For today: topics relevant to many drug developers (and others):

• The initial impact of digital
• The dilemma of data collection in early clinical studies
• The elusive search for “new normal” ways of working

Initial impact of digital in biopharma

The sexy promise of digital/data/AI in biopharma was that emerging digital technologies were going to solve our most important and vexing problems. At the most breathless moments, some asked whether IBM’s Watson would cure cancer.  

AI, generally speaking, has continued to struggle in areas where the underlying data are a mess and/or where it’s difficult to aggregate adequate amounts of reasonably reliable data. One example where such insight is needed but not currently available: translational models.

As I discussed in a recent column, drug developers desperately need improved translational models, improved abilities to predict which candidate molecules are safe and effective. This would increase the success rate of large and costly Phase 2 and Phase 3 trials. While AI has proved remarkably helpful in domains such as predicting  protein folding, it’s currently difficult to imagine that in the near future, AI will be able to anticipate reliably what will happen — good and bad — when a molecule is introduced into the human body.

On the other hand – as I described in 2021 — emerging digital technologies are having an immediate impact in the “digital transformation” of how pharma functions, similar to the way other businesses have been transformed. The new direction tends to emphasize large platforms, repeatable processes, and real-time dashboards that enable teams, as well as the broader organization, to align around shared data. In short: business operations, including R&D operations.

(I’ve previously emphasized the value of dashboards as a valuable management tool for Pfizer in the development of their COVID-19 vaccine; separately, I have also discussed the downside of excessive reliance on dashboards and metrics.)

The emphasis on operations in pharma closely parallels areas of near-term progress in healthcare, where much of the initial effort, and low-hanging fruit, seem to be in what physician and venture capitalist Vineeta Agarwala calls “the workflow and administration category,” and highlights the intensive activity she’s seen in the “revenue cycle management and billing world.”  (The comments are from a recent podcast, featuring Agarwala, fellow VC Vijay Pande, and physician and digital champion Eric Topol; the episode, here, is highly recommended.)

Why operations? For one, these data tend to be plentiful, easily obtainable, and readily analyzable. For another, these data are consequential in a fashion business leaders intuitively understand, particularly when focused on parameters like efficiency.

A more interesting question is whether these efforts will ultimately enable higher-level inquiry, or will just enable drugs to fail slightly faster and cheaper. The optimistic view – which I favor — is that eventually the process of digitization will enable the thoughtful secondary analysis of large volumes of laboratory and clinical data. That sort of analysis could catalyze the development of novel and effective medicines. In the short term, though, most of the impact is likely to be on more prosaic measures of process efficiency.

One final point: a key aspect of the digital vision for biopharma is to maximize the opportunity for the organization to learn from every byte of data collected. This mirrors the much-discussed and often elusive (see here) vision for a learning healthcare system, where the goal is for each patient to benefit from the knowledge gleaned from each previous patient and aggregated for all previous institutional experience.  (Dr. Agarwala nicely discusses this aspiration in a recent McKinsey interview, here.)

Historically, pharma companies have struggled to comprehensively capture and leverage the totality of their data and experience, as both data and expertise tend to reside in hard-to-locate organizational silos.  The digitization of the enterprise offers the hope that more wisdom will be shared, allowing more insights to emerge.

Dilemma of data collection in early clinical studies

As much as drug developers try to understand a new molecule in pre-clinical studies and models, the introduction of a compound into a human being for the first time represents a profoundly important event, and contributes to a step-change increase in our understanding of the molecule.

The initial (Phase 1) clinical studies are focused on ensuring the new molecule is safe and well-tolerated; the product is given to healthy subjects (Phase 1a) and volunteer patients (Phase 1b) in carefully escalating doses in a controlled and monitored research environment.

Phase 2 studies, generally somewhat larger, are focused on starting to figure out whether the new medicine is likely to be acting the way you hoped in the body – does it seem like it might be working on treating the disease?

Broadly speaking, there are two schools of thought regarding how to approach early phase studies. Many researchers see these trials as an opportunity (some would say obligation) to learn as much about the medicine and the human body as possible. New molecules, they point out, are ultimately “perturbogens,” designed to poke certain parts of certain cells in certain ways. By analyzing in as comprehensive a way as possible how the body responds, there is the opportunity for extensive knowledge capture and insight generation. Thus, some scientists want to include in early phase studies a number of additional assays and evaluations.

