PART 1
Jeff Elton:
Welcome to the ConcertAI podcast. This is Jeff Elton, CEO of ConcertAI. Today I have the great pleasure of welcoming Marisa Co and Gaelan Ritter of Bristol Myers Squibb. Both are involved in some of the most [inaudible 00:00:18] digital solution and clinical trials of any biopharma sponsors in the industry today. As we look at FDA mandates about assuring that the trial population represents the ultimate population receiving a medicine, the community context and setting is beginning to take real primacy to the next generation of new therapeutics going through the clinical development process, these sites are highly research capable, but now their research programs are growing substantially, and they too see the value of clinical trials for their own patient population. So with no further ado, let’s move to Marisa Co and Gaelan Ritter.
Marisa Co:
Thank you for inviting me. This is one of the topics [inaudible 00:01:03] I love the most and I have most passion about. A little bit of a background about myself, I’m Pharm. D. by training. I worked in this industry for about 37 years and 20 of them in the area of R&D. My career started in manufacturing, moved to regulatory and marketing, sales, corporate strategy, finance, and then I had the opportunity to manage clinical research sites as their CEO, and that’s when my eyes were wide open. I saw a lot of things that pharmaceutical companies could improve and I saw a lot of processes that were not efficient and that were inflicting a lot of pain at the sites.
And then I decided to go into the field of media and I got a degree in media psychology, and my project to graduate was how would media change your industry in 2025? Now, we were talking about 2011 when I had the project, so immediately forced me to think about the importance of digital for our industry, an industry that is still very, very manual. So I got passionate about it, and when I came back to industry with the site knowledge and the industry knowledge, that’s when I kind of brought together the idea of leveraging technology data and digital AI to help sites.
Jeff Elton:
So you’ve been on a path for a long time-
Marisa Co:
Long time.
Jeff Elton:
… to affect some real change in how this work.
Marisa Co:
Yeah, and I think we could do it.
Jeff Elton:
Yeah, wonderful. Gaelan?
Gaelan Ritter:
Sure. So my schooling was all in bioengineering, biophysics and medicine. And when I got to graduate school at Georgetown, like most grad students, they asked, “Hey, does anyone want to volunteer to help out with X, Y, and Z?” And in this case it was clinical research. And so I was working in a bioengineering lab at the time and thought, sure, clinical research makes sense. I’d worked in hospitals since I was in high school, couldn’t be that much different. And I never realized how complicated and how much of a mess it really was to run a clinical trial rather than treat patients with standard of care. And it was unfortunate because being in a research lab working on medical devices, it occurred to me that if it was that hard to do this work, it was going to be an enormous amount of time to get any sort of products in any area to market and to be able to help patients.
And so we had to do something to change the way research works to be able to enable more capabilities. And so really that was kind of the beginning of my realization that it might not be as simple and straightforward as I thought it was, and there might be an industry that needs a lot of help. And so then joined BMS nine years ago and started working in our clinical operations groups and throughout different parts of BMS and realized that it was as bad as I thought, if not worse, and we needed to really make some moves and then join Marisa’s team and we’ve been really kind of going after it, as she said, to really kind of change the way research functions and the way we kind of bring any sort of product to market in kind of the medical space.
Jeff Elton:
I think what’s really remarkable is I’m listening to both of you, is you clearly have the training to be in the leadership roles you have just in terms of the domain and scientific domains, but you’ve worked at the ground level of all of these activities. So the appreciation you have of what it’s really like to actually see inefficiencies at play here is not abstract to you in the least. To find that in both of you, that’s a pretty unusual combination.
So Marisa, as I listened to the story of your career, I want to give you an opportunity to go a little bit deeper into that. And even as you just noted, trials are complex. They’re complex anyway, much less with some of the efficiencies and they involve many workflows, many parties taking on multiple roles, sometimes years of lapse time for kind of doing that. So when you think about going from the study design to early phase trials to late stage studies and the key activities, out of all of this, what adds value and what actually detracts from the value but has been tolerated because we didn’t know a better way?
Marisa Co:
I would say what adds value, designing the right protocol has value because amendments takes time, takes time for not only the sponsor, the sites, is inefficient, sometimes you have to re-consent patients, so that’s not right for the patient either, neither it is for the site, but also engaging the sites and the patient when we write the protocols. Sometimes we write protocols that look scientifically very sound and it’s impossible to operationalize. So I would say start with the protocol design. The second one is patient safety. That’s important. The third is allowing the site to spend as much time with the patient as possible and reduce the administrative burden. I saw it with my sites, I saw it with my CRCs, and I have to say, I saw the pain of so many apps and so many systems and so many passwords and so on and so forth.
