Leo Barella: The role of data, I refer to it as the new medicine of the next century.
Jeff Standridge (Intro): This is Jeff Standridge, and this is the Innovation Junkies Podcast. If you want to drastically improve your business, learn proven growth strategies, and generate sustained results for your organization, you’ve come to the right place. Welcome to the Innovation Junkies Podcast.
Jeff Standridge: Hey guys, welcome to another episode of the Innovation Junkies Podcast. My name’s Jeff Standridge.
Jeff Amerine: And this is Jeff Amerine. Happy New Year, Jeff.
Jeff Standridge: Hey, you too, man. Good to be here.
Jeff Amerine: We’re glad to be back for another episode. Who do we have today?
Jeff Standridge: Yeah. Yeah. Yeah. So we’ve got Leo Barella. He joined Takeda Pharmaceuticals in September 2018 as their chief technology officer. He’s focused on establishing an enterprise data program as a foundation for the development of strategic data platforms, basically to connect Takeda to the global healthcare digital fabric and supply services to the patients that Takeda serves in real-time.
He served for AstraZeneca in a chief enterprise architect and head of data enablement. He has held teaching roles in multiple universities and has spoken internationally on topics such as artificial intelligence, big data, digital innovation in healthcare, and enterprise architecture. Leo, great to have you with us today. Thank you for joining us.
Leo Barella: Yeah, thanks for having me.
Jeff Standridge: Yeah. Tell us a little bit, if you will, Leo, about the role of data and technology in pharmaceutical firms.
Leo Barella: Yeah, absolutely. The role of data, I refer to it as the new medicine of the next century. The approach that we’ve taken around medicine and healthcare has been fairly traditional.
And if we actually look back about 100 years from now to today, it’s quite archaic, the way in which we have deployed medicine, meaning the majority of the products that we actually give to our patients have been given through a trial and error type of phase.
And we’re now actually entering the generation of medicine that will target specific patients, not only based on the disease that they have but mostly based on who they are. So it’s the transformation of targeting care for the individual rather than actually using the same medicine across multiple individuals, and then basically monitoring and look at the outcome.
Jeff Standridge: So what’s been described or termed, if you will, precision medicine or personalized medicine? Am I on the right track there?
Leo Barella: That is correct. So basically, personalized medicine. And again, the term medicine is vague now in the concept of healthcare because the journey of returning an individual to a healthy state, it’s actually well beyond just the use of medicine.
So we’re now actually looking at the family history, looking at, generally speaking, the health history of the patient, the metabolism of the patient, the behavior, the environments in which they’re in, the socioeconomics of the peer group, and also the pharmacogenetics. So basically, it’s a multi-dimensional approach that we actually take for care.
Now, obviously, medicine actually has a role into care. And yes, it’s now known that each individual reacts to products in a completely different way. So yes, absolutely, specialized care genomics, and then gene therapy is going to continue to actually be an emerging style of care.
Jeff Standridge: Very good.
Jeff Amerine: As this changes, as it becomes more personalized, and more precise, and more holistic, it’s also going to require behavior changes on the part of the clinicians and the physicians. How do you see that being enacted? Because they know what they know in medical school and the gold standard for care. How do you see these modern techniques being infused into the way they work?
Leo Barella: Yeah, that’s actually really a good question. The reliance on, basically, the visit to a doctor, I think will disappear rapidly. Or it will definitely be lessened, especially with the emergence of sensors.
Now, if you’re actually looking at the COVID days, and this is actually really a time that we should not forget, this time has really introduced a quantum leap relative to how care is being delivered. People were not moving around as much, and even from our industry perspective, clinical trials had to basically be executed.
And so sensors, devices, will become a lot more targeted toward the type of therapies that the patients will need to receive. And the interaction of devices with the ecosystem of care will continue to progress.
More and more, we’re now actually getting used to actually receiving shipments of anything, quite frankly, to our home in two days. In some cases, one day. In some cases, same day. Why not care? Why not actually innovating the ecosystem of healthcare to be able to actually deliver therapies and medicines to the individual at their home?
Jeff Standridge: Let’s talk a little bit about innovation in general, as it relates to the world of pharma. For instance, situations, and you mentioned COVID a couple of moments ago, where COVID vaccines, they didn’t just emerge very, very rapidly because COVID.