The other school of thought – generally adopted by the folks responsible for the conduct and operation of clinical studies – emphasizes the value of parsimony. 

They argue clinical studies should be (and must be) as simple as possible, with the minimal number of tests required to achieve the trial’s primary objective. Reducing complexity, they point out, increases both study recruitment (a simpler study with fewer evaluations is generally more convenient and less burdensome for subjects) and study conduct, since there are fewer ways for things to go wrong. The much-lauded RECOVERY study in COVID is frequently cited as highlighting the value of parsimony in trial design. 

The truth is that it’s a very difficult balance to strike, and if (like me) you’ve been around long enough, you’ve lived through the ongoing struggles as well as the consequences. I’ve seen clinical teams lamenting the omission of data elements that they subsequently wished they had, but I’ve also seen clinical study protocols (often drafted by recent academic emigrees) that are incredibly clever and refined and complex – and then prove to be impossible to recruit and execute. 

Clearly, an elaborately designed scientific  study with zero subjects isn’t particularly useful – the phrase “succès d’estime” springs to mind. At the same time, an extremely simple study that fails to capture valuable data may represent a critical missed opportunity to advance the science and accelerate therapeutic development for the benefit of patients.

My increasing concern here is that just as we’re gaining the ability to leverage and contextualize increasing amounts of multimodal data, especially critical for translational science, drug development organizations find themselves under enormous pressure to make studies as lean as possible.  Short-term performance metrics (the vital signs intensively scrutinized by most companies) are relentlessly focused on efficiency – on how fast and cheaply a study is conducted. 

The consequences of this emphasis – the missed opportunity to capture meaningful insight – is far more difficult to measure, and is appreciated (if at all) only far later in development.

To summarize, the short-term corporate incentives to drive efficiency tend to be more palpable than the incentives to resist them in favor of more extensive data collection. Presumably, if or when the value of richer data collection – and the pragmatic utility of and necessity for translational research and data – becomes more readily apparent, the balance might change, and measures viewed as optional today might become essential in the future.

Elusive search for “new normal” ways of working

Finally, a few thoughts on the “new normal.” Despite the efforts of most corporate leaders across most sectors of the economy to return employees to the office, many are still choosing hybrid work, and are coming to the office only sporadically.

The surprise, perhaps, is just how effective groups comprised of many hybrid workers seem to be. Teams are functioning, the work is getting done, and the employees are on balance happy with the arrangement — even if management continues to grumble.

I was talking about this recently with a technology colleague, a long-time pharma industry veteran who only recently joined our company, and had actually started working remotely even before the pandemic.

My colleague emphasized – correctly, I think – how much everyone has learned about remote work during the pandemic. The combination of improved tools and extensive experience has enabled many employees to become phenomenally effective. Particularly when combined with occasional, high-value in-person team experiences, hybrid work seems like a promising alternative for many employees that seems here to stay.

Full disclosure — I remain a bit old-school here. As much as I dislike the commute and dread the Cambridge traffic each day, I still come to the office 4-5 days a week. I like running into people unexpectedly and serendipitously, and I appreciate the sense of place – however empty the place may often be.

When not driving my kids to and from school — moments I cherish — I use my time in the car to listen to podcasts and audiobooks, catch up with friends and family, and follow-up with colleagues at work. But could I use the hours I spend in bumper-to-bumper traffic more productively outside the confines of my automobile? I suspect the answer is yes.

Searching for a good listen or an interesting read? Here are my latest suggestions.

Everything is awesome!

Looking for something thoughtful and uplifting? A great place to start is this recent interview with tech VC Marc Andreessen, who discusses, persuasively, why he is still so optimistic about technology. Particularly useful: Andreessen’s ability to contextualize the evolution of technology, including the predictable critique, which as he points out was associated with even such radical advances as the teddy bear and the bicycle (celebrated at this website, “The Pessimist’s Archive”). 

Citing the classic (1966) writing of MIT professor Elting Morrison, Andreessen notes that most transformative technologies go through stages where they’re first ignored, then rationally criticized. When this fails, Morrison says, “the name calling begins.” Of course, as Andreessen acknowledges, every new technology doesn’t represent a good idea – although he argues many failed ideas are simply “too early.”

