Mike Pellini, Section 32: “I hope we will find a way where the payers and the healthcare industry will work much more closely together to generate the data that will ultimately be useful in answering difficult, cost-based decisions.”
The following is an interview I recently conducted for the PMWC Intl. team with Dr. Mike Pellini, Managing Partner of Section 32 (former CEO of Foundation Medicine), and in essence is a repost of the original interview that can be found on the PMWC website. The discussion with Dr. Pellini focused on the various aspects that need to be addressed in order for precision medicine to deliver promised benefits to the healthcare sector.
Dr. Pellini will be chairing two sessions at the upcoming PMWC 2020 Silicon Valley conference:
We at enlightenbio are always very much looking forward to attending this conference, not just because it is right here in our backyard, but because it provides an unparalleled array of talks that traverse the many elements of precision medicine – new technologies, the status quo of immunotherapy, advancements in AI and machine learning, emerging therapeutics and diagnostics, the reimbursement challenges, and trends in microbiome research and applications, just to name a few.
Following is the interview with Dr. Mike Pellini.
Genomics, digital health, big data, and artificial intelligence are some of the newest technologies/fields that are reshaping medicine and healthcare.
- How will these technologies impact healthcare and in particular individualized medicine?
- Are we there yet in terms of tangible impact, or is a good part of some of the excitement still hype? What is it that we can realistically expect, and in what timeframe?
MP: Let’s consider two different perspectives when thinking about this question.
1. One is the medical perspective: We’ve already seen a more rapid translation of science into medical products. When we couple diagnostics with better access to real time data and newer therapies, the outcome of these three legs of the stool is going to continue to be better treatment for individuals battling disease. Recently, we embarked on the next frontier. New tools are going to be utilized to help us identify molecular changes in our genome which are contributing to disease well before someone even becomes a “patient”, so we can actually do something proactively before one has a full-fledged illness. From a personal standpoint, this access to information and to therapies is going to marry the technology tools that we already have everywhere else in our lives. This patient centricity and personal health management is going to come front and center to medicine over the coming years. In parallel, we will continue to see these accelerations on the development of therapies and diagnostics, and the utilization of data in a much more efficient manner will drive these discoveries into the clinic. We will even influence the emergence of disease well before that molecular change results in something that we call a disease today.
I firmly believe this coming decade is going to be the decade of cancer screening, and even more broadly, just disease screening. By the end of the decade – or perhaps by the middle – we will see many of screening tests become a routine part of medical care.
“Patient centricity is undoubtedly a key area.”
2. The other perspective, while it is related to medicine and treatments, is about patient empowerment! One area where these technologies will directly impact healthcare is on the topic of patient centricity for decision making, data management, and access to therapies. We have access to information, access to products, and access to expertise at our fingertips in every other area of our lives today except for medicine. I believe these technologies over the next few years, through the next decade, are going to continue to drive this notion of patient centricity in healthcare in a way that we have envisioned before but have never really been able to execute. Patient centricity, or patient empowerment, is the next frontier in health care.
When thinking about these various technologies, specifically about predictive diagnostics, how much of artificial intelligence is already within reach or even being incorporated, or how much is still hype?
MP: It depends how AI is utilized. If we expect the interpretation data is going to be able to mimic human biology in its entirety in the near term, then we are reaching way too far. We are not even close to being able to appropriately mimic all of human biology so we can develop drugs in a timeline that is 90% shorter than what we are doing today.
“Whether or not something is hyped is dependent on what the promise is.”
However, if we think about using data in combination with artificial intelligence to accelerate the development of new therapies, to accelerate the development of new diagnostic tests, and to gain additional insight into human biology in a way that complements other efforts, then I think we start to see a picture of what is actually occurring today, and in that, there is no hype at all. Whether or not something is hyped is dependent on what the promise is.
- If the promise is to utilize these tools, including artificial intelligence, to accelerate and complement all the work that is going on today, we are there and building.
- If the promise, or if the expectation is a novel algorithm, complete access to data, and aggressive software development to replicate in silico much or most of what we are doing experimentally today, then I would say it is still hype.
“When we can bring together the best of the drug and diagnostic development world with the best and the brightest in the machine learning/AI world, then we truly work together as a team. That is where I believe some of the most exciting technologies and outcomes will emerge.”
I think we are going down a good path here. Even some of the brilliant scientists/computer scientists which showed an early entrance into AI on the healthcare side and which believed that there were shortcuts, have learned that we all need to work together and complement one another. Therefore, when we can bring together the best of the drug and diagnostic development world with the best and the brightest in the machine learning/AI world, then we truly work together as a team. That is where I believe some of the most exciting technologies and outcomes will emerge.
What do you see as the biggest challenges we need to overcome to successfully implement individualized/precision medicine for everyone? How will we get there and is it feasible from a cost perspective?
