Fighting Cancer with External Control Arms: Friends of Cancer Research’s Jeff Allen
External Control Arms are a powerful clinical research tool, harnessing cutting-edge tech to fill gaps in clinical data and accelerate trials when patients’ lives are on the line. They’re particularly valuable for cancer research, where patients can neither afford to wait nor settle for the current standard of care.
Friends of Cancer Research President and CEO Jeff Allen sits down with Medidata CEO Anthony Costello and Senior Vice President, Statistics and Regulatory Science Innovation Ruthie Davi to celebrate 30 years of oncology innovation, and explore what’s coming next.
They dig deep into Medidata and Friends’ partnership to bring the power of External Control Arms to pancreatic cancer research, and how to build a future where clinical research takes full advantage of these life-saving innovations.
Follow Medidata: LinkedIn, Instagram, X, YouTube
Follow Anthony: LinkedIn
Follow Jeff: LinkedIn
Follow Ruthie: LinkedIn
Ruthie Davi: There's obviously been increased attention around the potential capabilities of using data in a more advanced way
than perhaps has been able in the past, and so our hope through these collaborations
was really to try and bring together various different data partners in order to demonstrate what's possible.
Anthony Costello: How are bold ideas born, and which ones survive to eventually shake up the status quo?
We'll hear straight from our industry's greatest visionaries, who are making waves,
and learn how they turned their dreams into disruptive reality. This is from Dreamers to Disruptors,
a podcast powered by Medidata. Time matters in all research, but especially in cancer research.
For patients and families waiting for new options, the question is not only whether a treatment works,
it's how quickly we can prove it, how reliably we can evaluate it,
and how soon it can reach the people who need it. Our guests today are Jeff Allen,
President and CEO of Friends of Cancer Research, and Ruthie Davi, Senior Vice President of Statistics and Regulatory Science Innovation at Medidata. Over the last three decades,
Friends has helped shape important conversations around oncology, clinical research, and regulatory policy. Jeff and his team bring researchers, industry leaders, regulators,
and patient advocates together to work through some of the hardest questions in cancer research.
In this episode, we talk about one of those hard problems: how clinical trials can keep pace
with the science they're designed to test, especially when highly effective targeted therapies emerge.
We'll also focus on one of the most challenging oncology therapeutic areas, pancreatic cancer,
and one of the most difficult aspects of oncology trial designs, the assignment of a control arm
for comparison purposes. In serious diseases with limited options, traditional trials randomization
to a test arm and a control arm can mean not every patient receives the therapy being studied.
As of today, clinicaltrials.gov shows 29 ongoing and planned pancreatic cancer trials.
Extrapolating from there, we can expect as many as 2800 patients with metastatic pancreatic cancer
to be assigned to control therapies in randomized trials in the US over the next two years.
With external controls, many of those assignments could be avoided, and if we expand our extrapolation
across other oncology indications where standard of care assignment is becoming ethically difficult,
as well as rare diseases, where standard of care is severely lacking, the impact could reach
10s of 1000s of patients over the same two year horizon. This is why synthetic and External Control Arms matter.
When high quality data can serve as the comparison, more patients may have the chance to receive a promising, possibly life changing therapy without sacrificing scientific rigor.
That is the backdrop for our podcast today, and for the recent External Control Arm validation work
in pancreatic cancer, conducted in a collaboration between Medidata and Friends of Cancer Research.
This is a conversation about cancer research, data, policy, and the responsibility we all share
to help bring better treatments to patients faster, but also in the most ethically responsible way possible.
Hello, everyone, and welcome to another exciting edition of from Dreamers to Disruptors.
I'm really excited to have two guests here today. I think this first time we've done that.
Pleased to have Jeff. Thanks so much for your collaboration on this project.
We're going to get into it, and I know it's very exciting to all of us at Medidata,
and Ruthie, I know you've lived and breathed this innovation for years now,
and I know that you've been central to Medidata's ability to make External Control Arms a reality,
and I also know you're very passionate about this, so could not be more pleased to have the two of you here today to talk about. This is a complicated topic. It's a complicated concept,
but I think we all agree there's so much space in our industry for the introduction of External Control Arms, and really the promise that they could change the way we all think about research and study designs,
and the execution of those study designs in the future. So I'm really excited to get into it,
but before we go too far, let's start with Jeff, and maybe just give us a little introduction
to you, your background, and what Friends of Cancer Research is all about.
