David Shaywitz

Biopharma, like the rest of the world, appears to be on the threshold of profound, technology-induced change. Incredible advances in artificial intelligence, manifested most recently in GPT-4, are here. 

This technology, Ezra Klein explains in the New York Times, “changes everything.”  Bill Gates describes it as “the most important advance in technology since the graphical user interface,” and declares, “the age of AI has begun.” Similarly, Times columnist Thomas Friedman argues:

“This is a Promethean moment we’ve entered — one of those moments in history when certain new tools, ways of thinking or energy sources are introduced that are such a departure and advance on what existed before that you can’t just change one thing, you have to change everything. That is, how you create, how you compete, how you collaborate, how you work, how you learn, how you govern and, yes, how you cheat, commit crimes and fight wars.”

At an entrepreneurship salon at Harvard this week, I discussed GPT-4 with Dr. Zak Kohane, Chair of the Department of Biomedical Informatics at Harvard University (disclosure: I’m a lecturer in the department), and Editor-in-Chief of the soon-to-be-launched NEJM-AI.  

Kohane received early access to GPT-4. He has just completed a book, The AI Revolution in Medicine: GPT-4 and Beyond, to be published in mid-April, about the impact of emerging AI technology on healthcare. I read an advanced, draft copy of the book. Kohane’s co-authors are Peter Lee, Corporate Vice President and Head of Microsoft Research, and Carey Goldberg, a distinguished journalist. 

From both the book and the salon, the three most striking features of GPT-4 seem to be:

1. Its ability to reason;
2. Its ability to communicate and engage with people in natural language;
3. The fact that no one really understands how it works.

How did GPT-4 impress Kohane? For starters, it performs spectacularly on standardized exams like the medical boards, and seems to be able to reason thoughtfully, Kohane says.

Zak Kohane

For example, we discussed the ability of GPT-4 to respond to an apparent paradox that Kohane says stumps the vast majority of the non-physician data scientists. The question: why is a low white blood cell count between midnight and 8 am associated with far worse outcomes than a low count between 8am and 4 pm? 

GPT-4’s top suggestion, Kohane says, was the correct answer: the issue isn’t so much the low blood count but rather the existence of a blood draw in the middle of the night. That signals the patient is experiencing some sort of medical crisis. 

GPT-4 can also provide sophisticated differential diagnoses, Kohane says, and suggest relevant next steps. 

He posed GPT-4 a question from his own specialty, pediatric endocrinology:

“I gave it a very complicated case of ambiguous genitalia that I was actually called for once back in my training. And it’s able to go through everything from the clinical presentation to the molecular biology. It had a disagreement with me and was able to cogently disagree with me, and it was also able to articulate concerns for the parents of this child and for the future engagement of the child in that discussion. So, on the surface it’s acting like one of the most sensitive, socially aware doctors I’ve ever met. But we have no guarantee that it is such.”

The ability of GPT-4 to engage in such a human-like fashion is one of the most striking, and disarming, characteristics of the technology. Many who engage with GPT-4 over time describe the sense of developing a close relationship with it, in a fashion that can feel dislocating. Kohane, Lee, Friedman and others all describing losing sleep after spending time with GPT-4. They are overwhelmed, it seems, by the power and possibility of what they’ve experienced.

 “GPT-4’s abilities to do math, engage in conversation, write computer programs, tell jokes, and more were not programmed by humans,” wrote Lee, of Microsoft Research. These capacities emerged unexpectedly, he wrote, “as its neural network grew.“

Peter Lee, Corporate Vice President,
Microsoft Research

This creates what Lee calls a “very big problem.” He writes: “Because we don’t understand where GPT-4’s capabilities in math, programming, and reasoning come from, we don’t have a good way of understanding when, why and how it makes mistakes or fails….”

The implications are dizzying. As Kohane writes, “I realized we had met an alien agent and it seemed to know a lot about us, but at the moment, I could not decide if it should be given the keys to our planet or sealed in a bunker until we figured it out.”

Implications for Healthcare

Some AI experts, like University of Toronto professor and author Avi Goldfarb, said the AI technologies will serve as a democratizing force in healthcare. He suggests, in a podcast interview with Patrick O’Shaughnessy, it will be able to “automate diagnosis,” with the consequence of “upskilling” the “millions of medical professionals” like nurses and pharmacists. The consequence, he suggests, is that:

“There’s hundreds of thousands of doctors in the U.S. and their special skill in diagnosis is going to go away. They’ll have to retool and figure out how to deal with that. But there’s millions of other medical professionals who are now going to be able to do their jobs much better, be more productive. And that upskilling provides a lot of what we see as the hope and opportunity for AI.”

