The current challenge for data science and technology (DST) in healthcare is moving beyond the “dancing bear” stage, where “the wonder is not how well the bear dances, but that he dances at all.” It’s time for DST to evolve past the novelty publications and the click bait, and demonstrate its ability to materially impact health and disease.
Vas Narasimhan, chief executive officer of Novartis AG, in a 2018 photo. Narasimhan assumed the reigns as CEO a year ago, emphasizing the importance of digital and data for pharma R&D. Now, a year into his tenure, he reflects on how the technology integration effort has been going. Photographer: Simon Dawson/Bloomberg© 2018 BLOOMBERG FINANCE LP
The need for DST impact in pharma is especially acute, and challenging, as this column has critically explored. In the last year, I’ve focused on the cultural challenges, the distinction between invention and implementation, the frustrations of black box AI, the potential for AI to generate misleading clinical classifiers, the fetishization of big data, and the opportunities to improve clinical research, among other topics. With few exceptions, it seems that DST’s exceptional potential has yet to find meaningful expression in most drug development organizations.
It’s easy to be misled by the deafening buzz. The conference circuit is now exploding with “AI and pharma” conferences (I’m speaking at several), consultants excitedly discuss pharma’s digital transformation (and convince each pharma they’re distinctly behind), and exuberant stories about the power of data and AI resound almost daily across social media.
Yet when you dive beneath the froth, a very different scenario is revealed. In an achingly perceptive 2017 essay, Dr. Sachin Jain, former CMIO at Merck and now CEO of CareMore Health, candidly laments the unexpected gap between discussions of tech innovation in healthcare and actual tech innovation in healthcare, describing our present state as an “innovation bubble.”
“There is a disconnect between the conferences I attend, the journals and blogs I read, and the reality of medical practice on the frontlines of healthcare delivery. There is a ‘change layer’ – the cloud in which visionary ideas about transforming health care resides. But there is also a ‘reality layer’ – the place where most care is delivered. Both are necessary, but there is little mixing between them. So while there is a booming innovation industry – a new startup being created every day, a new app being launched every minute – the actual experience of delivering or receiving care is changing scarcely.”
While Jain touches briefly on pharma (he shares my observation that the eternal dream of creating a “service beyond the pill” has struggled), his focus is on healthcare delivery. Enter Vas Narasimhan.
Pharma, Powered By Data & Digital (?)
Narasimhan is the 42 year old physician and former McKinsey consultant who joined Novartis in 2005 and took the reigns as CEO last year, drawing attention with his focus on technology. “We need to become a focused medicines company that’s powered by data science and digital technologies,” he told the Wall Street Journal in Feburary 2018.
Now, a year into this new role, he’s had a chance to reflect on his company’s “digital journey” (as his McKinsey colleagues might say), which he’s graciously done in an absolutely captivating podcast interview with several members of the silicon valley tech VC firm Andreessen Horowitz (best known for its slogan, “software is eating the world”). General Partners Jorge Conde and Vijay Pande, and Editorial Partner Sonal Chokshi, sat down with Narasimhan during the recent JP Morgan conference in San Francisco.
It’s hard to imagine a better window into how a forward-thinking large pharma, led by a relatively young, innovative physician leader, is actively wrestling with the issues and many challenges of incorporating DST approaches into their R&D efforts. The entire episode is a must-listen; I’ve extracted several key highlights, below, which reinforced in my mind both the difficulty and the urgency of leveraging DST approaches in pharma R&D.
Narasimhan sets the stage by pointing out the industry’s miserable attrition rate: of the twenty drugs that enter clinical studies, only one makes it. Worse, this rate apparently hasn’t moved in the last fifteen years, while costs have continued to increase). This is the fundamental problem (as I’ve also emphasized) for which the industry is trying to solve.