Researchers and others had been working on just this whole concept of messenger RNA and how that works, and then an application presented itself with COVID. Can you talk a little bit about your perspective on innovation that’s occurring in big pharma, and how we don’t sometimes see that taking place until we have a need for it?
Leo Barella: Yeah, that’s a really good question again. If you’re actually looking at the pharma innovation, in general, a life cycle of innovation usually lasts between 9 to 15 years. We’re a very sequential type of industry, very regulated, where we go through a very diligent process of discovery, trial, phase one, phase two, phase three trials.
And then eventually, we enter the market with a new drug and that is actually really the cycle of innovation of a pharmaceutical product. We talk about biotech and the word technology. When you’re actually looking at technology, innovation, well, that cycle is really 6 to 18 months.
And so basically the acceleration that technology has given to the industry of pharma is indeed accelerating how we deliver innovation. Now, mRNA… As a matter of fact, the mRNA for Moderna has indeed introduced this new concept of innovation through the use of the messenger RNA to basically produce proteins that are capable of reacting to a virus, like COVID.
Again, openly, I am not a physician. I don’t actually hold any degrees in medicine. So any of the information that I’m sharing is purely through interaction with scientists within Takeda.
But indeed, the more we actually understand about the human body, the more technology’s advancing, the more precise we can actually become in measurement. The more new science is actually entering the market.
So basically, now we truly understand the interaction between something as simple as nutrition. So anything that you actually eat technically is a medicine because your body is actually reacting, generating new microbiomes that generate a different type of protein synthesis. Most of it is actually good for you, some of it is not.
And depending on the individual, you can actually have drastically different reactions between food and especially medicine. So the introduction of the mRNA now is thoroughly opening a completely new science in how we deliver care.
Now, still actually now using the concept of trial and error is still very generalized. Obviously, with the pandemic, we couldn’t actually react at the individual level, which is the reason why vaccines and even COVID actually has such a different reaction depending on the individual. But the science is definitely going to become a lot more specialized.
And most importantly, the ways in which we actually measure the effects of medicine will continue to actually improve. And the detection mechanism, so basically how do you actually know that you have, for instance, COVID, is going to continue to evolve and become more and more precise.
Jeff Amerine: A follow-up question because we’re talking about personalized medicine. And a lot of that gets to, how do you efficiently do diagnostics, diagnosis in a way that is accessible and doesn’t require a lab visit, doesn’t require someone to be standing in a long line, waiting for a test?
Do you see more and more of the things that have been traditional lateral flow, finger stick to be achieved in other less invasive ways through electronic…? How do you see all that going as it relates to home-based sensors or attachments to your phone or your watch for gathering this important information and know what’s going on with the patient?
Leo Barella: Yes. I’m actually living proof of improvement through sensors. I wear an Oura ring that basically gives me biometrics on my heart rate, my temperature, my sleep quality, my activity during the day. Same for an Apple watch. And I know other sensors are actually used throughout the day.
So I’m actually logging several attributes about my health and my lifestyle and including my habits. So all of that is actually available to physicians. And not many physicians today are actually able to interpret the massive amount of data that I generate on a daily basis, let alone grow that across the entire us population.
It’s going to be more and more difficult to actually be able to ingest all this data. So I feel that basically the component of innovation now is to indeed be able to summarize all this data into something more readable.
So as in, when you actually go to a doctor, something as simple as actually measuring your weight. Well, I actually have the ability to actually measure my weight in the privacy of my home. Obviously, not actually having the excess weight of clothes or shoes or whatever I’m actually wearing.
So it’s a much more accurate and precise measure throughout my day. As a matter of fact, as soon as I actually get out of bed, I can actually measure my weight rather than actually having breakfast and actually having to go to the doctor.
So examples, something as simple as that would technically revolutionize the way in which we actually measure. Diabetes is another one. Very common disease that can actually now be measured on an hourly basis.
So basically, there are sensors that you can actually attach to your arm. Not a very invasive procedure, you can actually do yourself. And now all of a sudden, you can actually get the results as to how your blood glucose is actually changing based on what you’re ingesting.