So for me, for the site to spend as much time with the patient as possible and for the sponsors to spend much more time with the sites than we’ve had in the past. So that to me is value add. It’s also value add to understand how in this complexity of clinical trials, because when we step back, cancer research is becoming more and more complex because we’re looking for different genetically predisposed type of patients. So when we used to do trials, you can have your patient recruiter go through charts and they were all paper charts. We can’t do that today. And I would say even with EMRs, the folks at the site can’t take the time to identify that patient that could benefit from our drugs. So the value add is how do we inject technology and analytics and AI so then the sites don’t have to go through the burden of looking for the patient and so on and so forth.
So that’s kind of value add. What has been tolerated? Well, the many, many systems and apps and technology that we ask the sites to use on our behalf, the famous swivel chair asking the sites to enter data twice, sometimes more, and all of that is manual and the sites do not have the resources, but they tolerate it anyways. IRT, good lord. I used to have an entire team outside of my office when IRT doesn’t work and many times it doesn’t. Asking the sites to take a PDF and enter the lab data into EDC.
So those administrative stuff like contracting, budgeting, those have been tolerated because it’s the price that you have to pay to work in clinical research. But in the past, this was tolerated because trials were less complex. So these activities could be done by the staff, but today we’re asking oncologists to be PIs, nurses to be CRCs, pharmacists to be checking the drug dispensing and so on and so forth. So to me, those have been tolerated, but I think we need to rethink our approach to that because as I said, there’s a lack of resources. And what is more important is 50% of the sites that actually do research for this first time never do clinical research again.
Jeff Elton:
Wow, okay.
Marisa Co:
So we have a call to action.
Jeff Elton:
So even in your beginning early statement, it sounded like this notion that the protocol itself and the design of the study can almost have a consultative quality with the research team in a way, if I heard you correctly, that might contribute to its executability and maybe even to some of the general interests that the site may see in the value, they may see in the potential outcomes and placing their patients on that. Can you say a little bit more about that?
Marisa Co:
Sure. I remember when we were at the site, we used to do protocol dry runs. We would get the protocol and I will sit with my CRCs and then think through what it was going to take to actually run that study. And more often than not, we identify things that could not be done or things that will take a very, very long time to get done and we will go back to the sponsor and then there will be sometimes an amendment or two or three or four. So today we can’t do that anymore. So getting that feedback from the sites early on to do the same dry run, but instead of after the fact, do it before the [inaudible 00:11:28] is finalized.
Jeff Elton:
Yeah. And do you see that even more important now with the complexity of some of these study designs?
Marisa Co:
We have to do it, but we have to do it more smartly if you wish because in the past you can comb through chart reviews and comb through EMRs and so on and so forth. Today we can’t. So not only do we have the issue of is the trial operationalizable, meaning can we go through the activities with the resources that we have? Sometimes we have the issue that we can’t find the patients because the inclusion exclusion criteria is so, so restrictive that it’s impossible to find the patient and the sponsor doesn’t know until this study gets started. So it’s important now with the advent of data and AI and so on and technology, we can take all of that away. And when Gaelan and I started the idea of that with you guys, the dream was is it possible to take all of that guessing and work away from the sites so then the sites can focus more on taking care of the patient? And same in patient’s life.
Jeff Elton:
DAC does digitally accelerated clinical trials, just to kind of lay out that. I do want a footnote, you said a couple other things that we’ll pick up later in this session, which was even that patients may exist, but it’s very complex to find them. And there may have been patients that could have been benefited, but the workload required and everything else may not have even made that an option for them. And I think that’s a super important point in addition to just the broad complexity, double data entry and just the waste of resources in something that’s a pretty precious set of people and time and for the patient to kind of go into.
Gaelan, maybe to step outside for a second, this notion of trial diversity is a term that’s being used more and came from some initiatives of the FDA, and at least as I understand it, the notion that the trial population should look more like the ultimate population that may be receiving the drug. Can you say a little bit more about why this has emerged more recently and what it might take to actually start to enable this?
Gaelan Ritter:
Sure. And I think it’s emerged over time because more and more we’ve seen that in certain instances the results of the trials don’t respond the way they would in a broader patient population, you see it especially with drugs used across country borders or other areas. I think it’s also something that has been very challenging to address up until the point of some of the digital technologies that are available. So with the access to things like EHRs and records, you can now start to understand and trace back what some of the shortcomings are with not enrolling the appropriate addressable patient population in the trials. So you’re starting to see kind of who should have been enrolled or could have been enrolled in the trials. And then to better understand, especially from the point of view of a regulator trying to strive toward then getting to that place that you can now identify.
Whereas in the past, to Marisa’s point when it was all manual chart review with paper charts, the ability to even go through and create hypotheses about what could be missing from a trial population was probably near impossible with the amount of time it takes to do chart review manually. But now with the [inaudible 00:15:01] and other things, you can start to look at what did I miss in that? In creating? In some cases it’s in reducing the heterogeneity for this study. What did I miss with the IE criteria in terms of the patients that’ll be addressable. And then also what did I miss in terms of the different characteristics of patients? I think in the past there was a lot of understanding of diversity in the sense of age, gender, and race ethnicity, which we always use in our trials but more and more we’re understanding that there are other factors involved.