I asked Kohane about this, and (to my surprise) he seemed to largely agree, albeit with a slightly different framing.  He notes that we have a crisis resulting from a shortage of  primary care doctors. Massachusetts (as the Boston Globe has recently reported) is being hit particularly hard. While we may have an idealized view of how the best primary care doctors can treat patients, Kohane argues, this is generally not the lived reality, and suggests that a nurse practitioner or physician assistant, coupled with AI, could generally offer a higher level of care for patients than a typical primary care doctor without AI. Given the shortage of trained medical professionals, AI can help improve the quality and quantity of available care.

It’s also clear that patients will have access – and, through Bing (which works with GPT-4 when accessed from Microsoft’s Edge browser), already have access – to this knowledge and information. 

Many patients and caregivers are eager for this capability, as Goldberg writes in the book. The problem is that GPT-4 (like surgeons and Harvard grads) tends to be frequently correct but rarely in doubt. It still suffers from the problem of “hallucinations,” making up information that sounds plausible but isn’t accurate. Like humans suffering from the Dunning-Kruger Effect, it can insist that it’s correct, when it’s wrong. I saw that when GPT-4  tried to persuade me that Goose, not Merlin, uttered the line “That MIG really screwed him up,” in the original Top Gun. 

For now, everyone seems to acknowledge the hallucination problem, and call for “human in the loop” approaches. But what happens as we gain more confidence in GPT-4 and, motivated by both convenience and cost, are increasingly tempted to take the human out of the loop?

Stepping Back

What seems clear is that we are truly experiencing what economist Carlota Perez has described (see here) as the “irruption phase” of emerging technology. We recognize that there’s something promising and incredibly exciting, and now everyone is trying to figure out what to make of it, and how to apply it.

These days, it feels like every healthtech person I know who hasn’t started their own VC fund (as noted here) is either starting their own health+AI company or writing a book about health+AI, and in some cases both. Microsoft Office programs are about to be boosted by GPT-4. Other companies, such as the regulatory intelligence company Vivpro – are already offering GPT-powered tools. Every consultancy is offering executives frameworks and navigation guides to the new technology.

The truth, of course, is that no one has any idea how things are going to evolve. The pharma company of the future, the healthcare system of the future, the payor of the future, perhaps even the FDA of the future: all are likely to be profoundly changed by technologies like GPT-4. 

The technology will likely first arrive as incremental, point solutions, says Goldfarb, of the University of Toronto. Eventually, however, the real productivity gains arise from more fundamental change.  The classic example here, as I’ve discussed, and as Goldfarb also cites, is that dropping electric generators into factories built around steam power didn’t have much impact. But reconceptualizing the structure of factories from the ground up, in a fashion enabled by electricity, was transformative.

As Microsoft AI expert Sebastien Bubeck observes in Kohane’s book, “GPT-4 has randomized the future.  There is now a thick fog even just one year into the future.”

What an amazing, terrifying, thrilling, and hopeful time to be alive.  For those of us in medicine and biomedical science: what an opportunity, and profound responsibility, to be in the arena, actively shaping the future we hope to create and aspire to inhabit.

David Shaywitz

The success or failure of many technology platforms — including in particular health tech platforms — rests with a largely obscure role of outsized importance: the “solutions engineer.” 

The role itself goes by many names. Back when I was at DNAnexus in the mid-2010s, this role was called “Solutions Scientist.” Others call it “Forward-Deployed Engineer” or “Embedded Analyst.”

Whatever the title, the solutions engineer (SE) is a technology or data expert who embeds within an organization that’s trying to figure out how to make a new technology platform work. The SE functions as an on-site super tech-support specialist who helps the customer use the technology and get as much value from it as possible. While the SE doesn’t need to reside in the customer’s organization, the SE must inhabit the customer’s challenges and workflow — and having a desk (real or virtual) within the customer’s team can help.

Richard Daly, CEO, DNAnexus

“In a high velocity health information technology environment such as we are in,” says DNAnexus CEO Richard Daly, the SE is the “key person” for both selling and service, because “they get inside the customer’s skin, co-own the problem, and fit the solution to the customer need.”