Jeff Allen: Sure, well, thanks for having me today. My name's Jeff Allen, I'm the President and CEO
of Friends of Cancer Research. We're a DC-based advocacy and policy organization.
Our goal is really quite simple. We want to accelerate the pace of developing new medicines for the people
who need them most. We do that by funding some unique research collaborations, the ECA project being one
of our most recent ones, and also thinking about how the research that we support is able to inform future policy
in order to try and instigate that acceleration. You know, the technology is there, it's just oftentimes a matter of aligning all of the different parts.
Anthony Costello: And Friends of Cancer Research, we'll get into this a little bit more later,
but you're approaching your 30th year anniversary, right? So this is not a new organization,
you've been at this for a while.
Jeff Allen: Yeah, it's not a new organization, but you know, as technology and science continues to change,
I think so do the policy needs, and that's where we really try and focus on how we can specifically work with our partners
across the biomedical research enterprise, including policy makers and government agencies like the Food and Drug Administration,
as a key component of really thinking about how to move the needle and make things happen a little bit quicker.
You know, there's so many opportunities from a scientific standpoint, it really would be problematic if the policy is dragging those opportunities back.
Anthony Costello: Great, and Ruthie.
Ruthie Davi: First, thanks so much for having me. I've been looking forward to this conversation for quite a while.
My name is Ruthie Davi, and I'm trained as a statistician. I've spent more than 20 years at the FDA
in statistical or managerial roles there. I now lead a group at Medidata dedicated to the reuse
of historical clinical trials data and external controls, is a big part of that.
Anthony Costello: That's great. So, I think you know, before we get into the details of kind of how the collaboration worked,
let's just frame for the audience a little bit. What is an External Control Arm? Why is it important?
Ruthie Davi: They allow fewer patients to be assigned to a standard of care in a setting where the standard of care is undesirable,
so in these really difficult severe indications where there is unmet medical need. What they are is the reuse of external data
to create something that looks like a randomized control, so key aspects of that mean we have to have balance
in terms of the baseline condition of the patient and we have to have adequate measurement of the outcome of that patient,
so that we can create a comparative treatment effect, just like we would with a randomized trial.
Anthony Costello: Okay, so, so just to kind of make that very simple, we're taking historical data to create or replicate
the conditions of a control arm, so that real patients don't have to be randomized into a control arm group
and receive only the standard of care instead of the investigational therapeutic that could be beneficial for them.
Ruthie Davi: That's exactly right, and it's usually in a condition where the standard of care is changing,
and so that old standard of care has become undesirable.
Anthony Costello: A little bit later in the show, I want to try to talk a little bit about some of the details of why it's so hard to create an SCA,
but before we do that, Jeff, maybe you can kind of summarize for the audience what was the collaboration that we did between Friends and Medidata.
How did we envision that collaboration? How did it work, and then probably most importantly, what progress have we made in the industry, kind of breaking down new barriers with this collaboration.
Jeff Allen: Sure, this is the latest in a series of different collaborations that we've been able to do with a number of partners, Medidata of course
being one of them, that has helped contribute their expertise and thinking about new ways to utilize clinical data and real-world data.
There's obviously been increased attention around the potential capabilities of using data in a more advanced way than perhaps has been able in the past,
including by policymakers, regulators, and researchers generally, and so our hope through these collaborations was really to try and bring together
various different data partners in order to demonstrate what's possible, or even pressure tests to be able to best design how these approaches might be able
to be applied in clinical research more broadly, and the ECA partnership is a great example of that. Rather than just exploring whether one data set could
potentially replicate a clinical trial, we were fortunate to have a number of partners that utilize their individual data, so that we could show multiple times over
what was possible, the alignment that was able to be achieved through this type of collaboration, but then even start to identify some of the factors that would be critical in order for optimizing success, and thinking about how these, how these tools can be applied into, into research into the future.
Anthony Costello: So, when you imagine or envision a collaboration like this, the data are one piece, like I think Medidata was able to bring some expertise
and some historical data to the table. What are the other aspects? I think your Friends is in a really interesting position, being kind of at the nexus of the policy,
the study designs, the data, the partners, the collaboration. Can you just talk a little bit about what's that whole process look like, from envisioning that this might be
something that can be used to pulling the partners together, but then mechanically, how do you go through the process of developing one of these arms, we call them synthetic controls,
the industry, a lot of parts of the industry call it external control, so we're really talking about the same thing there, but can you talk a little bit about how that comes together as a collaboration, and then where do you take it next? Like, what's the regulatory piece of this?