MP: The challenges of cost and payment are related and belong in the same bucket. While I think about cost and payment, I also think about this notion of democratization of new tools well outside of the top academic centers. If we start with the cost and payment equations, we need to understand that the burden is largely going to fall on the companies and the organizations that are developing these innovative technologies, therapeutics or diagnostics, including digital ones, to develop the data that is going to ultimately demonstrate the cost advantages.
Historically many organizations have said, “Let us focus on the science and on the clinic, and if we get it right, everything else will follow.” I am an optimist, and I still believe there is some truth to that way of thinking. But if we start to think about the cost equation and the data that is ultimately required to generate the proof that these new tools are actually going to lessen the cost burden for the payers and the overall healthcare ecosystem, then I believe we will be in a much better position to implement these tools. The beauty is that many companies are already starting to do this.
“I hope we will find a way where the payers and the healthcare industry will work much more closely together to generate the data that will ultimately be useful in answering these very difficult, cost-based decisions.”
Some of the most innovative gene therapy and diagnostic companies have taken steps at the earliest stages of discovery and development to generate the data. So, it is not an afterthought; it is actually a forethought. In addition, more and more companies are reaching out to the major payers well before they have therapies that are coming to market, and so ideally I hope we will find a way where the payers and the healthcare industry will work much more closely together to generate the data that will ultimately be useful in answering these very difficult cost-based decisions. However, payment is only one aspect.
“We have to push much harder on cost and the payment, and we have to push much harder on the true democratization side of this industry, so all people ultimately benefit, not only the ones that can afford the best insurance plans or biggest out-of-pocket payments.“
Not just top academic and medical centers should utilize new therapies, diagnostics, and devices; we do have to bring these important new approaches into top academic centers, but then we must position them to take them to the next step, so these approaches can really permeate the communities in the United States and eventually globally. That is a piece of the puzzle I believe we must consider much earlier on. It is important to get new technologies into the hands of academics. In many ways, that is where all these things should start. Start with the true experts, but let us make sure we are thinking about how these important new tools can get disseminated so that not only 5% of patient population will benefit, but 100% of the intended population can benefit from them. By that I mean, not just 100% of the wealthy population, rather 100% of the entire population whether it is the Medicare, the Medicaid, or the private payer populations. Furthermore, these populations should be all inclusive and span ethnic diverse backgrounds, as well as diverse socio-economic backgrounds. In summary, we have to push much harder on the cost and payment, but we have to push much harder on true democratization, so all people ultimately benefit, not only the ones that can afford the best insurance plans or biggest out-of-pocket payments.
How can we accelerate the development and deliver on the promises of individualized medicine? You mentioned to bring some of these tools and technologies into the hands of the top academic centers. What advice would you give young investigators, key opinion leaders, and other stakeholders to push or move the field forward? What can we do as a community? What call to action should we as a community focus on?
MP: I think about many of these tremendous challenges in a very practical way. There are some concrete steps that we can take to make sure that each company and each investigator is aware of what ultimately needs to happen in order to make sure a new healthcare tool ultimately gets democratized. This requires a broader population to join the discussion much earlier. For example, when one thinks of a medical advisory board, for the most part, one still thinks of an advisory board filled with the top key opinion leaders in the nation, or the world. I would argue that we need to think about medical advisory boards, clinical advisory boards, or scientific advisory boards – whatever we want to call them – in a different way, where those advisory boards not only need to represent the best and the brightest in the medical and scientific community; we have to make sure we bring in providers and physicians that understand community practice and the unique challenges of working in the community, such as rural, suburban, or urban areas. Bring in folks that understand the practice of medicine in these different areas, so we are not just getting representatives who understand concepts and ideas of medicine at an academic level.
Clearly, the science must drive all these decisions – which is why this industry has been focused on bringing in the best and the brightest to guide these decisions – but there is no reason that we cannot bring in community providers much earlier. There is no reason that we cannot get patient advocacy groups involved in these discussions earlier. We simply have to start thinking about this critical step of democratization earlier in the clinical development effort. Instead of taking 15 to 20 years for something to transition from an academic center into the various communities around the United States and the broader world, which I believe is the average, why not thinking about it a lot sooner to shrink that gap from 15 years to 10 years, from 10 years to 5 years, and so on.
“The call to action is to make sure that the industry and the payer community representatives are collaborating much earlier in the process.”
We are all in this industry for a reason, to impact human health. The faster that we can do it, the greater impact we have on human health. The call to action is to make sure that the industry and the payer community representatives are collaborating much earlier in the process.
Let us start these discussions early, and let us set some goals for what this data needs to look like for the payers early. We have seen this latter scenario play out often in the past – especially true on the diagnostic side – with payers engaged too late saying, “Oh, great effort, but you missed the goalpost. You have to go back and try it all over again.” There’s no reason we should be doing that in 2020. We should be communicating much earlier. We should be working together to set the goals, so if the studies achieve them, there should be very few questions left about whether or not something is A) cost effective and B) able to get reimbursed.