Urban, rural divides, socioeconomic divides, educational divides that impact the way people receive healthcare, the way they perceive healthcare and the way they handle kind of their interactions with the medical system. And all of those things change the efficacy of a drug or change the outcome for a patient when you better understand those. And a lot of that work is predicated on what groups like HEOR and others have been doing for the last few years and identifying those differences when working with regulators and payers and being able to better understand them. And now it’s kind of filtered downstream that while we understand that there are differences now, can you please try to start testing those in the clinical trials that you’re starting? So it’s kind of the natural evolution from that kind of HEOR space.
Jeff Elton:
I think that’s a great observation. So rather than looking for the test of benefit in populations that were not part of the trial much downstream, let’s change the structure of the trial itself. And maybe if we could, historically, trials were conducted at the leading academic centers where 50% of the time people employed there oftentimes was in research and 50% of the time might have been in patient care. But as I’m listening to you now, it sounds like there’s an emphasis on moving trial activities into the communities closer to where the majority of the patients will ultimately be treated also actually kind of exist for doing that. What’s most important to actually enable that to happen? Because this is a relatively newer phenomenon, newer emphasis, and this concept sometimes, and I think Marisa brought it up of the burden being placed on it, you’re going into a setting that doesn’t have the same research capacity and staffing model, so what’s it going to take to be successful with this type of move?
Gaelan Ritter:
Yeah, it really comes down to being able to create trials and run trials that look more like standard of care, that have more of that kind of resource intensivity, more of that burden. There’s going to a lot of technology components and other things because there’s always more work to be done to run a trial. So we’re going to have to offset that extra work with technology enablement to make it runable for those sites. So often sites struggle under the weight of running clinical trials and to Marisa’s point, that’s why many of them run one and never come back. And more and more clinical trials with the pace of medical research, clinical trials are becoming an important part of the care journey, especially for certain severe diseases. You reach a certain point in the treatment pathway where clinical trials are the next therapeutic alternative.
So it can’t be something that’s only available at academic research sites because patients are going to get substandard care then across the country. So it’s important that we kind of enable the trials to be able to be run where the patients are that are needed. And to do that, it’s really going to be about, to your point, about burden, it’s really about reducing that burden, making it simple enough that sites can run it with mostly their existing staff that does run standard of care and everything else, and not having a whole bunch of extra things that come along with it that increase the time and intensivity of it. I think that becomes an exercise in user experience as much as anything else in terms of thinking about how the sites can really bring on the trials without disrupting what their day-to-day looks like, make it that therapeutic alternative that it’s supposed to be.
Jeff Elton:
So it sounds like if that trial is closer to the standard of care then for the research teams in that community setting, actually both for the patient and actually for the team that’s delivering and overseeing that trial activity, it’s no more burdensome itself than the standard of care itself, which in itself actually probably makes it more possible for them to open up more capacity for research activity.
Gaelan Ritter:
Absolutely. It can be different, but not necessarily more burdensome and I think that’s the big change that we’re going to see more and more.
Jeff Elton:
Let me ask you a nuanced question because historically, and you can correct me, but a trial design oftentimes would sort of reflect and look at past trials going into the same population as a reference. And you’re almost suggesting I might want to look at standard of care, not ignore necessarily past trials, but look at standard of care as a strong form reference about in the design. And I know you exist in a world with biostatistical inputs, et cetera into this. Is there any concerns in loss of any end points or anything that comes from the approach you’re suggesting?
Gaelan Ritter:
There is but a lot of it can be salvaged with the kind of technologies to offset those needs. And one of the things we’ve seen over the last few years is that when you design… trials have kind of taken off, as you said, they’ve been designed off of past trials and they’ve kind of taken a little bit of life of their own where they’ve diverged more and more over time from the realities of medical practice and they’ve kind of become a strange sidebar in the way medical practice occurs.
And so it is a need kind of to start bringing that back toward medical practice. And I think we do see issues with introducing heterogeneity and other kind of characteristics into the studies, which can reduce the ability to do statistical analysis, but you can offset, at least at BMS, we found ways to offset all of those work with different kind of trial designs, different capabilities and technologies on trials. You’re seeing the emergence of things like wearables and other devices to get better data and broader data collection on the trials. And so in a lot of instances you’re actually seeing companies in the medical research space realizing that their trials can actually generate more data and better data in the more modern methods than with the kind of historical trial designs that we’ve seen.
Jeff Elton:
So I’m hearing it almost as a lower burden augmentation of a web may be collected that kind of offsets and so it just becomes a different design approach, not one that’s an inferior approach, which is super important.
Gaelan Ritter:
Absolutely, different design and in some cases it’s even the superior design because of the use of the new technology because you can leverage a lot of capability of that. The historic days of the only data you get is the data the site is willing to manually reenter into your system, with the passing of those days means you open up a whole world of possibilities.