At the same time – and arguably, this is the most critical aspect – the SE also develops a richly nuanced understanding of the needs of the customer and is able to provide this intelligence back to the product manager and the rest of the engineering team, so that the platform can be evolved to more effectively meet the needs (and future needs) of the customer, and presumably other future customers.

In serving as a “key transmission point” for both customer and tech company, Daly notes, the SE “improves product development and fit-to-market.”

Moreover, Daly adds, since “customers in the health tech market are scaling,” which introduces its own set of challenges, a well-functioning team of SE’s can “draw on industry and other customer experiences, in a way syndicating industry-wide knowledge, and ensuring continuing fit as the customer evolves.”

SEs come in different flavors, says Dr. Amy Abernethy, President of Product Development and Chief Medical Officer of Verily. 

“The type of SE depends on the type of tech itself. Sometimes the tech is purely software, in which case you need someone fluent in the intersection of software development and customer needs. Sometimes the tech developer is supporting mostly data products, in which case the SE looks more like an embedded analyst who has data empathy, an understanding of customer analytic needs, and a sense of the possible from a data perspective. In pharma, the tech product is becoming increasingly more a combo of software and data, and the SE needs to be able to blend both of these skills.”

Your Initial Solution May Not Be Your Customer’s Exact Problem

A lot of the thinking here comes back to the advice offered by Lean Startup author and entrepreneurship guru Steve Blank, who emphasized the need for the technology developers to spend as much time as possible with actual customers, to ensure that the needs the developers have prioritized are actually the same needs customers face. 

In this context, SE roles provide the opportunity for essential fine tuning; after all, if a tech platform wasn’t in the ballpark, the SE would never have been given the opportunity to engage with a customer in the first place, and certainly wouldn’t be in the privileged position to go work on a customer’s team. 

The most successful SE’s exhibit an authentic interest in the customer’s business – they are innately, intensely curious about the work the customer is doing, and the problems the customer is trying to solve.  A high EQ SE can become an enormously valuable member of the customer’s team, a critical resource that enables them to function better and achieve more.

At the same time, it is absolutely critical that the SE is not regarded as simply as an implementation specialist, someone who gets a team up and running on a new system. Rather, the most significant opportunity the SE affords is to provide an inquisitive and adaptable technology team with a sense of what is and isn’t working well, and how the technology could more effectively meet customer needs. If the technology team isn’t thirsty for and responsive to this feedback, a valuable opportunity is lost.

Significant Challenge And Opportunity for Biopharmas

The need for SEs applies not only to technology companies who are developing platforms, but also, in biopharmas, to internal technology teams as well. Indeed, because of the abiding, largely unbridged differences between the seemingly immiscible cultures of life-science trained drug developers, and engineering-oriented technologists, effective communication and shared understanding can be a challenge, as I’ve discussed here and here.

Amy Abernethy, president of product development and chief medical officer, Verily

In this context, Dr. Abernethy points out, SE’s can “help with ‘lingua franca,’ translating terms between customers and developers.”

Adds Dr. Abernethy, “This task is also becoming more important within the tech and pharma companies themselves as we see a confluence of actions/capabilities across teams within the companies. Building lingua franca will be a key accelerant across the industry and the SE can help.”

In large biopharmas, it’s common for technology teams, after a highly-structured, well-intentioned needs-gathering exercise, to put their heads down and set up developing and deploying a technology solution. 

Afterwards, there’s often little rejoicing. Tech teams tend to grumble about how their brilliant technology is not being efficiently utilized, inevitably calling for more “change management,” while customers routinely complain that their most important needs are (still) not being met. It’s not uncommon to hear technology teams push for mandates that compel customers to use the new technology – an effort that’s received about as well as you might expect. What’s worse, this pattern seems to repeat all the time – it feels like the rule, not the exception.

In many cases, a critical missing link is an SE role – a person with “amphipathic” qualities embedded inside the customer teams to help apply and troubleshoot the technology, and (critically!) to provide ongoing granular feedback to the tech teams about how the tech could evolve to meet customer needs more effectively.

Success critically requires both curiosity and a commitment to continuous iteration on the part of tech teams. These teams must want to deeply understand customer challenges, and ideally begin to viscerally understand what the customers are trying to do, the problems the customers are trying to solve. At least as importantly, the tech teams must also see the relationship with their customers as a constant, on-going dialog. This can be particularly challenging in biopharma when technology teams are often more accustomed to obtaining and then building towards a set of fixed specifications. 