Narasimhan suggests increase costs reflect the increased complexity of clinical trials (essentially due to an increased amount of work each trial is being asked to do, gather information for regulators, of course, but also data that address scientific as well as market access questions). He reports that tech may be able to excise up to 20% of this cost (which sounds like a classic consultant SWAG).
Responding to a question, he said he saw a role for engineering especially in improving their processes around the manufacture of new categories of medicines, such as cell therapies and gene therapies, where he says the field is still at the “learning to crawl” stage. He also suggested engineers could contribute to chemical biology, helping to design more effective molecules rather than rely on empiricism to discover them.
AI – But First, Data
When asked about AI and ML, he began by level-setting: “As we’ve gotten quite scaled and working on digital health and data science, we’ve learned there’s a lot of talk and very little in terms of actual delivery of impact.”
Wow – “very little in terms of actual delivery of impact.”
“We’ve learned a lot,” he continued.
“The first thing we’ve learned is the importance of having outstanding data to actually base your ML on. In our own shop, we’ve been working on a few big projects, and we’ve had to spend most of the time just cleaning the data sets before you can even run the algorithm. That’s taken us years just to clean the datasets. I think people underestimate how little clean data there is out there, and how hard it is to clean and link the data.”
He did call out several areas of promise for AI. The first involved imaging – Novartis has embarked on a massive project to digitize all of their pathology images, he said, partnering with a startup called PathAI, as a prelude to machine learning. He can envision repeating this process for other categories of images as well. At the moment, this feels like a work in progress – lots of images being digitized, value of the effort TBD.
“Sounds like a gold mine,” Chokshi observed.
“It should be,” Narasimhan cautiously replied.
There are two areas where AI approaches are apparently already delivering actionable results: clinical trial operations and finance. In operations, Novartis has set up a control center that monitors all of their clinical trials. As Narasimhan describes it,
“A team sitting centrally in our headquarters can look at all our clinical trials in the world, and AI is predicting which of our trials are going to enroll on time or not enroll on time, and which ones are going to have quality issues or not have quality issues, and the reasons we could do this because we had ten years of history to train the algorithms, and we run about 400-500 clinical trials a year, so we had a lot of data on which we could train the algorithms.”
He notes that they’re not looking at patient-level data, but are, deliberately, a level up from that.
He also says that AI is proving quite useful in finance. “AI does a great job predicting our free cash flow,” he says, “predicting a lot of our sales for key products. It does better than our internal people because it doesn’t have the biases, and the data are really clean, and we have a lot of long-term data.
In short: one ambitious but unproven AI effort in science, two apparently successful efforts in non-science (operations and finance). Even there, I wonder if the role of “AI” is perhaps overstated, and means something different and less profound than when used in the DeepMind/AlphaZero context, but that’s just my hunch.
To his credit, Narasimhan also pointed out an area – in science, predictably — where AI was clearly coming up short:
“The Holy Grail of having unstructured machine learning go into big clinical data lakes and then suddenly finding new insights – we’ve not been able to crack, mostly because the data…to link it up..….We are spending a lot of our energy just trying to get all of our data harmonized, so that some algorithm could maybe find anything of use.”
The Problem With Real World Data
Interestingly, Narasimhan, with deep roots in clinical development, was distinctly critical of real world data – or at least, skeptical that data extracted from EHRs could or should replace traditional randomized controlled trials (echoing what a number of clinical trialists told me).
“I do believe that the power of randomization, the power of blindedness, it’s what enables us to control for all the things we don’t know about complexity of human life and human biology. To think we’re going to take that away, and then be able to really determine the efficacy of a medicine, puts a lot on the statistics that I don’t think we have. I’m more of a real world evidence realist, after we have randomized placebo controlled data that really tells us that something has the effect we think it does, then to explore more effects or explore more uses through real world evidence makes a lot sense, but I don’t see this as a panacea that suddenly will make the world much easier.”
But what about the “sensorification” of the world, Narasimhan was asked – doesn’t the increased adoption of wearables (seen as transformational for medicine by some enthusiasts) fundamentally change the game, and create more value from these approaches? Short answer: no.