Jeff Allen: Yeah, the data is obviously a critical component of this, but these partnerships are not simply a process of contributing data, and our organization taking that in
and calling our partners in six months when the analyses are done. You know, we really benefit from having the expertise of those partners, Ruthie and her team, all of our different partners
that were able to help contribute to developing that study design, crafting the protocol, the analysis plan, engaging external partners in that, whether they be our partners from the FDA,
drug developers, clinical researchers, in order to really think through what can be done with these types of tools. And I think this is an interesting collaboration, because in some,
in some ways, it's sort of a low-risk proof of principle. We weren't asking for a regulatory decision to be made, we weren't evaluating a new drug by constructing this external control,
so it gave us the opportunity to to really dive into the details and examine what could be done multiple times over and hopefully identify for those instances moving forward how best to construct an external control
What are the characteristics of the data that are needed in order to be most successful and provide the confidence that those results are are replicating what you would see if you were conducting a more traditional clinical trial?
You know, like Ruthie mentioned. I think in these instances it's really important to keep in mind in thinking about what's what's able to be done in applying these tools into the future.
You know, it's really about optimizing research in a way that is very evidence driven, so providing the foundational evidence to think about doing things differently is a really good thing when you're unfortunately still dealing with areas of high unmet medical need.
Anthony Costello: Yeah, and I think it's not a mistake or a coincidence that we decided in the collaboration to kind of go after this particular therapeutic area. As trials are randomized, I said in the introduction
for the podcast today that one of the difficulties with clinical research in some of these therapeutic areas is they're really ethical challenges putting patients onto a control arm to receive standard of care when obviously
their life may depend on the new therapeutic, so can we talk a little bit, you know, maybe Ruthie, to you first, can we talk a little bit about why pancreatic cancer, and maybe a little bit about kind of our experts, our expertise
historically at Medidata that allowed for an External Control Arm to be created in this case, but also why we choose to focus on indications like pancreatic cancer, where there's such an ethical challenge having a traditional control arm.
Ruthie Davi: Yeah, I think that's a really important question. We had done previous work alongside Friends of Cancer Research in non-small cell lung cancer and relapsed refractory multiple myeloma.
Both of those validation exercises were successful. They demonstrated that the external control replicated the result, but neither of those were in an indication that was so severe, and with a standard of care that might allow
this to be used in the regulatory setting. Pancreatic cancer is different. This is an… Metastatic pancreatic cancer is an indication where it's very difficult to study, and patients go to the clinical trial basically for treatment.
They do not want to be assigned to the old standard of care. And so this pancreatic cancer was chosen for this reason – let's do this in an indication where it's possible that it would be used in the regulatory setting.
As this project was ongoing, there was an important readout of a randomized controlled trial in metastatic pancreatic cancer, showing that targeted therapies have great promise. This particular targeted therapy increased
overall survival almost over double, from about six months to over 13 months. This is an incredible result. It's groundbreaking, changing in changing the standard of care. It will make it very, very hard to do randomized control trials in pancreatic cancer going forward, and so it makes this validation work even more important.
Anthony Costello: So, once we get to the point that we can show the value of an External Control Arm for an indication like pancreatic cancer, and you know, sort of doubling down on that with what you just said, that the standard of care,
or the new treatments that are, that are being proven out in the market, are going to make it harder and harder and harder to not give patients the, the experimental treatment. Where can we take this next, like, if we have the, let's call it proof of concept for an indication like pancreatic cancer. Talk about a little bit, maybe both of you, about how does that scale, and what are some of the hurdles to scaling that? What's some of the pushback that you might see in the industry? Because I'm imagining for the average listener,
even though most of our listeners are already clinical trial experts, more or less, I'm imagining this sounds like this sounds groundbreaking, and once you prove that it works in a certain disease area, why doesn't just every future trial utilize this kind of a control arm?
So you know, maybe we could share a little bit of insight about how challenging it actually is to sort of roll these things into the normal flow of clinical development.