There’s another complicating factor: many customers — especially within biopharmas — may not be all that clear on what they initially want from technology, or understand what might (or might not) be possible. This understanding can evolve and sharpen over time, particularly when catalyzed by a skilled SE, and enabled by a tech organization that’s driven to constantly refine the product on offer.

An Evolving Role

As technology plays a more central role in healthcare and biopharma, the role of the SE is likely to evolve accordingly. As Dr. Abernethy observes, “The technologies that are being developed are more and more informed by our understanding of underlying biology and/or data/implementation elements from clinical care. I wouldn’t be surprised if the phenotype of the solutions engineer of the future has a bit more science or clinical knowledge built in, and the job descriptions will similarly become more sophisticated.”

Perhaps reflecting her previous experience as Deputy Commissioner of the FDA, Dr. Abernethy adds:

“The SE may also need to generally understand the implications and requirements of many different regulatory paradigms. We see this right now, because sometimes the SE looks like a person who understands computational biology and software engineering, sometimes looks like a person who understands clinical and EHR data coupled with the needs of outcomes researchers delivering a dataset for a regulatory filing, and sometimes looks like a person who understands when a product crosses over the line to being regulated clinical decision support.” 

Phrased differently, the SE role reflects and embodies the need for constant dialog and evolutionary refinement between those developing digital, data, and technology offerings, and those who hope to leverage these powerful but still unformed or unfinished capabilities.

Bottom Line

A key gap in technology deployment, particularly in biopharmas, is the space between what technology teams develop and what customers actually want and need. A skilled solution engineer (SE) can bridge this gap and serve as a bidirectional translator, helping customers more effectively utilize the technology, and guiding technology teams to create improved solutions. Success requires not only a skilled SE, but also a curious and adaptable technology team driven to elicit, understand, and respond to customer needs – even (especially) when these needs are difficult for the customer to articulate.

David Shaywitz

Most biopharma companies have started down the path of digital transformation – a fundamental overhaul of everything they do for the digital age.

It’s not clear yet that anyone has arrived at the desired destination.

Even so, there have been some early wins, generally related to operations, as the CEOs of both Novartis and Lilly have described. Arguably, the most significant R&D success has been the organizational alignment and focus afforded by accurate, up-to-date digital dashboards, reflecting, for example, the status of the COVID clinical trials that Pfizer was running, as discussed here.

Behind the scenes, many biopharma R&D organizations have been exceptionally busy trying to apply emerging digital and data technologies to improve every aspect of how impactful new medicines are discovered, developed, and delivered. This strategic focus – and my current job description – is an industry preoccupation.

R&D represents such a vast opportunity space for emerging digital and data technologies that it can be difficult to keep track of all the activity across this expansive frontier. But a recent podcast delivers. A January 2023 episode of BIOS hosted by Chas Pulido and Chris Ghadban of Alix Ventures, and Brian Fiske of Mythic Therapeutics features Sanofi’s CSO and head of research, Frank Nestle. He provides a comprehensive, fairly representative introduction into the many ways biopharmas are approaching digital and data. He also shares insights into key underlying challenges (spoiler alert: data wrangling).

Frank Nestle, chief scientific officer, Sanofi

Nestle is a physician-scientist and immunologist by training; he has been with Sanofi since 2016, and in his current role for about two years.

Below, I discuss the key points Nestle makes about digital and data across R&D, and then offer additional perspective on these opportunities and challenges.

Vision: The Great Convergence

Nestle envisions that we’re heading towards a “great convergence between life sciences, engineering, and data science.” He adds that the “classical scientific foundations of physics, chemistry, and biology” have each “had their heyday.” Now, he says, “it’s data sciences and A.I.”

AI/ML Impact on Research: Optimizing the Assembly Line

Early drug development, Nestle argues, can be understood as an assembly line, where a new molecule is designed and then serially optimized. At each stage, “we are optimizing drug-like properties, like absorption, biodistribution in the body,” he explains. Historically, decisions along the way were made by people – often with extensive experience — sitting around a table and reviewing the data. Now, Nestle is trying to collect the rich data associated with each step in a more systematic way, so that A.I. can contribute. 

At the moment, Nestle says, the focus is on using data science to optimize each individual step, but allows that eventually, a “grand model” might be possible. 