Jeff Elton:
That’s fantastic. So I want to stay on technologies here for a moment because both of you had an opportunity to see lots of experiments during the pandemic. It was no longer… sites weren’t as accessible, I couldn’t have people visiting the sites, I had to do more remotely. All sorts of terminologies such as decentralized trial technologies emerged that had been kind of slowly perturbing and incubating for a while, but hadn’t kind of come to the forefront of the scale that came through. There’s e-screening, patient matching, things that go down into the workflow of the provider. So as you kind of stand back now to two and a quarter kind of years later on, what did we learn? What seemed to work well, what worked less well? And perhaps what do we want to make sure we take forward into whatever we’re doing next?
Gaelan Ritter:
I think one of the biggest things that we learned is that it is possible to use some of these modern technologies in medical practice. There’s a lot of hesitation just with what’s new is unknown for many years, and this was kind of the jumpstart to just try things that have been kind of out there in a lot of different industries like finance and technology for a long time. I think the other part of it was that it’s not one size fits all. So patients value flexibility rather than, okay, the trial used to all be in the clinic now the trial’s all from home, that doesn’t work for the entire patient population either. So it’s about flexibility. It’s about making some of these parts of medical care more integrated parts of a patient’s life rather than being something that they have to go out of their way for all the time.
So I think we saw more and more of that and we’re seeing the technologies that kind of foster that kind of flexibility kind of rise to the top. And so that kind of adapting with the patient is working out very well. We’re seeing too many technologies just deployed and hope for the best. It needs to be less of just kind of deploy everything that you can and more thoughtful design and engineering to make sure that the user experience is positive and that it all fits together in a kind of seamless package rather than just be 40 apps that I now have to download to my smartphone, which isn’t helpful.
Marisa Co:
I think the other thing that we learn is that the regulatory authorities are amenable to the use of technologies.
Jeff Elton:
Oh, that’s important, yeah.
Marisa Co:
Because what scared us in the past, in clinical research is you can’t go through a trial and collect data differently and then have the FDA saying, “I’m not going to accept your data.” So I think what we learn is more and more the regulatory authorities are encouraging the use of other ways of collecting data as long as you can prove that you can get straight to the source, so that’s another thing we learn. And what we also learn is that if we go back to fragmentation, the sites are going to go back and push back because every sponsor went with the different company that came out of the [foreign language 00:24:53] and started a new business and they have limited offering, but we all got enamored with the little offering from different small companies. And what we’re doing is exactly what we’re trying not to do, which is overburden the sides. So I think we now learn that we need to be more thoughtful and less enamored what the shiny toys and take a look at technology as long as it enables the site to do what they’re supposed to do to be successful.
Jeff Elton:
I just spent three days with 10 different research sites in Las Vegas and they strongly echoed your sentiment actually, in terms of where they came out. And both of you are almost effectively discussing it’s a system problem, and if you don’t think about changing and shifting the system, but that system problem may need some scale solutions that become the new standard was what I’m kind of taking away from both of your comments.
Marisa Co:
Yeah, absolutely. We can’t disassociate the workflows at the sponsor and at the site. You have to think about this from an ecosystem standpoint and how do those workflows kind of fit together to get your maximum output, which is the shorter amount of time to get a study or a site to start up or a shorter amount of time to get to enrollment. And at the end of the day, all the sites want to be successful.
Jeff Elton:
Yeah, they do. Absolutely. So Marisa, I have one current state, maybe slightly looking backward question here a little bit, which is industry benchmarks and conversations that I’ve had with people around the industry would say that most trials, at least not all, but the majority of trials don’t meet enrollment expectations. So why is that so hard? And since we know they don’t meet those expectations, why don’t the expectations get set in a way such that they do get met?
Marisa Co:
That’s a low ball, but let’s break that into pieces. So it is true that most trials don’t meet timelines expectation, so about 80% of trials don’t meet timeline expectation, and that’s many reasons. One is… the protocol is one reason why we don’t meet timeline expectation because if you don’t have a [inaudible 00:27:42] constructed protocol, you may initiate a site but you’re not going to enroll if the amendment is coming. In fact, at my site we used to know the sponsors that were amendment happy and we used to joke to say, let’s not start working on this because another amendment will be coming shortly. And sure enough, that was true. So for me, the protocol is a big component of it. The second one is the time that that takes for the administrative things to get resolved. We’re spending more time in contracting, more time in budgeting, more time passing information back and forth, more time with lab manuals and the contracts with the labs and the central labs and the local labs and so on and so forth.
So all of that adds to the trial enrollment. And then you have the actual enrollment, which goes back to what we were saying. If you don’t have a mean to identify, almost like technologically pre-screen your patient and have an idea of whether you have patients or not, then chances are we’re going to pick sites that may not be successful and may not have the patience. And let’s face it, sites want to be successful because the more successful and the higher the quality, the more triumphs we’re going to give them, and therefore they’re going to subsist as or exist as a business.