So now all of a sudden, you can actually thoroughly understand what are foods that basically can cause you to actually have swings in mood or swings in temperatures or headaches. And so basically, you now actually have a data foundation on you that can actually generate insight as to why do you actually feel a certain way.
Jeff Standridge: In a number of our clients in industries where we work, you have some of the larger players that are out there, but R and D is happening in smaller boutique firms. In my days in technology, we used to make build, buy, or partner decisions.
We have a problem, we have a solution for the problem that we’ve identified, and we’re either going to build that solution, we’re going to buy that solution, or we’re going to partner with someone or acquire someone which could be a buy/partner decision. Do you see that happening in pharmaceuticals? And I’d love to hear maybe some of your thoughts on that.
Leo Barella: Yeah, absolutely. So this is really, again, the nexus or the connection between technology innovation and pharma innovation. So we can no longer afford to actually build the entire intellectual properties within our own company. So for instance, at Takeda, we have more than 220 external partnerships for drug development.
Now, if we actually enter the space of data and information technology, and especially artificial intelligence, I believe that basically there will be an emergence of smaller boutique AI firms that will be able to actually generate models that become more and more accurate in prediction, based on the volume of data.
As you know, basically, an AI model becomes more and more accurate, the more data it can ingest. So if you actually now federate the data and you actually federate the AI studies across multiple companies, well, obviously the dataset that they can actually use is limited compared to an aggregate or the experience that the AI algorithm can gain for a very specialized purpose.
So I can definitely feel that in the future, you will actually bring your data to a very specialized firm of AI that will actually give you an answer, based on whatever type of therapy you’re trying to actually develop.
Moderna alone was actually really born as a data platform company that eventually started to develop their own medicine. But essentially, they are a platform that basically is actually producing pharmaceutical products, but it’s a data platform.
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Jeff Standridge: So how do you approach innovation in your own shop, so to speak? What do you do to make sure that your team is innovating and is pushing the envelope there?
Leo Barella: Yeah, so innovation… First of all, we perceive innovation as a team sport. And we try to actually push the innovation as much as possible to the edge, realizing also that when people are trying to actually innovate in whatever sector of our company, from research and drug development, to patient engagement, to manufacturing.
Manufacturing now is actually going through a tremendous amount of innovation, especially because of the fact that we rely on so many external partners. So what we do believe is the fact that obviously, people across the organization have their own talents, their own experience, their own education, their own intuition.
But they might actually lack the ability to basically put on paper what they’re trying to actually change and how they’re trying to actually change it. So we have, basically, developed the capability of innovation that can actually be deployed across any one of these edges across the company, and actually help people develop journey maps, leveraging design thinking principles, leveraging first principles type of approach.
So really teach people how to actually innovate in an efficient way. Anyone is actually an expert that is coming up with a good idea, but how do you actually land the idea? So we then actually have deployed across our major site’s capabilities to basically execute on what we actually call fast prototyping.
What that means is that once the team is actually now generating an innovative idea and we now actually have landed more or less on what the prototype of that idea could be, we try to actually get, within 24-hour, a prototype that can actually give a tactile experience as to what they’re trying to actually develop. Based on what they were actually sharing with us.
And then eventually, that gives it the ability to decide if we actually want to move forward or not. So basically, this fail fast, fail forward type of principle where we decide then if we actually want to move forward and actually develop an MVP of the specific product being a sensor, being software, being actually something that has nothing to do with digital technologies.
But that is basically how we deploy innovation. So it’s a center of excellence that basically is helping people innovate better, so give them the assistance and skills to land on a more tangible product faster.
Jeff Amerine: How do you make sure that that becomes not just the domain of a particular innovative group, but it sounds like you’re really trying to spread a culture of innovation? How do you get it to stick in a large organization?
Leo Barella: Yeah. We try basically the centralized approach and obviously, it does not necessarily work because of the fact that you run into skill problems. So we really try to augment the skills that are present out there, no matter the discipline of execution of a process in the company.
But it’s really providing people help now. How do we actually help you to stick? I think that we do feel that the organization overall is adapting to the concept of basically asking for help and actually really leveraging the skills of acceleration, of innovation, even though it’s actually… Yes, it is actually provided centrally. But that’s really offering the economy of scale to basically exponentially produce more and more, again, prototypes and then eventually having the faster time to deliver a physical product.