Jeff Allen: Yeah, I think one of the things that we saw through this partnership was, you know, data sufficiency is really paramount to the success, and we had the good fortune again of working with a number of different partners that had data aggregated
from various different sources, and those data sets may have looked quite different, both in terms of the breadth and the size of the populations that were able to be isolated, in terms of patients with pancreatic cancer, as well as the depth of the data,
you know, the different factors and characteristics that were available in all of those things become important when thinking about how to attempt to replicate a a control arm from a clinical trial, and what we saw through this partnership was by deliberately
applying and selecting the various characteristics to try and mimic the population that would be enrolled in the clinical trial, how then you can draw inferences about the effect size of a new therapy that is that is being tested. And this is really critical,
and in just demonstrating where it can be applied, you know, to your point about how you know this might be able to be utilized into the future, obviously randomization is a critical component of understanding the effect of a new therapy, so we're not suggesting
that that be entirely done away with, but thinking about ways that you might even be able to augment a clinical trial to have a component of the population be brought in from other sources in order to minimize the number of patients that need to go on a perhaps relatively ineffective standard of care is just an important consideration, both in terms of making sure the patients have the greatest options available to them, but even accelerating that process of obtaining the results about a new therapy.
Anthony Costello: And Ruthie, maybe you can talk a little bit about we've been doing this work for a while, and I know you and your team have ushered some of these innovative new methods for creating External Control Arms, Synthetic Control Arms through regulatory bodies
in the past, and it takes a lot of partnership and a lot of willingness on the part of one of our sponsors, as well as your team developing the external control. Can you just talk a little bit about in other cases besides pancreatic cancer, where this has been successful?
What has the process been to sort of identify that it's possible to create an arm like this, and then what's the role of the sponsor and the regulator in collaboration with us to move one of these kind of all the way across the finish line.
Ruthie Davi: So I think to understand why it's so hard to institute the use of external controls is you have to start by understanding why randomization is so important, and it is important to give a balanced a fair starting point between the investigational arm and the control,
so that when you observe the outcomes, you know that the difference in outcome is due to the assigned therapy and not due to maybe one group was older or less sick or other feature like that, and so what you hear everyone in the industry say is that randomization balances
both known and unknown covariates with an external control. The best we can do is balance known covariates, and so the biggest criticism of external controls is that you're not balancing the things you don't know about or the things that you didn't measure,
and that leads us to then how we attempt to make our external controls as good as we can, we find the best data with the most prognostic variables measured, and we balance them with the best statistical methods out there. This is the procedure, and then we adhere to sort of standard industry
principles for pre-specification, correctional multiplicity, all of these types of things that are often used in the regulatory setting, and we follow those principles. The sort of thing that tips the scales in the negotiations with the regulator is then what we've been saying, the severe disease with an inadequate standard of care. That is the last piece, that once you've done everything else right, then you can say this is the reason we're considering it in this case.
Anthony Costello: And this is a very conversational process with regulators, right? Like we're talking about engaging with them in, in the context of a trial design, and talking very upfront about before the trial starts about how this could could be done, and looking for acceptance by the regulator,
so that there's no surprise at the end of the trial, right? Like, it's it's baked into the way the trial conduct is set up from the start.
Ruthie Davi: Absolutely, it becomes part of the study protocol. We participate with our customers in the, you know, type C or other meetings with the FDA. We help write the statistical analysis plan, describing the external control, and then help with executing those analyses,
including them in the marketing application. When it goes to the FDA, this is how things get done when you're working with the FDA by precedent. So, you do it once, and then you use that as a precedent to do it again. But we were very fortunate to have this opportunity with Friends of Cancer Research
to get an extra view into the FDA position on external controls, and this is a unique aspect of Friends of Cancer Research that allows us to see the regulator's reaction and opinions about external controls without being in the high risk setting of actually trying to have a drug approved.
Anthony Costello: So then there's some parts of this that sound like almost too good to be true, right. We put together an extra external control, we solve all sorts of ethical issues, we give everybody access on the trial to the experimental new therapy, which is what they want, but,
but in reality it's really hard to put one of these together, right? I mean, even, even if the regulators were, were willing to do this in many use cases, from a data perspective and a historical data access perspective, it's it's hard to get one of these that can provide an adequate surrogate representation of a would-be true control arm full of patients. Can you talk a little bit about why is this so hard to do, and why are we still today kind of looking at a comparatively narrow set of therapeutic areas where this is even possible?
