There is precedent for this. There are multiple kinds of synthetic controls, and the ones discussed up to this point—the reuse of data coming from the rigorous scientific and regulatory environment of a trial, albeit a different trial than the one at hand—represent what should become the gold standard moving forward. There is also data from the world of health care outside of clinical development. If we can look at, standardize, and benchmark the data from clinical trials against these other sets of data, the same progression of value should be possible. Using this information to plan studies, estimate the value of therapeutics, mitigate biases that could be introduced in prospective controls, and ultimately supplement or replace the controls necessary for a trial are all steps along the way. Companies like Flatiron Health, for example, which is using real‐world data to accelerate cancer research, are proving out this idea—scientifically and as a business model—every day.21
Making Every Trial an Adaptive Trial
There is an exciting progression beyond this idea of synthetic controls. If you think about the process for creating synthetic controls as described above, a key step is eliminating the subjects previously exposed to experimental therapies. This makes sense, since we want to compare a new drug to the on‐market standard of care. But: imagine a trial where the new drug is better than the standard of care. It should, therefore, be on a track—or at least be a possible candidate—to become the next standard of care. So there can become a virtuous cycle of clinical trial data and synthetic controls. The old experimental cohort is the new control, if the tested therapy turns out to be the new standard. We end up with a self‐perpetuating data asset that benefits both patients and the life sciences industry.
Now consider the Bayesian adaptive design, and the advantages already discussed of a trial where we aren't just testing the fitness of a particular drug for a broadly defined set of patients, but creating a learning environment where biomarkers are continually leveraged to pair the best therapy with each patient enrolled. Recall the complexity with administering a process like that, and with creating a master protocol that governs how the trial, across all therapeutics, is run.
How is this Bayesian adaptive environment different from the one we are creating with synthetic controls? My answer is that they aren't different at all. By combining these concepts, the life sciences industry can create a collaborative research environment that involves not just the reuse of controls, but also allows for the continuous learning necessary for the precise pairing of every available drug (on‐market and experimental) to every waiting patient in a close approximation—almost a perfect implementation, save the impractical, unethical idea of testing patients and therapies like temperature and pressure in a lab—to the steam table‐backed phase diagram vision of the future.
This future is one that colleagues and collaborators both inside and outside of Medidata and I hope to make a reality, ultimately creating a super‐sized virtuous cycle, as represented in Figure 11.2.
Patients in previously‐run clinical trials for what is the current standard of care become a continuously‐refreshed control arm. Standardized data across not just a handful, but tens or hundreds of potential new drugs (or combinations of therapies) can be compared by using the same data cleaning, standardization, and benchmarking techniques already used today for synthetic controls.
Instead of a competitive environment where investigators, or the patients themselves, are recruited into disparate clinical trials, the industry can work together to create a bias—in this case a positive one—where a Bayesian adaptive approach is used to enroll every patient who could benefit from an experimental therapy to the best possible one for them, based on the industry‐wide knowledge at the time.
Figure 11.2 The virtuous cycle of synthetic controls, standard of care, and new drugs
The life sciences industry's commercial architecture is based on the value generated by drugs and devices. The cost savings generated by running more efficient clinical trials, and the revenue opportunity of getting a drug to market faster, are of course hugely valuable to the companies themselves and to patients. But a truly collaborative environment where we preferentially randomize patients to the best available known therapy of any type—for them, at that point in time—creates an unprecedented number of units of evidence per patient in research programs.
A great litmus test for conviction in medicine is whether—just like Dr. David Fajgenbaum—someone is willing to take their own medicine. A collaborative environment where volunteers' biomarkers are measured, where the past experiences of patients like them is available across therapies, and where they can be randomized to an experimental therapy regardless of what company is running a trial (effectively creating an adaptive environment for every drug being tested in it) is the world in which I want to be a patient.
A Stroke of Insight
I was asked not long ago to present ideas about the future of research at a conference on stroke, run by the American Heart Association. The extent of my academic cardiology knowledge is unfortunately limited to an afternoon crash course in interpreting ECGs, so I thought it was best to admit as quickly as possible in my talk that I was thoroughly unqualified to offer any opinions on how cardiology research specifically could or would change in the future.
However, I then explained to them what I would say to a group of oncologists: If you are looking to build a mathematical model for early diagnosis in oncology—asking what biomarkers can be measured as early indicators that a patient will be diagnosed with cancer—and you are only looking at oncology research, you've made the problem much harder than necessary, if not impossible to solve.
All of the patients in the oncology data sets have already been diagnosed with cancer. On the other hand, virtually all research projects—whether academic or funded by industry—that enroll patients prospectively include a medical history, patients' vital signs, standard blood labs, an extensive list of prescription and nonprescription medication being taken, and a list of “adverse events”—severe, life‐threatening ones like cardiac arrest, as well as less severe (but not necessarily less important) ones like headaches.