So to me, the biggest issue is not, there’s no one single bullet. It’s a combination of lots of steps that have to happen for us to be successful as a sponsor to enable the sites, but also for the sites to be successful in the trial. We have always asked the sites to input the data into the EDC within 48 hours. Well, my side used to have a person that only entered data into EDC, but it was a large site. Not a lot of sites can have the luxury to have people dedicated-
Jeff Elton:
Very true.
Marisa Co:
… to entering clinical trial data into EDC.
Jeff Elton:
Absolutely.
Marisa Co:
So we need to be able to enable the sites to do things faster and to be more successful and to have fewer queries. All of that adds to the timelines and the enrollment of the patient. The same with drop off. Well, when you have a patient drop off the study, it’s a big headache for the site. It’s a big headache for the sponsor for sure. The sites don’t want to drop patients. They don’t want to lose patients.
Jeff Elton:
No, they don’t.
Marisa Co:
And if we make the protocol very cumbersome, the sites have another burden, which is how do I keep that patient coming to the site because that patient may lose interest, might be a long trial.
So there’s so many complexities that we have to think through. Is it possible to use technology to do the things that used to be done at the site with the patient coming in, but that could be potentially done remotely with their healthcare physicians taking a physical exam as in their office and adding that to a EMR so we can extract that and incorporate it into the study data. So we need to start thinking more how do we reduce the steps and all the reasons why the leaky pipeline, we continue to lose patient as we go through the trial.
Jeff Elton:
I want to give a great thanks to Marisa Co and Gaelan Ritter. Clinical trials themselves will begin to change the way we used to design trials, which was looking back at past studies or the most recent one completed in a disease category will give way to optimizing them for the context in the settings are going to be run. Primarily even focusing on community oncology research sites. That actually means designing these studies, obviously for the endpoints of interest, but to assure that they have a minimum burden on the patient and on that community-based research site, since we do have imperatives that make sure that all trials represent the ultimate population of be receiving these medicines beginning to work with an array of community providers, sites, and settings that historically have not been emphasized will become all the more important. So this too will begin to change various aspects of biopharma, large biopharma, mid-size biopharma practices.
We developed some super interesting terms about how digital solutions, particularly digital accelerated clinical trials, as the name of the program at Bristol Myers Squibb, is allow trials to be thought of differently, more than just the setting, but the nature of some of the data itself that can be collected because there’s an automation layer, we’re able to integrate into an array of different clinical data sources to even be able to have a lower burden and collect more data because of that automation. So that’s a very different way in a very different paradigm to thinking about clinical trials themselves and the promise that we can actually have much more facile identification of patients for clinical study eligibility all the way over to much more rapidly completed trials and studies allows biopharma innovators to advance more needed new medicines to patients. Thank you for tuning into this podcast to all those who have listened. Good morning, good afternoon, and good evening.
PART 2
Jeff Elton:
Hi, this is Jeff Elton and welcome to the Concert AI Podcast. With no further ado, let me move over to some of the conversation with Marisa Co and Gaelan Ritter.
I’m hearing you say studies have great diversity, even in study design. There can be myriad vectors, find five, 10 different factors that could be contributing to it. But what’s in the background almost how you started is there’s a design problem here, and this almost goes back to your digital meets workflow and different things in life sciences. So, maybe what we’ll do is we’ll use that as a bridge instead of kind of looking at current state looking backwards. We’ll actually now take a little shift and we’ll look forward at some of this.
Gaelan, I want to go back to this term digital because digital’s been used for a number of major business transformations through the years, over the course of the last decade. And BMS is now assigning this to the DACT or digitally accelerated clinical trials. And I guess part of it is maybe you can give DACT some definition here a little bit. What makes it digital in the sense of differentiated kind of workflows and things of that nature?
How is it differentiated from some of the more traditional technologically based approaches that’s there and any of these transitions, these are not for the faint of heart, if you will. They’re complex things to pull off. They sit in organizations that have perfected legacy operations and try to get there. And you’re going into sites that also, as much as they don’t like the complexity of the current state, they’ve accommodated aspects of the current state. So what gave BMS the confidence to or compelling rationale to kind of go into this
Gaelan Ritter:
In terms of DACT and digital accelerated clinical trials, it starts with the component of study design like Marisa mentioned. So, incorporating all of the digital characteristics of the data that we have access to, the analytics and some of the predictive modeling to be able to better design the studies, moving it from there into digitally integrated workflows with the site. So, less email and paper handoffs and PDFs and other things and more digital integration between the sponsor’s tools and the site’s tools and our vendor’s tools to make sure that everyone’s kind of looking at the same thing. Avoid all of that rework.
All of that in the study setup phase to make it easier for everyone to be a part of the study and pass those designs through. And then it goes right into the patient matching and the digital characteristics of being able to give sites the criteria for the study, the design for the study, and help them with algorithms to look through their medical records, do that pre-screening chart review exercise from the digital perspective before a clinical research coordinator has to get their hands on the records and actually review them. Narrow that funnel down ahead of time.