Ruthie Davi: Yeah, it is very hard, mostly because of the issue I mentioned, is that it will be criticized for the possibility of unknown confounding, and that means things that you didn't measure about the patient or don't have access to in the external data are imbalanced with something
that you do know about the investigational arm, and that will obscure the comparison outcomes later. The way you get around this is by having really high-quality data with endpoints measured at the right time and balanced well. So, you know, even when you do everything right, that theoretical
concern of whether there could be unknown confounding remains. We can never fully exclude the possibility of some imbalance in something that was not measured. It's also difficult just to get access to high quality external data, and even in the case like Medidata, utilizing historical clinical
trials data, we still have challenges harmonizing data across trials, and you know, making that data useful for this purpose. It's, you know, it's something that we've gotten very good at over time, but we think about very carefully every time that we do it.
Anthony Costello: We always end the podcast, as, as I told you guys earlier, we always end the podcast with kind of a crystal ball thing. So, let's hold off on that for a second, but if we just keep the lens around the work that we did with Friends and the success that we've seen in other programs
at Medidata with External Control Arms and other disease areas. Can you guys just share a little bit of where you think this takes the industry over the next few years, if we're starting to get some not just acceptance for external controls, but maybe a little bit more of the mechanics around
creating them, and we can start to turn these over a little bit faster, a little bit better. What sort of impact could this have on the industry? I know we focused a lot on pancreatic cancer here, but there's lots of other therapeutic areas that you could fit into that category of either ethical
concern or places like in rare disease, where there often isn't really a standard of care that you want to put patients onto for that arm of the trial. So, where can this take the industry? How could this really make things look different over the next several years?
Jeff Allen: Yeah, I hope this opens doors into other areas of research that may have been challenging, if not infeasible, when you think about the direction that oncology is heading, where many types of cancers are in rare subsets. How to be deliberate with data collection and utilize that
as another tool in the toolbox to characterize an experimental drug, in thinking about what are the best trial designs to be able to isolate the effect of the drug, but also balancing feasibility and in a patient-centric type of approach to clinical research, and I think that this is just an important component to to be able to really think through optimizing the design of those clinical trials. Not every scenario is going to be fit for utilizing an External Control Arm, but that doesn't mean that we shouldn't try and keep an open mind and thinking about what are the best approaches for any development program and design that in the most feasible way to try and move things forward.
Anthony Costello: It really does take collaborations like this, right? I mean, Ruthie, is there what's the probability that an individual sponsor pharma company with their own historical data from their own previous trials would be able to put together a reasonable synthetic control. I mean, doesn't it really take
a bigger collaboration like this and more fruitful origin kind of source of information in order to construct something like this?
Ruthie Davi: It does take a lot of data to create an external control. There have been examples where pharmaceutical companies have done it using their own previous data. Generally, those are few and far between, because first of all, they have to have that data, and there's not a lot of opportunity, from what I can see,
for them collecting it among different pharmaceutical companies, and they're dependent on the data that they actually have, so it does require a lot of expertise and a lot of data for to create the external control, and requires I think a cross collaboration is a much better setting, like we've had here with Friends of Cancer Research,
brings validity to the approach, it allows us to do the comparison directly to the randomized control rather than in the other setting, it would very likely be the use of an external control in comparison to an investigational arm. So, this, you know, work with Friends of Cancer Research really was a unique setting and a unique platform for this to be evaluated.
Anthony Costello: Yeah, that's great. Well, again, Jeff, you know, we really appreciate your partnership with this, and maybe we can turn for a few minutes to your organization going on 30 years. Can you talk a little bit about maybe some of the most exciting efforts that you've had, or some of the most groundbreaking work
that you've done over the past history? And then maybe some things that you're really excited about moving forward.
Jeff Allen: Yeah, you know, I would say for the better part of the 30 years we have focused on ways that we can identify policy levers to try and accelerate development of new therapies. Probably the most visible of that was several years ago being able to construct the concept of the breakthrough therapy designation,
which hopefully is an indicator both from Congress and the FDA that when you see a substantial effect for a drug early on in development, that it's important to have these discussions about optimizing and potentially compressing, as appropriate, that development strategy, and we've seen since that law was implemented
that drugs that have a breakthrough therapy designation are able to compress their overall development time in a matter of multiple years. So it does make a difference in terms of addressing unmet medical needs for the patient who need these drugs. As we think about opportunities moving forward, this this is just one great
collaboration that we've had the opportunity to be part of other areas that we're thinking about, in addition to these approaches to leveraging data, are looking at things like characterizing alternative endpoints, things that can be measured earlier in clinical trials to demonstrate the effect of drug, molecular measures that
may be early indicators of response to therapy, and these are all representative of opportunities given the direction that technology is taking us, just opening doors in terms of being able to do things differently, and really just having the chance to try new things in these lower-risk scenarios through these collaborations in order to generate confidence in these new types of approaches, I think are really important in thinking about how we might be able to design trials into the future.