“In general, sensors is another place where there’s been a lot of hype about expectations. We’ve been really trying to explore the use of sensors in clinical trials now in my own experience for at least six years – it’s been tough trying to get sensors that really meet clinical trial grade outcomes, that really show that they can be validated vs our current clinical endpoints. As consumer products, fine, perfect. But here, we need to really be able to replace what are pretty rigorous tests, and we haven’t seen that yet. Now we’re exploring the use of many different sensors. The real power of it is a continuous variable to actually see how a patient is doing between the study visits. I think that will help a lot but I still think in the end you’ll need to randomize and blind. If you don’t randomize I think it’s really hard to figure out what’s going on in a complex system.”
Why Each Successful Drug Is A “Miracle”
Narasimhan clearly remains interested in the potential of DST; his experiences have left him both hopeful and cautious – and I couldn’t resonate more. What I think Narasimhan understands and viscerally appreciates – and what many tech folks struggle with – is that for all the pretty mechanistic diagrams of biological pathways, and precise schematics describing drug actions, our actual understanding of biology and disease is astonishingly poor. This is the core truth that everyone in pharma groks.
In 2008, Nassim Taleb and I wrote in the Financial Times,
“For all the breathless headlines proclaiming breakthrough discoveries, the truth is that we still do not understand what causes most disease. Even when we can identify a responsible gene or implicate an important mutation, we have made only limited progress in turning these results into treatments.”
Similarly, look what Merck R&D head Roger Perlmutter told Matthew Herper (then at FORBES) in 2013:
“…if we’re discovering drugs, the problem is that we just don’t know enough. We really understand very little about human physiology. We don’t know how the machine works, so it’s not a surprise that when it’s broken, we don’t know how to fix it. The fact that we ever make a drug that gives favorable effects is a bloody miracle because it’s very difficult to understand what went wrong.”
And now, this month, look what Narashahim went out of his way to emphasize at the end of his podcast interview:
“What’s often lost on people is how incredible it is that we find any human medicines at all. Every human being is 40 trillion cells, working together. We understand a fraction of the proteins, what they do. 1200 drugable proteins, there’s only a fraction of those we can actually drug. We don’t know what most of RNA does, non-coding RNA, we don’t know most of what the genome is even talking about. Since the creation of FDA there’s only been about 1500 new molecular entities ever found, and most of those are overlapping in similar therapeutic areas. If you account for double counts, my guess is it’s in the hundreds of medicines that we’ve actually found.
It’s worth reflecting on how hard it is to do what we do. I tell our people, you have to think every medicine we find is a miracle that fits in the palm of your hand. We’ve unlocked, in a sense, a billion years of evolution of the eukaryotic cell and human biology and somehow we found something able to move the needle in this incredible complex system. I think that’s easy to forget when we overly simplify what we do.”
While humility is not generally the first quality one associates with CEOs, physicians, or McKinsey consultants, our staggering collective biological ignorance, and the enormity of the challenge of drug development, brings even the mighty to their knees.
For technologists hoping to impact disease, it will be critical to move away from solutionism, the belief that an app or an algorithm will effortlessly solve the complex and often ill-defined medical problems that plague patients and preoccupy drug developers. DST entrepreneurs need to evince some appreciation of the messy complexity of biology, and to understand just what they’re getting into – and to recognize that even a successful product is likely to solve only a very small (though potentially important) part of the overall problem.
As Narasimhan emphatically conveyed, our understanding of the human organism, in both health and disease, is exceptionally primitive. It requires a strange combination of audacity and foolhardiness to believe you can create a product that will impact disease in a meaningful way. We need to bring our best technology – biological and digital – and our most creative people together to work on this monumental challenge.
One clear take-away from the Narasimhan interview: pharma is at the very earliest stages of figuring out how to do this.
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