Evolving The Biopharma R&D Model — Improbable Players Are Changing The Game

By Rob Wright, Chief Editor, Life Science Leader
Follow Me On Twitter @RfwrightLSL

At a recent conference* I overheard an executive comment that the clinical trial space remains one of the biggest bottlenecks to successful drug development. Sure, companies are developing much more sophisticated and targeted therapies. However, one of the problems this more individualized drug development creates is a slowdown in clinical trial recruitment. While new technologies are creating an enormous amount of clinical evidence, for the most part this data is being divided into two silos. On one hand we have data that is general knowledge for future cases. On the other hand, clinical care is not only creating evidence for an individual patient, but also promoting well-being. How do we bring these two silos together in an industry rapidly shifting from a pay per vial/pill model to a value-based or algorithm-driven approach? How can we actually innovate in the clinical trial space? Let’s consider a few examples of how some improbable industry outsiders have been changing the biopharmaceutical drug R&D game.

Improbable Players Make The Impossible Possible

If I told you it was possible to enroll 7,000 patients in a clinical trial in just one day you would probably suggest that I have my head examined. But just one year ago, the Apple Research Kit launched, allowing Apple users to simply opt in to clinical trials via their iPhones. John Wilbanks, the chief commons officer at Sage Bionetworks, tweeted, “After six hours we have 7,406 patients enrolled in our Parkinson’s study. Largest one ever before was 1,700 people. #ReserachKit” One year later they have enrolled about 48,000 patients, of which about 10,000 were evaluable (i.e., response to a treatment can be measured as enough information has been collected). Now while you could argue that this is observational, or not interventional, the reality is it represents a new platform that has the potential to revolutionize the clinical trial space.

You might think it’s impossible to take published clinical trial data and find a predictive biomarker for response. That is, of course, until someone actually does it. While at Stanford, Atul Butte, M.D., Ph.D., who now heads up now the Computational Health Sciences Institute at UCSF, began looking at public data and turning it into meaningful insights. Butte decided to relook at a noninferiority trial done by Genentech of rituximab versus cyclophosphamide for ANCA-associated vasculitis. What he found was a Granular Index (GI) that measured the difference between the percentage of hypergranular and hypogranular granulocytes. In English, Butte could predict when cyclophosphamide was superior to rituximab in inducing remission and vice versa.

As a parent of a high school daughter, I have often told her friends when picking her up to, “Drive carefully, (insert smile) as we have big plans for Abby [our daughter] to someday find the cure for cancer.” But before we can find cancer cures, we could probably use some better diagnostics. While you might find it inconceivable that a high school student could be capable of developing a better breast cancer diagnostic, don’t tell that to Brittany Wenger. When just 17-years-old Wenger developed a computer program called “neural network” that mimics the human brain. Using “fine needle aspirates,” a minimally invasive procedure that is often one of the least precise diagnosis processes, her program correctly identifies 99 percent of malignant tumors. For her efforts she was awarded the grand prize of the 2012 Google Science Fair. Wenger’s Cloud4Cancer breast cancer diagnosis app has garnered her national recognition (e.g., Time’s 30 under 30) and even a Ted Talk. But more importantly is the impact this teenager has had on women and breast cancer. By the way, she also did it for mixed-lineage leukemia (MML).

Could you have ever imagined that gaming might play a role in the biopharmaceutical industry? Welldoc created a mobile app called BlueStar, which went head to head against Metformin, a widely used type 2 diabetes treatment. In a clinical trial BlueStar actually reduced a person’s average blood glucose level by two points as compared to Metformin. In June 2013, BlueStar became the first FDA-cleared, mobile prescription for type 2 diabetes with insurance reimbursement. If you work at a biopharmaceutical company, you need to consider that your next competitor might not be a molecule, but a digi-ceutical. For example, Akili Interactive Labs in Boston is currently tackling ADHD. Doctors currently prescribe drugs like Ritalin to manage one of the most common childhood disorders that can continue into adulthood. However, in the near future physicians might prescribe a game. Project Evo is being developed under the leadership of Eddie Martucci, chief executive officer of Akili Interactive Labs, and involves a game made for mobile devices to be used as daily therapy for brain disorders like ADHD. The developers are currently seeking approval from the FDA to make Evo the first ever video game that can be prescribed as a therapy.