And then on the data return side, Marisa mentioned this, the swivel chair activity of manually reentering data into a system. It’s the most ironic thing in the industry. It’s actually, it’s done almost a hundred percent of the time except in some of the new DACT and EMRDC models. And the irony of it is that it seems like it’s done to improve the quality of data, or you would think it first is done to improve the quality of data on the study when actually it’s one of the biggest detractors from the quality of the data on the study. Which is the ultimate irony of it is that a manual task, which was basically entirely because there was no digital integration, has resulted in a significant part of what we call clinical trial execution today on both the site and the sponsor side.
So, eliminating some of that, making this, like you said, an integrated digital workflow for everyone really simplifies the whole pathway and it makes it a lot easier for everyone to participate, a lot faster for all of these things to move through. And then I think to the other question about what is it that makes BMS participate in it initially is honestly, it was the size of the upcoming assets that we have in the portfolio.
It is almost inconceivable that a company the size of BMS would be able to run that number of assets through the pipeline in the traditional methods. The sites wouldn’t be able to handle the volume and wouldn’t be able to handle the resource volume of the studies. They love the volume of the assets, can’t handle the resource volume of the studies. BMS, that there’s just no way to maintain that bolus of work over that period. And so to be able to support the number of molecules that we want to test in clinical trials, you have to find a different way.
And so it has to be a way that’s more efficient for us, more efficient for the sites. The easiest part of that is taking out the low value manual work that’s been going into this process for so many decades and replace it with digital integrations that allow everyone to focus on the higher value work that we all joined the medical research industry to do, but that you kind of lose in getting stuck with the moving information back and forth as kind of your activity for the day. So, it’s really a necessary change that’s going to have to be enabled to be able to support what we need to do from a research perspective.
Marisa Co:
As you can see, you have two people who are passionate about-
Jeff Elton:
Absolutely.
Marisa Co:
… clinical research and probably even more passionate about the site. And to me at least this concept came from the idea that if I could have, back in the day when I was at the site, if I could have patients identified, ready to go, I know the drug is going to be the best medicine for that particular patient at that particular time. And I know it may give that patient an opportunity that they may not otherwise have because the window of opportunity might have passed. I think that to me is the reason why BMS is investing time and resources and developing partnerships. Because at the end of the day, clinical research is about advancing science, but if we don’t have the patient at the right time at the right site, then it is hard to advance science and save that particular patient’s life.
Jeff Elton:
Yes.
Marisa Co:
When you think about from a site perspective and from the patient perspective, I will dream of a time where any patient that is confronted with not having the opportunity to have a therapy, but where a clinical trial could potentially impact their lives, I want any patient to have the ability to participate and not be constrained because, A, the physicians don’t know about the clinical trial, or because by the time we comb through the EMRs or the charts, the patient window is gone.
Jeff Elton:
I want to keep you on that thought for a moment and you’ve answered probably slightly more than half of the question I’m going to ask, but I think you can probably add a couple other dimensions to it, which is when you’re talking to a site and you’re talking to a site about why should they want to implement, deploy, and integrate DACT as an approach within their particular workflow, how do you position it to them?
Marisa Co:
As a former CEO of [inaudible 00:07:23], my biggest issue was am I going to be able to make payroll this week? That was my biggest issue and I couldn’t hire people to actually do all the work that I need. So, for me, it was enormously important to increase the number of clinical trials that I had for my site while sustaining or decreasing the resources or make them more productive.
Here is an opportunity where we are eliminating so many steps that take so much resources from the site and really do not add value, which is doing the feasibility. I would like to hear how many sites really when they get the feasibility questionnaire go to the EMR and comb through the EMR to say, “X number.” I could tell you my PI used to say, “Oh, say 10.” We wouldn’t touch a single thing. But if you could, with a technology, identify even before the study or the site is initiated, and by the time you have your protocol, you have an idea of what is the likelihood that a patient might qualify, all of the work of patient identification is gone.
Moreover, if you have technology that is used to do the contracting and the budgeting, by all means, I used to do paper-based negotiations and technology-based negotiation. I can tell you with technology-based negotiation, there will be like three clicks, maybe 20 minutes back and forth with the sponsor and we’ll be done. And the other one is the enormous amount of time that data entry takes. If you could have a workflow that takes the data from the EMR that was approved by the IRB to be extracted and dumped it into EDC or some other source and not having to touch a single thing, not only does this process save a lot of time for the site in data entry, but think about how fewer queries we would have. How much cleaner the data will be.
So, it’s a win-win for the sponsors, it’s a win-win for the site, and then we will actually be the sponsor of choice because that would’ve made the site do a lot more with the resources that we have, and we know they’re not a lot.
Jeff Elton:
I’m definitely hearing they need to focus on just the study and whether their belief is that study will bring benefit to their patients, the rest of it actually almost disappears into the background’s a little bit.
Marisa Co:
That’s right.
Jeff Elton:
And in fact, their capacity for studies will actually increase substantially.
