entrants with larger market share. 

LT: I might be wrong, but I don’t suspect you have many industry involvements (given your vocal criticism of high drug prices). Were you skeptical at first when approached by EQRx?

PB: I don’t have a lot of industry involvements, but I very much think my professional life is intertwined with the industry. I know how I think we should prioritize or regulate prices and to what extent access should be the metric of success rather than FDA approval might diverge from many in the industry (and I am not shy about highlighting these differences), I don’t intrinsically doubt the motives of participants in it. So I think I approach every conversation with an openness to further understanding, and in this case the proposition really aligned well with my priors regarding what opportunities had arisen that could actually play some small role in transforming the market at least towards one where lower prices of drugs enabled garnering of market share, rather than impeding it as a lot of evidence suggests is the case today. 

LT: How did the principals persuade you to get involved?

PB: I have known Alexis and also Krishna for a long time, this wasn’t about persuasion it was at least from my end a convergence around a common set of perceptions and motivations, and complimentary skills and experience. 

LT: What do you hope to accomplish by working with this company?

PB: I don’t approach things like that. I have always pursued opportunities because I think they are interesting and challenging, will give me a chance to both learn new things and employ the knowledge and skills I already have towards important objectives that will have positive spillovers for others, and frankly be around people I like.  Going into government met these standards, so did becoming a doctor, and I gauged that EQRX did as well.

LT: Do you think one company can provide enough market force to bring real downward pressure on prices, or is it going to take multiple companies following this sort of model to actually bend the cost curve down?

PB: I honestly don’t know, but because it is you, I will fall back on “the longest journey begins with a single step.”

10
Jan
2020

Entering JPM20 With a Grounded, Yet Hopeful, View of Health Tech

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. 

Dazzling only the technologists who are developing the technology, while VC backers proclaim its historical inevitability, feels so last decade — and perhaps just a tad onanistic.
9
Jan
2020

Sanofi’s New CEO Captures Pharma’s Grounded View of Health Tech

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:

  • the need to “prioritize data management if we want to get the most out of our AI investments”
  • “organizing interoperable data pools from which we can pull out patterns and trends”
  • the use of cloud-based data systems to “streamline regulatory submissions by using a common data storage platform.”

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.

6
Jan
2020

Three Lessons for Business Ahead of JPM, from the Kilimanjaro Climb to Fight Cancer

Julia Owens, CEO, Millendo Therapeutics

While reflecting on the past year and preparing for the JP Morgan Healthcare Conference in San Francisco, my mind has kept coming back to some simple life lessons picked up on a dirt trail half a world away.

The Kilimanjaro Climb to Fight Cancer was a profound life experience last July. Ever since coming home from the highest peak in Africa, I’ve been thinking about how to incorporate some of the experience from that journey into my daily life as CEO of a small clinical-stage biotech company.

I find myself continually sharing with friends and family the photo memory book we received from Fred Hutch documenting the climb. For weeks, I’ve been looking forward to the reunion, the night before JP Morgan, of the 27 women and men who were part of this expedition.

This was no regular trip. This was special. It forced me to stretch me way outside my comfort zone. For one thing, I hadn’t been camping in more than a decade and never been backpacking. For another, I had no idea what it would feel like to hike in the thin air above 19,000 feet of elevation. And while I have plenty of experience raising money for my company from institutional investors, it was an altogether different experience to raise more than $50,000 for a nonprofit cancer center from family, friends, and colleagues.

I am incredibly proud to have been a part of this. Together, our team raised $1.6 million for cancer research at Fred Hutch. We also forged some meaningful and enduring friendships.  

Six months after this special experience, I’d like to share three lessons that helped get me up the highest peak in Africa, and that have influenced both my professional and personal life.

Lesson 1: Make it work for you

Mt. Kilimanjaro is not a technical climb. You need good hiking boots, and to be reasonably fit. No ropes, crampons or other technical climbing gear is required. We were given a long (!) list of specific items to bring (including a pee funnel and bottle), but with those in tow, you are pretty much set.

The evening before each day of the climb, our lead guide Eric Murphy would let us know generally what to expect the next day, including how long we would be hiking, what the terrain would be like, how much the altitude rise would be and what to include in our packs (versus what the porters would carry).

From left to right: Luke Timmerman (Kilimanjaro Climb to Fight Cancer team captain), Lakpa Rita Sherpa (guide), and Eric Murphy (lead guide).

Amongst our Type A group there were always numerous questions, mostly focusing on what gear to bring, and if I’m honest, many of them were from me.

Eric, a guide with 100+ summits of Kilimanjaro, has heard it all before from nervous climbers. Each day, he would patiently address a few of the inquiries, but then, when things got a little too far into the weeds, he very deftly cut off the questioning with what became his catchphrase — “Make it work for you”. What it meant was that we had been given the basic information. Now it was up to each of us to judge how that applied to our personal circumstances. For me, it usually meant bringing an extra layer or gloves to make sure I was warm enough. But that was for me to decide.