Tech giants are eyeing the trillion-dollar healthcare industry and not only recognizing the challenges, but seeing the opportunity to create new platforms that are based on different principles than what biopharma is used to (e.g., patient-centric, transparency, access, convenience, prevention, etc.). For example, Jeff Bezos of Amazon and Bill Gates lead a $100 million investment in the creation of Grail, a new company developing a blood test that can detect many kinds of cancer much earlier than currently available.

How Should Biopharma Respond To The Hacking Of Its R&D Engines?

There are a bunch of opportunities to reimagine the biopharmaceutical industry R&D model. And while emerging technologies, “omics” (e.g., genomics), Big Data, wearable sensors, and computational biology are powerful tools, what makes these even more powerful are patients having access to data, as well as the ability to connect directly with industry. Already this type of increased interaction has resulted in regulatory wins (e.g., the precision medicine initiative, the 21st Century Cures Act), and new initiatives (e.g., the Cancer MoonShot 2020), which undoubtedly will result in new data sources. While the volume and velocity of data seems to be ever increasing, people continue to look for new ways to turn that data into real value (e.g., PatientsLikeMe). And though those in biopharma could view the current convergence of technology and biology in drug development as a threat, let’s instead consider three more productive responses.

1. Consider choosing to work with some improbable partners beyond just those close to the biopharmaceutical industry. For example, Under Armour and IBM Watson are collaborating in an effort to rethink how personal fitness can be managed. GSK and the McLaren Group are identifying high-tech approaches to Formula One racing by applying to GSK’s manufacturing, R&D, and nutritional research. And finally, Novartis and Google are collaborating on a “smart” contact lens.
2. Embrace transparency. While drug development can be considered the ultimate team sport, in the past that team consisted only of folks from within your company. Biopharma needs to embrace open and crowdsourcing models. For example, a few years ago Jay Bradner (Ted Talk video here) came across a molecule for an undruggable target, JQ1. But instead of patenting and reaping the profits, he published his findings and mailed samples to 40 other labs. As a result, Bradner was able to accelerate the time it took from discovery to the clinic. He has since repeated this process with other molecules.
3. Incorporate “design thinking” into clinical trials. For example, what can be learned from GE, which transformed MRI and CT scan rooms to create adventures for pediatric patients? But a fancy paint job of a machine isn’t nearly enough to change a child’s perception from being scared of the unknown. In addition to changing the appearance of the technology, GE worked to change the appearance of the patient and the provider (e.g., kids dress up like pirates, appropriate music is played in the room, technicians wear costumes). Fundamentally, GE changed the experience so patients didn’t need to be sedated. The company also got better data and images while also reducing costs. While incorporating design thinking into clinical trial sites can make them more friendly and inviting, some might argue that the use of design thinking could introduce a mediator variable (i.e., external physical events such as a comfortable environment that takes on internal psychological significance to the subject). However, there are plenty of these variables already in play (e.g., friendly/unfriendly clinicians) that may or may not impact a patient’s clinical trial experience.

Some biopharmaceutical companies are already incorporating design thinking into the clinical trial process. For example, Genentech/Roche, during its “RETHINK D” initiative, applied design thinking to recruitment. In a partnership with a small startup called Science 37, which has a combination of telemedicine and clinical research expertise, Genentech/Roche was able to create a model that brings the trial directly to the patient. One of the challenges when conducting rare disease clinical trials is slow enrollment, a consequence of few patients suffering from the disease in a geography that is convenient to a physical site. However, by decentralizing the site, Genentech was able to enroll patients much faster. The novel idea behind their approach was to find the patient first, then activate their local healthcare system (e.g., local hospital and physician) to help with participation. The old model of taking a site-based approach relies on investigators, who will have their own biases, to find patients for clinical trial inclusion.

Many industries have been disrupted by the digital revolution. Perhaps it is time for biopharmaceutical companies to start thinking like software and analytics companies and contemplate how they can digitize their R&D models before other companies do it for them. When you think about companies (e.g., Apple) that have disrupted other industries (e.g., music, telecommunications), do you think they were successful because they thought much like those in that industry, or, were they successful because they thought differently?

* This blog was developed from a presentation conducted by Komathi Stem, former strategic innovation leader at Genentech/Roche, during the 2016 Conference Forum’s R&D Leadership Summit.