Marisa Co:
You know what I like about DACT? Is that I think for the first time in my 37 years in this industry, instead of asking the sites to add stuff into their routine, this system, this app, this whatever, this device, we’re subtracting and we’re saying, “You know what? Don’t even think about our systems. It will be done for you.” When I close my eyes and dream, that’s what I dream about.
Jeff Elton:
It’s my favorite Marisa quote. Gaelan, I want to maybe just build on that same energy. You kind of talked about one compelling part was the business case was almost the capacity requirements of the organization given the set portfolio of emerging studies or evolving demanded thinking through new approaches. But now that you think about the results you’re seeing and you also think about further augmentation of that through other technologies that can get more integrated into this, how do you see this evolving from the sponsor’s perspective going forward?
Gaelan Ritter:
We’re going to see huge changes in the study design realm. So, it’s opening a lot of doors to new ways, new types of trial designs that we hadn’t thought of or considered possible before. We’re seeing a lot of that with seamless trials coming up and other kinds of capabilities coming out of the rare disease space and some of the cell therapy studies where really trying to better design clinical programs to test the molecules you’re trying to test rather than it being so leveraged against the operational perspectives of running the thing.
It’s the same thing, like Marisa said, with the sites. It should be about the molecule getting it to patients and collecting the data about that molecule, but the operational elephant of actually running any of these things has taken over the determining factor of how you design any of it. And so making some of that simpler has allowed us to rethink the way some of these things are designed because you don’t have to worry about, “Well, I can’t afford those resources ’cause I’ll never have the time or the effort to do that, so I can’t put that in my study.”
You can rethink the way your study is designed and maybe you don’t actually need that much input cost. And so there’s a lot of that, that goes into it to be able to leverage some of these kind of technologies and make them work for the science you’re trying to achieve. I think a big part of that goes on the data collection side. For so long we’ve been limited largely by how much data you could reasonably ask a site to collect on your behalf. You reach a certain point where the ask got preposterous and no site would be able to operationalize it for you. And because of that, you kind of over time, the trials that were more efficient for sites where the trials were, you learned less and less science from those trials.
Jeff Elton:
That’s very interesting.
Gaelan Ritter:
And now with the advent of these new technologies, you can collect 10, 20, 50 times the amount of data that you would’ve collected in a previous study design because the site didn’t have to put in all that effort to do manual work against it. And so you can start leveraging data and generate understandings and insights about these molecules that frankly were just not feasible before.
It would’ve been an army of people and their full-time job to be able to collect these things at a site and would just never been practical. It’s going to make a huge difference in terms of the types of scientific understanding you can get, the depth of that understanding, the breadth of hypotheses that you can test. It’s really going to change the way you can actually adjudicate some of this research, which is really nice.
Jeff Elton:
That’s super exciting. Let me ask you a couple of quick round robins as we kind of go through this. Do you see a time that DACT will become the main vehicle for conducting US clinical trials?
Gaelan Ritter:
Absolutely. US and globally, I think there’s definitely going to be in the next five to 10 years, we’re going to see this become the model going forward.
Jeff Elton:
Across disease categories as well?
Gaelan Ritter:
For sure. I think so. I think across phases as well, you’re going to see it pick up through, I mean, starting in phase three and four with your huge patient populations and then carrying down through two and one, you’re going to see more and more of these technologies.
Marisa Co:
That statement that Gaelan mentioned is completely true. Even only if there are partners like Concert AI that have access to the data and has the relationship with the sites where they can deploy technology and enable the site to actually do more work with fewer resources. And if we’re talking about the one [inaudible 00:15:09] sites that are still doing clinical research with no technology or basic technology, well, it’s going to be hard for them to stay in business.
Jeff Elton:
If I understand the performance of the current DACT solution, it is performing well at a near number of sites, but probably performing at a level that’s ahead of comparable legacy solution, at least in terms of patient accruals and small end, but kind of there. Do you see that actually you could create a network of whatever size you would define that actually would function as that accelerator, that because of the nature of the technologies allow faster, better, more competent, more predictable accruals that in fact you could kind of create that across different regions and set that up?
Marisa Co:
Well, I think it’s a team effort. I played volleyball and soccer for many years and I think you cannot score a goal unless you have your attackers, your midfielders and your defensive players. I think what happens here is we may have all the interest in the world, but obviously we’re not going to create networks of sites and comb through their data, obviously. So, we’re going to need partners, partners like you and other disease areas and so on and so forth.
Partners in Europe, and in Europe is going to be a very different model than in the US. And we need to partner with the regulators and the data privacy folks and the patient advocacy to really help them understand how important this approach is to give a patient a chance to live in the case of cancer, right? So, I think we need to hold hands with all the players in the life science sector.