If we were to look at a cardiology study instead of one in the oncology space—which might have thousands or tens of thousands of patients in it—we should be able to see which patients manifested comorbidities (like a cancer diagnosis) based on adverse events, medications taken, having to be dropped out of the study, or even death. We have a run‐up to that diagnosis or death that includes a set of medical data far more extensive, and more exquisitely curated, than we would find in any integrated health system's medical record, in our personal health records, or even combinations of the data sets that governments, academic institutions, and companies around the world are trying to create. (Not that these data sets from outside of research aren't worthy endeavors. They are.)
Figuring out how to get unexpected evidence from the data collected in a research project is the ultimate manifestation of the strategies presented here. Making cardiology research data a valuable synthetic asset for oncology studies—or the converse, seeding models for heart failure or stroke with data from oncology, diabetes, or other studies—will unlock and fuel the virtuous cycle of precision medicine research even more. We can find clues that add huge insights to our patient equations from the records of patients who just so happen to be diagnosed with new conditions while they are being closely monitored.
Adaptive trial designs offer huge potential as we go forward in a world rich with data, and where we need better ways to test hypotheses quickly and accurately. But we don't just need to rethink our approach at the front end of the clinical trial pipeline. We also need to reinvent our relationship with patients at the other end in order to get the right treatments out to the public and truly make an impact.
In the next chapter, we'll turn our attention to the patient‐facing piece of the data revolution: disease‐management platforms and around‐the‐pill apps that can motivate and chang
e behavior, measure outcomes, and match patient to treatment in ways that we haven't ever been able to do before smartphones and wearables. Not everyone will be part of a clinical trial at some point in their patient journey, but everyone has the chance to be impacted by apps and other interactive programs that bring trial results to light, and get people to care about their health and take the right actions for their future.
Notes
1. Susan Gubar, “The Need for Clinical Trial Navigators,” New York Times, June 20, 2019, https://www.nytimes.com/2019/06/20/well/live/the-need-for-clinical-trial-navigators.html.
2. Liz Szabo, “Opinion | Are We Being Misled About Precision Medicine?,” New York Times, September 11, 2018, https://www.nytimes.com/2018/09/11/opinion/cancer-genetic-testing-precision-medicine.html.
3. Meeri Kim, “The Jury Is Out,” CURE, June 19, 2018, https://www.curetoday.com/publications/cure/2018/immunotherapy-special-issue/the-jury-is-out.
4. Ibid.
5. T. J. Sharpe, interview for The Patient Equation, interview by Glen de Vries and Jeremy Blachman, July 1, 2019.
6. U.S. National Library of Medicine, home page of ClinicalTrials.Gov, 2019, https://clinicaltrials.gov.
7. Alicia Staley, interview for The Patient Equation, interview by Glen de Vries and Jeremy Blachman, July 1, 2019.
8. “PCORnet®, The National Patient‐Centered Clinical Research Network,” Patient‐Centered Outcomes Research Institute, July 30, 2014, https://www.pcori.org/research-results/pcornet%C2%AE-national-patient-centered-clinical-research-network.
9. Anthony Costello, interview for The Patient Equation, interview by Glen de Vries and Jeremy Blachman, December 2, 2019.
10. George Underwood, “The Clinical Trial of the Future,” PharmaTimes, September 23, 2016, http://www.pharmatimes.com/magazine/2016/october/the_clinical_trial_of_the_future.
11. Kara Dennis, interview for The Patient Equation, interview by Glen de Vries and Jeremy Blachman, February 24, 2017.
12. Eric Wicklund, “Gartner Analyst: Healthcare Isn't Ready for Wearables Just Yet,” mHealthIntelligence, November 19, 2015, http://mhealthintelligence.com/news/gartner-analyst-healthcare-isnt-ready-for-wearables-just-yet.
13. Don Berry, interview for The Patient Equation, interview by Glen de Vries and Jeremy Blachman, May 2, 2019.
14. Center for Biologics Evaluation and Research, “Interacting with the FDA on Complex Innovative Trial Designs for Drugs and Biological Products,” U.S. Food and Drug Administration, 2019, https://www.fda.gov/regulatory-information/search-fda-guidance-documents/interacting-fda-complex-innovative-trial-designs-drugs-and-biological-products .
15. Janet Woodcock and Lisa M. LaVange, “Master Protocols to Study Multiple Therapies, Multiple Diseases, or Both,” ed. Jeffrey M. Drazen et al., New England Journal of Medicine 377, no. 1 (July 6, 2017): 62–70, https://doi.org/10.1056/nejmra1510062.
16. “Introduction to GBM AGILE: A Unique Approach to Clinical Trials,” Trial Site News, May 3, 2019, https://www.trialsitenews.com/introduction-to-gbm-agile-a-unique-approach-to-clinical-trials/.
17. Andrew Lassman, “Smarter Brain Cancer Trial Comes to Columbia,” Columbia University Irving Medical Center, April 24, 2019, https://www.cuimc.columbia.edu/news/smarter-brain-cancer-trial-comes-columbia.
18. Ibid.
19. Stuart J. Pocock, “The Combination of Randomized and Historical Controls in Clinical Trials,” Journal of Chronic Diseases 29, no. 3 (March 1976): 175–188, https://doi.org/10.1016/0021-9681(76)90044-8.