In life, and particularly in the business world as a biotech CEO, the same advice applies. I may receive input from my board on their expectations, or from our legal counsel or our PR firm on how any particular situation is often addressed. The guidance is useful in a general sense, but is rarely specific to our exact situation. As the CEO, it is up to me, with input from my team, to assess the situation and decide on a course of action. It usually resembles the original recommendation, yet nearly always has variations specific to our company.  

As Eric likes to say, “Make it work for you.”

Lesson 2. Pace yourself

Mt Kilimanjaro is over 19,000 ft high. No matter how fit you are, you cannot prepare for altitude like that without giving your body time to adjust. Many people struggle on Kilimanjaro, and about half fail to reach the summit each year. Usually that’s because people try to go up too fast, and end up getting sick on the way.

While our Machame route and 7-day climbing strategy allowed time for acclimatization, we still had physical challenges at high altitude. On our summit day we tried to sleep a couple hours before leaving camp at midnight. The plan was to reach the peak at dawn. Many of us were tired and short of breath as we climbed in the cold and darkness of the early morning alpine environment.

In order to persevere in these conditions, you need to take a slow and deliberate pace that doesn’t wear you out. Pushing yourself is going to be counter-productive. The Tanzanian team supporting our climb knew this concept quite well. “Pole, pole” [pronounced PO-lay, PO-lay] means “slowly, slowly” in Swahili. We heard this reminder multiple times every day. It took some of us longer than others, but all 27 of us did ultimately summit the mountain that morning.

Julia Owens on Kilimanjaro, July 2019.

As a biotech executive, or even as a busy working parent, there are times where I feel like the mountain in front of me is simply insurmountable. The demands on my time from email inboxes, presentations to review, work travel, board meetings, networking and more can consume every waking hour, and even cut into proper sleep. Simply pushing through is not going to work.

The business world always compels us to move faster and faster. There’s rarely anyone there to caution us with a kind “Pole, pole” reminder to keep us from burning out on our journey to achieve our objective. The best course, often, is slow and deliberate progress forward. I seek to accomplish what I can without over-taxing myself. Which naturally leads to the final lesson.

Lesson 3. Practice self-care

This last lesson is the most obvious and also the most easily ignored. On a week-long climb, we were constantly reminded about the importance of “self-care”.

Self-care on Kilimanjaro.

When hiking for an average of 7-8 hours a day, it’s essential to pay attention to adequate water intake before you get dehydrated and suffer acute mountain sickness. You have to take care of hot spots on your feet before they become blisters. At the end of the day, it’s smart to stretch tired muscles and get a good night’s sleep. Yet, we all had to be reminded numerous times each day of the trip. If we didn’t pay conscious attention to our physical and mental well-being, we weren’t going to be ready for subsequent days, which seemed to get more and more challenging.

During busy day-to-day life, the distractions are greater. The need for self-care can be less obvious and the reminders are less frequent or at least less explicit. It’s easy to over-schedule oneself, and end up neglecting personal time, family, diet, exercise, or sleep. As a biotech CEO at a lean startup, it can feel like nearly everything rests on your shoulders. Pretty soon, you can find yourself in a routine of working 14-hour days, 6-7 days a week, which eventually becomes physically and emotionally depleting.

Trust me… I know this way too well. One of my personal motivations for signing up for the Kilimanjaro team was to give myself a break from the daily pressures after being a CEO of a company that has had significant ups and downs over 7 years with little break. (Listen to The Long Run podcast from last February.)

So now, with the epic summit nearly six months in the past, I’m left with a memory of a lifetime, close new friendships, incredible satisfaction in what we were able to contribute towards advancing cancer research… and, for me at least, some simple but important lessons for life, both on and off the mountain.

Summit of Kilimanjaro (19,341 feet). Julia Owens is pictured on the far right.

1
Jan
2020

New Job, Same Thesis: Aligning Tech & Pharma To Elicit Best Of Both

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:

  • Increasingly abundant and compelling;
  • Intriguing but largely peripheral to R&D today;
  • Going to be core to R&D in the near future.

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:

  • Advances in data science, technology, and digital health, collectively enabled by astonishing advances in computing power and speed, are beginning to permeate how science is done, redefining the sorts of questions we can contemplate and the way we can pursue them; and
  • These advances have generally not yet reached the point of effective implementation in healthcare and biopharma;

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. 

30
Dec
2019

Designer Proteins as Better Therapies: David Baker on The Long Run

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:

26
Dec
2019

Losing 80 Lbs Was Hard; Keeping It Off Was So Much Harder

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.

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