This is not a BMS thing, by the way. We started it because we truly believe that continuing to do clinical trials the way we’ve been doing for the last hundred years, it’s not a viable alternative. It’s not a viable business. For us, it’s more about a conviction that in this day and age we have to use technology and we have to use analytics and ML and all kinds of AI to actually help find that patient and give that patient a chance. And for that we’re going to have to continue to work with the regulators, with partners, with patient advocacy groups, with privacy groups to figure out how we’re going to proceed because not proceeding shouldn’t be an option, shouldn’t be an option for the patient.
Jeff Elton:
If we had a patient sitting here at the table on the fourth side of our square, for instance, how would you express to them why they should care and what the promise is? Because I’m hearing deep patient commitment throughout all of the statements, but sometimes the language we would use to express it to them sometimes is going to have to be a little bit different.
Marisa Co:
Yeah. Well, for me it’s super personal because my dad died of lung cancer. My mom died of lung cancer, my sister died of colorectal cancer. So, if today I could have any of them living, I think I would push them to ask for that clinical trial alternative. What people don’t know is that about 56% of patients don’t know that there is a trial and don’t have access to a local trial. In community setting, that number is even higher. First and foremost, I will say to the patient, “Look for those answers, demand those answers. It’s your life. Fight for it.”
The second thing that I would say is this is a numbers game and you have to be able to contact a lot of people. I remember my sister-in-law, she had an army of people trying to look in clinical trials, [inaudible 00:19:45] trying to call sites around, physicians around and so on and so forth. And that shouldn’t be that difficult. The patient should know whether there is a trial in their community and although one could say, “Well, but it that’s posted in clinicaltrials.gov,” patients don’t have the time nor the knowledge to actually go in and find it themselves.
I think we have a responsibility as an industry to help take the extra work out of the equation because just the diagnosis itself, and I lived it with my sister, just the diagnosis itself was such a burden that it kind of blocks you from thinking about anything else.
Gaelan Ritter:
I think for me it was in working in the clinic setting for so many years, there were times when you knew that the drugs available in standard of care weren’t going to work for that patient. And there were times when we looked into the clinical trials and just for operational resource reasons couldn’t onboard them. And so you were knowingly providing substandard care than you would’ve preferred to provide. And that’s super disheartening in the medical profession to be there to treat disease and then to know that you could treat it better, but something silly like whether or not they could onboard another study or the time it would take for site activation or whatever else gets in the way of providing optimal care. That was really unfortunate.
I think to me it was really about speeding up the ability to onboard those medicines as therapies and then the ability to get those medicines in the commercial space to as many patients as possible. Speeding up that cycle time is so valuable because, to Marisa’s point earlier, people are getting missed and once that window is gone, that window can be gone forever. I think that is huge to me is we need to start using these as therapeutic alternatives because too many patients are suffering from the inability to treat them with available treatments.
Marisa Co:
Patients don’t know that there is almost like two sides of the equation. They go to an institution and there is the medical practice and then there’s the other side, which is the clinical research practice. And they have different type of qualified staff, and those two very rarely see each other. They have different workflows. If you’re a patient and you go for a treatment, you would think that that doctor or team of doctors will have access to all the information that they need to say, “Okay, these therapies might work, but there’s also this clinical trial ongoing somewhere in our institution,” and that doesn’t happen.
Those two roles rarely connect and what you guys have done so effectively is connect those two worlds so that a patient who is eligible for a clinical trial will show at the physician’s office as a potential patient to be enrolled in a clinical trial. So, it’s almost like we forced the integration of those two worlds, so the patients don’t get missed.
Jeff Elton:
Well, I almost feel that these were very passionate calls to action as well as calls to DACT in terms of what could be provided. I really want to thank you for taking the time to be here today and bring these perspectives together, both a little bit of looking forward, looking where we are and looking back and setting a context for why we absolutely have to change.
Marisa Co:
Thank you for inviting us and I hope this is the beginning of the discussion-
Jeff Elton:
Absolutely.
Marisa Co:
… and not the end of the discussion.
Jeff Elton:
Of course it is. Absolutely. Thank you.
Gaelan Ritter:
Thank you.
Marisa Co:
Thank you.
Jeff Elton:
Again, I want to thank Gaelan Ritter and Marisa Co for joining me in the Concert AI podcast room here in Cambridge, Massachusetts. The community has historically had much more modest trial participation rates, in part because of the burden and the complexity of running clinical research in settings that are largely standard of care, clinically focused. But now with the advent of AI-powered patient identification tools and semi-automated data solutions, such as the digital accelerated clinical trial solution being deployed by BMS in their collaboration and partnership with Concert AI, we can actually lower the burden on the patient and the site for actually running a wider array of clinical trial types and on the same resource research base, increase the number of clinical trials that that particular site can entertain making available to its patients.
These are super important transitions. They’re part of FDA imperatives to make sure that the trial population appears more and closer to the ultimate population receiving these drugs and they make needed medicines that are being tested more available across a wider array of settings and therefore to a greater number of patients generally. Thank you again for tuning into this podcast and we hope you’ll join us again next month. To all those have listened, good morning, good afternoon and evening.