Famously, Andreessen believes in founder-CEOs (versus professional managers who might assume a CEO role). As Andreessen has presumably discovered, there’s actually a podcast that focuses exclusively on the story of founders – specifically founders who have created businesses that have stood the test of time. The podcast, appropriately called “Founders,” is the brainchild of innovation enthusiast David Senra, and the premise is remarkably simple: Senra reads biographies (often autobiographies) of founders, or essays about founders, and then discusses what he’s learned, in exquisite and engaging detail, week after week. It’s an exceptional resource, and highly recommended.

Surveying the Landscape

At their best, good management consultants leverage their breadth of industry experience to offer relevant insights to perpetually overwhelmed executives trying to keep their heads above water. Three recent contributions (all open access) from McKinsey that are worth your time include:

• The Helix Report – an assessment of the current state of biopharma, with an eye towards future challenges. The disconnect between areas of pharma focus and areas of unmet need, while not surprising, is one particularly striking finding.  Another: the remarkable growth of companies McKinsey describes as “ecosystem players” – essentially, toolmakers, like Thermo Fisher and Danaher, that provide many of the core technologies upon which pharma relies.
  

  Anne Klibanski, president and CEO, Mass General Brigham

• Interview of Dr. Anne Klibanski – a captivating discussion with the CEO of Mass General Brigham. Admittedly, I am somewhat biased – Anne, a distinguished neuroendocrinologist, was my clinical mentor during my endocrinology training at MGH, and is an extraordinary teacher, researcher, clinician, and human being.  The McKinsey interview captures these qualities, as Anne discusses her career journey, and the critical need in both medicine and science for multiple different perspectives, values she authentically lives and has consistently exemplified and championed.
• AI in biopharma research – perhaps my most ambivalent recommendation of the three. Essentially, the premise of the article is that AI needs to be incorporated into research workflows, and when biopharmas do this, “we have observed significant impact along the value chain.” Since generally biopharmas are just beginning to figure out how to leverage AI systematically in research, I suspect early reports of effective practices may be greatly exaggerated. (See also my last TR piece on the failure of AI, so far, to meaningfully impact the area of greatest need in biopharma – improved translational models.)  Nevertheless, my hunch is that the advice offered by McKinsey is directionally correct: there’s a huge opportunity for biopharmas to incorporate AI systematically in research. It will be particularly interesting to follow the progress at Genentech, where Aviv Regev, formerly of the Broad Institute, is leading early research efforts. She brings what might be called a digitally native mindset to the South San Francisco operation.

Although venture capitalist Bruce Booth left McKinsey years ago, the clarity of his annual Atlas Venture year-in-review presentations does his alma mater proud. This year’s talk – available on YouTube – is once again exemplary, stepping us through the macrotrends and market pressures, and providing insights into how Bruce and the team at Atlas see the opportunity space for future early-stage biotech investments.  One particularly striking slide presented the cash individual large pharmas have on hand; it turns out that J&J and Pfizer are each sitting on around $33B of cash, potentially available for future acquisitions.

Given the outsized contribution of externally-sourced products to pharma revenue (another remarkable slide), one wonders if this will become another example of accumulated advantage, where those who start with the most to begin with will be best positioned to accrue still more. (Also striking: a slide showing the swarming behavior of biopharmas, in this case, the more than 110 CD-19 CAR-T products in the pipeline).  

Think Different

If there’s one thing business executives and technologists can agree on, it’s that the best way to make progress is to set ambitious goals, then monitor progress, adjusting course as necessary along the way.  Right? Well, maybe not. While this approach may work for relatively straightforward goals, AI scholar Ken Stanley argues, it actually doesn’t really work for super ambitious goals, where we don’t even know what the stepping stones are. “Almost no prerequisite to any major invention was invented with that invention in mind,” Stanley asserts. In essence, he argues – based on captivating examples from AI research – that if you want to achieve something profound, you’re more likely to get there by the pursuit of the interesting; “honor interestingness,” he advises. While this approach may not lead you to a specific goal, it is more likely to lead to something profound than more direct attempts to arrive as the same ambitious destination.  Listen to Stanley make his case in this interview with Patrick O’Shaughnessy, host of “Invest Like The Best,” here.