The other thing that we found also is that using this model, it’s much easier for people to actually develop a classic, say, business case to basically request for funding. And the education that we’ve given across the organization on Agile methodology, where basically Agile is not just something that you actually do within the IT organization, but it is indeed a process of innovation. It’s something that can actually help you think about, how do I actually make incremental improvements and actually look if these incremental improvements are actually delivering value.
Rather than actually having the classic Waterfall, it’s going to take you two years to know if you’re actually going to succeed or fail. So I feel that the education is actually what has given us the edge, relative to others.
Jeff Standridge: Fantastic. We do quite a bit of innovation work in healthcare. And one of the things that I’ve observed, and I just want to throw this out here and maybe let you respond… And Jeff, certainly elaborate if you want to.
But many times, healthcare, pharmaceutical biotech organizations have a lot of academically oriented Ph.D. research, basic research type folks, which you got to have. And I love that.
But they’ve never really been introduced to the tenets of commercialization or looking at your research from the beginning as if you’re honing that research to eventually commercialize its outputs.
And so helping them make that transition from academic researcher to academic researcher who intends to commercialize, that can sometimes be a leap or can be a challenge. Have you seen that? And if so, how have you dealt with that?
Leo Barella: Yeah, absolutely. This is really the classic difference between the innovation, what I call the innovation ecology between pharma and digital. If you’re actually looking at digital in general, digital innovation…
So if you actually pick any company like whatever, Apple for the iPhone per se and/or any technology, the innovation usually… And when you’re actually generating a product, you make it as an open system. There is usually shared learning that is actually going on for the specific technology.
It’s very customer-focused as a design so that you actually have a specific target customer that you’re trying to actually develop the technology for. And the customer usually is global, it’s massive.
And then you actually have multiple target markets. You try to actually develop products for teenagers or older populations, but you basically are starting to really try to actually develop the product in many of these ways.
If we’re actually looking at pharma, we’re mostly focused on closed systems, very specialized, well regulated. There are basically a lot of rational designs that we actually do. There is usually a binary outcome, either we actually put the product to market, or we just toss it.
And so if you’re actually looking at the ecology, the difference between the two, it’s really where I feel that the join of the two is now actually really introducing a lot of innovation into pharma.
So basically, the fact that we’re now actually really patient-focused, the fact that the patient actually comes first, that’s not… Again, it’s not just something that you actually put on a slide.
Truly, we’re trying to really understand the patient because again, a lot of different medicines have been generated across the history of pharma. But yet, we don’t know exactly what patient could benefit from all the medicines that have been developed.
So with the use of technology and data now, we can truly align the patient to the medicine a lot faster than in the past. We don’t necessarily need to actually rely on the knowledge, the experience, and intuition of a doctor in isolation to be able to actually prescribe medicine.
So I feel that basically in the next 10 years as doctors, and as they will actually be equipped with more AI, with more information, with better education about both medicine and disease…
At Takeda, we actually produce a lot… Most of actually our products are actually targeting a rare disease. Rare disease is rare just because of… Not because of the fact that it’s not out there, it’s because of the fact that most of the doctors are unable to basically prescribe the right medicine because it’s so rare that they don’t necessarily know that you actually have that type of disease.
The user technology, with better sensors, with better use of technology, now we can actually target that specific individual with the right medicine, being Takeda’s or being anybody else’s product. But the interaction between the patient and the product is going react a lot quicker than in the past.
Jeff Amerine: Now, one other follow-up, and I’d… To the extent you’re willing to comment on this, I’d be interested in your outlook is, I think we saw during the pandemic that the regulators were able to move much more quickly in approving the vaccines, even for just emergency use than anyone thought would be practical or possible.
And so it begs the question, why couldn’t that become the norm? And what are the risks associated with that? Obviously, patient safety is very important, but how do you see all of that sorting out from the regulatory side going forward?
Leo Barella: I honestly think that the ecosystem… Well, first of all, again, let’s never forget the time of COVID because I do believe that that has generated a lot of great transformations across the industry. The FDA, Center of Disease Control, they also have moved in ways in which they’ve never moved before.