Nestle notes that both early focus and early successes involve small molecules. For example, the number of potential molecules that must be synthesized and evaluated in the course of making a potential small molecule drug has been reduced, he said, from 5,000 to “several hundred.” He asserts Sanofi remains  interested in applying A.I. to the optimization of biologics. The challenge is “complexity.” Nevertheless, he suggests that using A.I.-based approaches to optimize biologics holds “at least as much promise, if not more promise, than in the small molecule space.”

To accelerate their efforts in the early drug discovery and design space, Nestle says, Sanofi has partnered with a number of tech bio companies including Exscientia, Atomwise, and Insilico Medicine. 

Translational Research: Organizing Multimodal Data

The process of understanding the molecular basis of disease, Nestle says, begins with “molecule disease maps.” The best description of these maps that I found was actually from ChatGPT (a technology I recently discussed), which reports that the term:

“refers to a visual representation or diagram of the molecular interactions and processes that are associated with a particular disease. This map can include information about the genes, proteins, and signaling pathways involved in the disease, as well as how these elements interact with each other. The goal of creating a molecular disease map is to gain a better understanding of the underlying causes of a disease and to identify potential targets for therapeutic intervention. By mapping the molecular interactions involved in a disease, researchers can gain insights into the complex biological mechanisms that drive disease progression and develop more effective treatments.”

According to Nestle, “these molecular disease maps are becoming more complex and more rich in data sets by the day.” He continues, “It started with mainly genetic datasets, but now we have expression data sets at every single level from RNA to proteins to metabolites. And we look at these molecular disease maps actually at a single-cell level.” 

The upshot, he says, is that these “provide us with an incredible space of data to interrogate.”

The algorithms used to analyze these data are often simple cluster analyses, Nestle says, but the goal is always to “reduce dimensionality” and find a “signal in these sometimes very noisy datasets.”

These molecular disease maps not only inform biomarker identification, but also assist with the identification of patient populations who might be particularly well (or poorly) suited for specific medications. 

Also contributing to the translational work, according to Nestle: a partnership with the French-American company Owkin. He cites their expertise in both federated machine learning (focused on clinical data associated with various medical centers) and digital pathology. 

Emerging Trial Technologies and Patient-Centricity

Nestle describes digital biomarkers as “absolutely ready for prime time.” He cites the use of actigraphy (see this helpful explainer from Koneksa) in the assessment of Parkinson’s Disease as a promising example in an area – neurology – where such biomarkers are critically needed because “trials just take too long.” He also mentions an approach under development, pioneered by MIT professor Dina Katabi, that repurposes a typical wireless router to monitor activities such as itching and scratching in some skin conditions.

Sanofi, like all biopharmas, is interested in decentralized trials; Nestle highlights a partnership (initiated pre-pandemic) with the company Science37. He also sees a “clear future not only for (the use of technology) in patient recruitment but also remote patient monitoring.” The adoption of technology to enhance trial recruitment and patient monitoring, he says, has been accelerated, dramatically and irreversibly, by the pandemic.

Nestle also emphasizes the role and importance of real world data (RWD) as a tool to better understand patients and their journeys. Insights from RWD can be used to improve “study feasibility or sample size optimization or endpoint modeling,” he says, and points to a “journey mapper” Sanofi has used to integrate and interpret RWD. This approach helped identify additional indications for Sanofi and Regeneron’s IL-4/IL-13 inhibitor dupilumab (Dupixent). That work has translated into benefits for a broad range of patients — and more revenue for the company.

Finally, Nestle highlights internal work on “integrated platform data solutions.” That sounds like efforts focused on supporting a drug after it’s launched through the provision of enabling technologies connecting patients, physicians, and data.  

Limitations: Analyse Sexy, Données Difficiles

Perhaps Nestle’s most important comments concern the limitations of advancing A.I. and other emerging technologies. And the most difficult challenge – “the ultimate bottleneck,” he asserts –is “the data.” 

He continues:

“Right now, data often exists in silos, they’re fraught with missing values – those zeros, as we call them — they’re not labeled correctly, they’re difficult to find. And that’s probably one of the biggest hurdles … that whole effort of generating, aggregating, normalizing, processing the data sometimes outweighs the actual analysis effort.”

He points out that “building foundations” – required for thoughtful data management – “is not necessarily a KPI [key performance indicator]” for large pharmas (who tend to be more focused on near-term measurements of performance). Hence you can only accomplish this, he says, with strong strategic support from “very senior leaders.” (This support may prove both more elusive and more essential in the context of Sanofi’s disappointing 2023 outlook – see here.)