Bedside Rounds describes itself, accurately, as a podcast “about fascinating stories in clinical medicine.  Hosted by internist Adam Rodman, a hospitalist at Boston’s Beth Israel Deaconess Medical Center, the show offers a thoughtful, humanistic, generally historical perspective on topics in medicine. A pair of recent episodes – here and here – stand out, as they focus on the way doctors have approached and attempted to systematize the collection of information from patients. Rodman is joined on these two episodes by one of medicine’s most gifted diagnosticians, UCSF’s Gurpreet Dhaliwal. Of particular relevance to healthtech readers, Rodman and Dhaliwal describe subtleties of medical data that may not be obvious to data scientists. For example, Dhaliwal describes the difference between information gathered from a questionnaire (for example, a patient checking a box next to the statement “I do not have a fever”) and information Dhaliwal elicits from a patient. Dhaliwal says this is the difference between “building truth with the patient” and “accepting it as a binary construct.” Dhaliwal clearly understands the concept of data empathy — and of empathy more generally.

Most everyone today recognizes the importance of cybersecurity, including in healthcare, as Eric Perakslis, among others, has eloquently reminded us. Even so, we’re not likely to be drawn to podcasts focused on cybersecurity – which is what makes “Click Here” so extraordinary.  Hosted by NPR veteran Dina Temple-Raston (she is also the executive producer), Click Here features captivating, well-constructed narrative-driven stories that relate to cybersecurity, but focus on people, taking us from Ukraine to digital health surveillance to violence-as-a-service (!) to cold war espionage, educating, entertaining, and gently terrifying us along the way, even as we eagerly await the next installment.

Meet Me At Camera 3

As long-time Daily Show fans may remember, when Jon Stewart had an important message to convey with brutal honestly to viewers, he’d urge them to meet him at Camera 3 (see this classic example).   For those seeking this spirit of uncomfortable candor, here are several considerations.

Among the most memorable experiences I’ve had in consulting and in biopharma have been the opportunities to observe, through a one-way mirror, a focus group, individuals who are convened to discuss (in a facilitated conversation) a particular condition or product. Frequently, their actual experiences and priorities are vastly different than what conventional wisdom might have dictated.  You can get a feel for this in “The Focus Group” podcast, featuring political strategist Sarah Longwell. The appeal of the podcast is that it provides a relatively unfiltered opportunity to hear what people actually think, including people you might not typically encounter, discussing perspectives you might not typically hear expressed, on all sides of the political spectrum. 

In the gauzy world of LinkedIn (“… humbled to be honored…”), effusive corporate PR, and giddy entrepreneurs (“crushing it!”), it can be easy to lose perspective on some of the grueling verities of our work. I can imagine no better tonic than this blog post, describing in excruciatingly painful, all-too-familiar detail the preposterous dance biopharmas do with contract research organizations (CROs) in the course of preparing to conduct clinical trials. As we watch the author’s vitality and ambition slowly extinguished by inexorable bureaucratic processes, we share the author’s sentiment that there must be a better way.

Finally, there is Stanford Professor Jeffrey Pfeffer, who has managed to write a series of utterly essential business books (including Power, Leadership BS, and his latest, 7 Rules of Power) that effectively have been banned from the corporate canon. No HR exec is going to pay thousands of dollars to bring in a distinguished speaker who will tell employees that their bosses probably got there through the skillful deployment of self-serving, often inauthentic behaviors – behaviors the same leaders now actively discourage as they seek to preserve the status quo. It’s not that Pfeffer encourages such distasteful behavior; rather, his view is that if you are, or are planning to be, part of any organization, then the data suggest these are behaviors you are likely to encounter and will need to be ready to handle. Pfeffer offers the critical business books (and now, podcast series) you need, even if – and precisely because – they are the ones you are least likely to be told about by senior management. 

Drug development remains an incredibly expensive endeavor. Much of the cost can be attributed to late-stage clinical trial failures. 

The burden is borne first and foremost by clinical trial participants who aren’t helped by the experimental medicine. It also significantly impacts the companies sponsoring these studies. Everyone would like to improve the chances that a novel medicine that advances into clinical studies ultimately will prove successful and earn approval from regulators around the world.

Yet the odds of success remain stubbornly low.

What’s the problem? (And: is AI likely to help? We’ll return to this briefly at the end.)

Let’s consider two recent perspectives on the topic.

David Grainger: remove organizational bias

We’ll start with David Grainger, a scientist, entrepreneur and VC (now Venture Advisor) at Medicxi. I’ve admired and discussed Grainger’s work for years (here, here, here). Writing in his always worthwhile “Drug Baron” blog, Grainger argues that drug developers have the data to make much smarter decisions than they actually do; the problem, he suggests, are the biases that consistently lead developers astray.