20. Julian Jenkins, interview for The Patient Equation, interview by Glen de Vries and Jeremy Blachman, March 24, 2017.
21. “About Us,” Flatiron Health, 2019, https://flatiron.com/about-us/.
12
Disease Management Platforms
There's an app for everything, or so it seems these days. For every OneDrop, which has a carefully defined mission and proven clinical effectiveness, there are dozens of others with spurious value, whether calorie counters or fitness trackers or mood detectors or, recently found on a list of “best” health apps, Waterlogged, which reminds you throughout the day to drink more water.1 Not to say that drinking more water isn't likely a smart call for many of us, or that an app can't engage us and help make sure we do, but when these apps are marketed to literally billions of people with smartphones, even a subscription price tag of a few U.S. dollars creates an attractive enough incentive to envision a new digital snake oil economy.
Atul Gawande has criticized wearables for not being “integrated into the practice of medicine in a really critical way…demonstrating major improvements in people's outcomes,” and the same criticism can apply to most apps.2 They are standalone patches that purport to diagnose a problem or help to treat one, but they're not necessarily clinically validated, or part of a larger platform, or integrated into patients' lives such that they actually get used consistently, produce actionable information, and ultimately make a difference. Is all of this digital infrastructure, from apps to the proliferation of sensors around, attached to, and even inside us destined to be an ineffective fad? Clearly I don't think so, but acknowledging these criticisms as valid and relevant is an important step toward making the digital ecosystem an effective part of health care, and a valuable lever for us as individuals to manage our well‐being.
Kara Dennis, formerly managing director of mobile health at Medidata, says that one of the biggest challenges with apps is retention, getting patients to be bothered to consistently log in and enter data, whatever kind of data it might be. I haven't tried the aforementioned Waterlogged app, but I can certainly report my own lack of compliance after deciding to measure my coffee intake with Apple HealthKit. Even my own strong curiosity around quantifying another measure about myself wasn't enough to keep me tapping a button on my phone's home screen every time I indulged in an espresso for more than a couple of days.
This chapter is about how we overcome these issues. How do we make apps relevant, and how do we make sure we remove users' needs to manually manage devices and data? We've already covered how the data itself isn't useful without the right algorithms behind the scenes converting it into something clinically relevant—something actually connected to and affecting our biology—but how do we design the apps, wearables, and the larger platforms around them so that they will actually, in the end, matter?
The Promise of Mobile Apps
When things work, it all sounds easy. A study presented at a recent meeting of the American Society of Clinical Oncology discussed an app used by lung cancer patients that improved overall survival time by seven months compared to the average. According to FiercePharma, the app, MoovCare, collected a set of data and alerted physicians when anomalies were detected by the AI algorithm behind the scenes.3 Doctors could then follow up with their patients, address problems sooner, and, ultimately, keep them alive longer.
Another study looked at whether mobile apps are able to improve medication adherence—showing that an augmented version of a medication reminder app for patients on antiretroviral therapy was able to reduce errors, improve adherence, and in fact decrease viral load.4 Groove Health is one company combining data from a mobile app with existing health care data to better understand patients and improve adherence.5 “The complex nature of medication adherence demands solutions that are more innovative than simple medication reminders,” Groove's founder and CEO Andrew Hourani told MobiHealthNews. The app attempts to identify each patient's reason for nonadherence and to intervene appropriately by providing medication information, encouragement, dosing help, or directions to a nearby pharmacy.6 Janssen, the pharmaceutical division of Johnson & Johnson, has also released tools to help improve adherence, including smart blister packs for medication and electronic drug labels.7
These examples only begin to scratch the surface of what a digital health management platform can do. Beyond just warning doctors about suspect data anomalies or reminding patients to take their pills, we can broaden our thinking to truly integrated
digital solutions—digital therapeutics. BlueStar is an app that provides in‐the‐moment guidance for patients, telling them when to check their blood sugar, and collecting information on diet, activity, medication dosing, symptoms, lab results and more—and sending that information to their doctors.8 BlueStar has been clinically shown to lower hemoglobin A1c values by 1.7–2.0%.9 This is just one of a bunch of approaches. Pear Therapeutics has partnered with a division of Novartis to develop an app called reSET that delivers cognitive behavioral therapy for substance‐abuse disorder.10 Proteus Digital Health has developed an ingestible sensor that can be embedded into an oral medication and record if and when someone has taken their pills.11
Novartis recognizing the powerful future of digital therapies is commendable, and Proteus is a substantive platform that can objectively, quantitatively monitor and enhance compliance with sensors and digital technology. But the fact is that pharmaceutical companies and digital therapy developers are working separately, and, considering the value they create as individual companies, not as partners. Pharmaceutical companies are being left out of most of these patient platforms—and they shouldn't be.
If we accept that the ultimate litmus test for the worthiness of a digital intervention is a biological change in the user—that is, a change in behavior, cognition, or physiology—then there is not a divide between drugs, devices, and digital. Digital is certainly a newer tool for health care providers. But digital therapeutics should not be thought of any differently than a molecule in terms of whether and when to use them. The same rules—and the same measures of success through outcomes—should apply.
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