A second problem Nestle points out are the well-intentioned country-specific regulations governing data protection. These policies tend to be quite fragmented across nations and healthcare systems. That impedes the flow of data, and complicates opportunities to learn from the aggregated experiences of patients. The federated approach used by Owkin represents one approach to managing some of these challenges.

Additional Reflections

Nestle offers a comprehensive and generally upbeat assessment of the opportunities before us in the application of emerging digital and data technology to R&D. 

Additional opportunities I’m particularly excited about include imagining what may be possible (more accurately, anticipating what seems likely to be possible) through current and future large language models like GPT-3 and (soon?) GPT-4. One example: effortlessly matching clinical trial protocols to the patient populations already present at certain medical centers. 

There are additional significant challenges that are easy to lose sight of, particularly in the context of such a compelling vision. For instance, I’m impressed by the challenge of timely digital biomarker development and validation. In a regulatory environment where teams struggle to validate electronic administration of well-established paper scales, the challenge of validating wearable parameters is often substantial, and doing this at the same time you’re developing the molecule you hope to use the digital tool to assess is exceptionally, often prohibitively ambitious. The many levels of complexity, and relatively constricted timelines, can be overwhelming.

On the clinical trial front, the universal embrace of decentralized trials – an obviously patient-centric concept that is endorsed and pursued by most everyone – belies the many challenges in pulling these off at all, to say nothing of doing them efficiently and reliably, and using available technology platforms (PR promises aside). The complexity and expense of actually executing meaningfully decentralized trials (versus, for example, conducting a single check-in remotely and calling it a win) is the elephant in the room (one of them) that isn’t discussed in polite company, but which preoccupies many of us who believe deeply in the concept of decentralized clinical trials and are eager to see the promise fulfilled.

Also not to be underestimated: the challenge of organizing multimodal data on a platform where the data can be accessed and analyzed. I appreciate the value of giving names (like “digital molecular map” or “journey map”) to important problems and significant data missions. Ultimately, though, success depends on the quality of the underlying data and the utility of the platform on which these data are housed and analyzed. This is arguably yet another area where vendor slideware seems far ahead of actual user experience.

Some of the most significant challenges digital and data efforts face within R&D are (remain) organizational. Traditional drug developers – those in the trenches, doing the work – are still trying to figure out what to make of data science and data scientists in what is still a largely traditional drug development world. This challenge is compounded by a massive gap between the hype of technology and contemporary reality. 

On the other hand, when there is compelling, readily implementable technology, it’s enthusiastically adopted.  Every structural biology group, for instance, routinely uses AlphaFold, the program that predicts protein folding (structure) based on underlying amino acid sequence data.  

An area of real opportunity (and acknowledging my own bias) is translational medicine, where practitioners struggle to parse actionable insights from a motley collection of multi-modal data. (The need is particularly great given that the industry’s most costly problem — see here — is the absence of good translational models.) The concept of integrating diverse data to generate biological and clinical insights is universally celebrated, of course, but the data wrangling challenges are exceptional. Perhaps because of this, translational medicine, arguably, still hasn’t quite lived up to its potential. As a discipline, it offers particular promise to thoughtful data scientists, with profound opportunity for outsized impact. 

Bottom Line

R&D organizations recognize that emerging digital and data technologies represent important, perhaps essential, enabling tools to advance their mission. As Sanofi’s Frank Nestle explains, digital and data technologies are increasingly deployed across a range of R&D activities. So far, our reach far exceeds our grasp, but there’s been real progress, along with a high probability of more success. Our greatest challenges are navigating not the sexy analytics and data visualizations that everyone covets, but rather, the far less glamorous work of establishing the underlying data flows upon which everything depends.  This is a lesson familiar to accomplished data scientists like Recursion’s Imran Haque and Verily’s Amy Abernethy, among others – see here.

The data challenges are amplified in large biopharmas because these companies:

• operate internationally, necessitating adherence to a wide array of data restrictions;
• are the result of decades of mergers and acquisitions, further complicating data management;
• inevitably involve complex organizational politics that must be navigated, as Stanford’s Jeffrey Pfeffer has compellingly described.

Data science continues to hold extraordinary promise for biopharma; translational medicine represents a particularly compelling opportunity. Our collective challenge is figuring out how to work through the considerable data wrangling challenges and deliver palpable progress.