Among the examples Grainger cites:

Infrastructure bias – “if you have a team of employees, a lease on a building and so forth, it can be painful to call a halt even if the enthusiasm is draining away.”  Since “there nearly always is” some potential path forward, “further investment becomes almost inevitable.”  This is sometimes termed “inertia bias” or “status quo bias,” reflecting our shared tendency to stick with, and justify, decisions we’ve already made.

Narrative bias – the story “that companies, particularly biotechs, tend to weave around their assets to make them palatable to investors.”  Grainer notes the narrative can involve technology (e.g. DNA editing), deep expertise in a particular indication (e.g. endometriosis), or a focus on a particular biological mechanism (e.g. caspases). 

The problem with this thematic approach, Grainger says, is that there can be a reluctance to discontinue development of a lead asset (despite bad data), for fear of besmirching the broader narrative. It can also lead to a failure to consider promising programs outside the narrative, even if they might have a higher probability of success.

Commercial bias – large pharma companies, Grainger observes, “are usually phenomenally good” at sales, adding that “selling drugs, rather than discovering or developing them, is arguably the core competence of the biggest and best in our industry.” Because of commercial commitment to a finite number of areas and indications, Grainger writes, “the ‘gravitational pull’ from the commercial hub starts to dominate R&D decisions.” Thus, to meet the need for new products in a given area, an R&D team may “risk choosing to buy or progress inferior assets, in order to meet that imperative.”

The answer, according to Grainger, is to develop drugs in a strictly asset-focused way, deliberately seeking to avoid the biases he has described.  He’s pursuing this through a Medicixi-backed company called Centessa (he’s the Chief Innovation Officer). 

While Grainger is right about the existence of these biases, such as the drive to fill therapeutic area pipelines, he may underestimate the value that comes from working intensively, over time, in a given therapeutic area, and the expertise and judgement that comes from this hard-won experience. 

It’s reminiscent of the logic that leads some to suggest pharma should ditch research and early development entirely, and just focus on in-licensing promising assets for late-stage development.  The problem, of course, is that if you’re not actively trying to develop your own drugs in a particular area, you tend not to know what you don’t know, and absent this organic expertise – likely including the learning associated with previous failures — your ability to make informed, nuanced decisions can be limited.  Perhaps this might be termed “ignorance bias.”

Another challenge with Grainger’s approach is that bias-free drug development, much like a learning health system, is an entity that’s often discussed, invariably praised, yet rarely if ever seen in the wild.  Blame human nature. 

Grainger, certainly, is as aware of these caveats as anyone. So credit him for not just writing about a solution but actually trying to build and inhabit it. Let’s see how it goes.

Jack Scannell: improve translational models

Next, let’s turn to Jack Scannell, whose thoughtful, scholarly musings on drug development I’ve admired for more than a decade (see here). 

In his latest article, in Nature Reviews Drug Discovery, Scannell argues that the key success factor in drug development is “predictive validity” – how well the success of a given preclinical or translational model predicts ultimate success in clinical trials. 

Everyone recognizes predictive validity is important, Scannell acknowledges, but argues we may underestimate just how important.  A slight improvement in the predictive validity of a model  – say increasing the correlation coefficient from 0.5 to 0.6, he says – can “often have a bigger impact on the probability of selecting a clinically useful drug candidate … than a 10x or sometimes a 100x change in the number of candidates tested.”

Phrased differently: if we had the choice, we’d be smarter to invest in a slightly better translational model than in a much larger drug screen.

Scannell points to the exploitation of translational models as one reason why pharma productivity famously declined between 1950 and 2010, despite profound advances in science and technology. 

His argument is that it was precisely the ability to efficiently pursue existing translational models – he cites models of stomach acid secretion as an example – that led to an explosion of effective drugs for these conditions (e.g. H2 receptor antagonists, proton pump inhibitors), medicines that eventually went generic and raised the bar for future products . 

The development of a good translational model can transform therapy, Scannell observes. He points to “the invention of the hepatitis C virus replicons” as an example, noting this approach “made it possible to produce reliable, high-titre, viral RNA replication in cell cultures, and to screen and optimize drugs.”

Pragmatically, what are drug developers to do?  Scannell offers several suggestions, which, as he distilled in an email with me, include:

• Devote a lot more time and effort to evaluating models and to improving models.
• Focus on “model-able” diseases (as Vertex does, he says) and avoid those with bad models
• Think harder about calibrating models and assembling sets of complementary models.
• Fix financial incentives

The financial incentives comments reflect the observation, developed in the article, that “it is easier to capture financial value from novel drug structures than from novel decision tools,” essentially because “much of the value of the innovation can spill over to other firms at low cost.”  While acknowledging “this is a difficult problem to fix,” Scannell suggests perhaps pre-competitive consortia, focused on developing decision tools (translational models) in areas of shared interest could make sense.