We’re now actually becoming a lot more reliant on data, and we will continue to actually become more reliant on data because of the massive amount of volume that is actually being generated.
And I do believe that the interaction with the FDA, Center of Disease Control, and global health organizations has drastically improved in truly trusting the science. Again, not to actually become too political here. But I do believe that with COVID, the fact that basically medicine almost took a second spot to the politics and into the political system might have really, in a way, generated a benefit, and maybe not, to our industry.
Because of the fact that obviously the science now has been accelerated, based on the level of investment that was actually made for this specific outbreak. Now COVID, if you’re actually looking at warning signs, we basically had H1N1, we had SARS, and now we actually have SARS-2, and then we actually have COVID.
So there were plenty of information. And then due to the media, probably now we actually have gotten a lot more attention and measurement. So we never actually really measured H1N1 nor SARS at the level in which we’re actually now measuring COVID. So I feel that basically the exponential growth of COVID definitely has triggered the need for a completely different regulation.
Now talking about devices and the FDA, I do believe that now with the ability of actually deploying clinical trials in people’s homes. I do believe also that when you’re starting to actually measure someone in their home, the information that you’re actually distilling from these sensors and devices is a lot more accurate than when you’re actually asking someone to actually enter a hospital or enter a doctor’s office because your biology changes completely.
And so I do feel that the acceleration of clinical trials and the fact that we’re going to be able to generate a lot more data through sensors will also expedite the process of clinical trials and the introduction of new medicine in the market a lot quicker than between 9 to 15 years. And the FDA, I believe that it’s actually moving in the right direction, that they’re really understanding that we’re in a completely different world right now.
Jeff Standridge: Very good.
Jeff Amerine: The reason to be optimistic, that innovation and the adoption of data by the regulators is hopefully going to match or be close to matching industry so that these things can safely be rolled out on shorter intervals. That would be great if that works out.
Leo Barella: Absolutely. Believe it or not, I believe that basically, this digital economy is going to mandate that. I don’t believe that the individuals will take ownership of their healthcare in a major way, based on the fact that basically sensors and software is actually going to give them answers.
Now, again, do you trust the software? Do you or do you not? Do you actually trust a sensor that basically has been installed on, say, several millions of people to actually…? And be able to actually accurately predict… For instance, the Apple watch can actually not necessarily predict if you’re actually going to have a heart attack, but it can actually really detect some heart conditions. It can actually give you signals as to should you actually go to a doctor or not?
So it would never actually be prescriptive, probably. Not just in my generation, but eventually it will become so because the amount of data is going to be far more accurate than any prediction that any human can actually make because we’re limited by our memory.
We have about a terabyte of storage in our head. We clock at about eight megahertz, I think, as a CPU. So we’re nowhere near the ability of the level of interactions and data integration that any AI model would be able to do.
Now not to say that, basically, that is the only thing you can trust. But once you actually start to reach a level of predictability that is better, far better than a human, then eventually we’re going to start to actually re-adapt.
And I feel that, again, the proliferation of sensors will definitely introduce a completely different way to actually really handle your wellness. So wellness will actually become far, far, far, far more important in people’s lives than eventually going through some sort of therapy or surgery.
Jeff Standridge: Very good. Leo, it’s been a pleasure having you with us today. We appreciate you for taking the time to join us.
Leo Barella: Thank you. Thank you for having me.
Jeff Amerine: Yeah. Very interesting stuff. Keep doing what you’re doing. You’re fighting a good fight there, and we appreciate it a great deal.
Jeff Standridge: That’s right.
Leo Barella: Thank you so much.
Jeff Standridge: Yes, sir. If some of our listeners wanted to connect with you, where could they find you best?
Leo Barella: Well, either LinkedIn or firstname.lastname@example.org.
Jeff Standridge: Very good. Thank you so much again. We appreciate it. We hope you have a great 2022.
Leo Barella: Thanks so much. Have a good one.
Jeff Standridge: Take care.
Jeff Amerine: Hey folks, this is Jeff Amerine. We want to thank you for tuning in. We sincerely appreciate your time. If you’re enjoying the Innovation Junkies Podcast., please do us a huge favor. Click the subscribe button right now, and please leave us a review. It would mean the world to both of us. And don’t forget to share us on social media.