While immediately implementable solutions don’t leap from the pages of Scannell’s manuscript, he is almost certainly right about:

(1) the problem with inadequate predictive translational models;

(2) the undervaluing of incremental improvements in such models; and

(3) the need to pay meticulous attention to the performance of these models; otherwise, it will remain challenging to deploy them thoughtfully and to detect small but meaningful enhancements.

What about AI?

Predictably, neither Grainger nor Scannell believe AI is on the threshold of offering a solution to the fundamental problem in biopharma R&D: the need to increase the odds of success in late-stage clinical development.  

For Grainger, the contribution of bias towards poor decision-making swamps any positive contribution AI might make.  In his view, we don’t need AI – we have the information in hand today to make far better decisions, if only we could disentangle the many entrenched biases.

Scannell, for his part, also doesn’t believe AI is about to revolutionize drug R&D, and cites the work of Andreas Bender (whose work I’ve frequently discussed in this column – see here, here). Bender argues that AI researchers focus on computational models that don’t reflect that actual challenge that’s bedeviling R&D researchers: predicting safety and efficacy in advance of late-phase clinical development. 

Academic researchers, Bender suggests, tend to focus on abstractions of drug development, artificial quantitative models that can be interrogated easily, and which produce publishable results. However, these mathematical models are largely removed from the actual questions and needs of real-world drug development, which tend to be closely enmeshed with the detailed context of the specific project.  (For more on Bender’s arguments, see the slides here, and the references to which he links.)

If AI is to meaningfully impact drug development, Bender contends, we’ll need not just more data overall, but also, data that are directly relevant to the specific needs and nuances of  particular drug development programs. Like Scannell, Bender proposes a role for large precompetitive consortia, though neither sound especially optimistic.

If AI isn’t likely to resolve the fundamental challenge of biopharma R&D, why is there so much excitement around (and investment in) the promise of emerging digital and data technologies? 

Let me briefly suggest several possibilities:

• As I’ve previously described, the most significant impact of digital and data on biopharma, as in many other industries, appears to be in industrial efficiency, making repeatable processes, from manufacturing to shipping, flow better. Your drug may not have a higher chance of clinical success because of digital technologies (success or failure is largely baked into the molecule by the time it enters clinical studies), but you may find this out faster, and at less cost.
• Shared visibility of your data can serve as a powerful management tool, helping to align teams around a common set of information, enabling better execution. This appears to have been a critical factor (as I’ve discussed here) in Pfizer’s rapid development of their COVID vaccine.
• Technologies can help increase patient engagement (for example, in the context of decentralized clinical trials, as Lisa Suennen (now President of Digital and Data Solutions at Canary Medical) and I discussed with Craig Lipset on our Tech Tonics podcast in 2020). Deeper knowledge of patients, coupled with the savvy application of AI, can also enable earlier identification of patients who might benefit from a medication or a relevant clinical trial. One compelling example: a collaboration between the Mayo Clinic, an AI company (Anumana, a spinout of nference), and Janssen’s data science team, led by Najat Khan, revealed that patients with pulmonary artery hypertension could be flagged through a pattern identified by AI on routine electrocardiograms.  This algorithm received a breakthrough device designation from the FDA in May.
• Improved data management can facilitate the collection and analysis of datasets that can generate future insights. Ideally, data around the predictive validity of translational models, as Scannell has proposed, might be evaluated rigorously, so that the models could be optimized iteratively, and applied thoughtfully.

Bottom Line

A critical challenge for drug developers is to improve their rate of clinical trial success. One cause for the low success rate: organizational biases that lead projects to continue far longer than they should. Another cause: inadequate (poorly predictive) translational models. In the short term at least, digital technologies are unlikely to meaningfully impact the success of clinical development, although programs are likely to be prosecuted more efficiently. Digital technologies are already contributing to drug development in other ways, including promoting alignment through dashboards; making trials more convenient for patients; and facilitating earlier disease diagnosis and patient identification. Eventually, improved data management and the application of appropriate analytics may lead to the generation of insights that truly make drug development smarter, so we can succeed more often, rather than fail more efficiently.