Anthony Costello: Fantastic. Well, I think we're kind of getting close to the end here. So, I want to go to my favorite part of the show, which is your projections for the future, and we try to make these podcast sessions about the most interesting, cutting-edge innovations that are happening in the industry, and they're also,
as the name suggests, they're also about a dream that turns into a disruption. I know the two of you have no shortage of dreams with your past experience in this industry, and you know, Ruthie, some of the things that your, that your team has been able to achieve here at Medidata were certainly dreams when, when we first started thinking
about them, but if you look into the future, and let's try to spin this as we always do into a call to action. Our industry, perhaps more than any other industry in the world, is ripe for innovation. It's ripe for disruption. It's one of the more noble places to put your effort and your career into making things faster and better because we're
addressing so much unmet medical need around the world with the work that we do. When you think about the next, let's call it five years, and you think about what can we do to really disrupt the industry, what's your hope about where we get, and can you try to frame it a little bit as a call to action for an industry that sometimes is a little bit slow to adopt new ways of doing things.
Ruthie Davi: I know you're expecting me to say widespread adoption of external controls.
Anthony Costello: No, not necessarily. Say whatever you want
Ruthie Davi: But I'm going to say something different. I want widespread data sharing. I think this is the key to success with external controls, and the key to success with a lot of research. It will advance patient interests, it will advance research interests. I think it will also advance commercial interests, in the sense that a rising tide
raises all boats. And as we share more data, learn more things, develop more assets, and get those to patients faster. I should acknowledge that there's been meaningful strides made in data sharing, but I just would really love to see those continue to grow and be highly successful.
Anthony Costello: I'm going to push on that a little bit. I love this idea, of course. I'm not pushing back on it, but let's take it one step further. So, talk a little bit about what data sharing examples we see around the industry today that, that you, you think are a model for this, and then if, if, if someone listening to this podcast
is in a position of being able to make data sharing decisions, let's say on behalf of their organization, what should they do? Like, what? What is it that needs to happen that doesn't happen regularly today to kind of create this vision of widespread data sharing that you're talking about?
Ruthie Davi: This could take some time.
Anthony Costello: That's fine. We’ve got time.
Ruthie Davi: So, I think there are a number of organizations, Project Data Sphere, others sharing, making, giving access to historical clinical trials data. I guess I should start by saying historical clinical trials data is perhaps some of the highest quality data in the world.
Anthony Costello: Because of the way it's captured and curated and cleaned for regulators.
Ruthie Davi: That's right, right, the… some other data sources with a lot of missing data or unmeasured variables. What you don't know does hurt you. Historical clinical trials data does not solve that completely, but goes a long way towards that. And so the idea that we would run a clinical trial, use that data one time to understand that particular
investigational product, it's kind of sad. And so I really commend and applaud the organizations – many of them are, you know, volunteers – making these data sets available more widely. And then also commend the pharmaceutical companies and the innovators for providing access to those data sets. And I think really what we're asking for is, let's do it more. Let's provide access to the best trials that you have, not the ones that you feel comfortable to share. Let's, you know, let's just really promote these programs and have this data become available.
Anthony Costello: And in cases that, at least the ones that I'm aware of, where there is data sharing, there's an abstraction away from a lot of this type of information, right, like we're abstracting away, certainly de-identifying patients, but we're even de-identifying sponsors and sites, and the data that are shared are our data that are a little bit scrubbed from identification and really just get down to the actual data values themselves that are relevant for the clinical trial.
Ruthie Davi: That's correct, and we do that type of thing at Medidata with the historical trial data that we're using, and we're very careful about that to adhere to those de-identification procedures that are required, but I will admit that each of these takes something away from what we can learn with that data set, then. So there are concessions made in order to make the data available, we have to find the happy middle, and hopefully we can get more towards making more information available.
Anthony Costello: So there's a call to action for our audience, more widespread data sharing, of course. Now, in this, we didn't talk a lot about AI specifically in, in this session, but with the onset of AI and sort of the ubiquitous nature of AI touching the clinical trials industry, more data plus the sort of AI factor could really start to help us accelerate our access to insights and our ability to tell create external controls, but also just see signals faster in trials by comparing those trials to historical information.
Ruthie Davi: Absolutely.
Anthony Costello: Alright. Jeff, it's your turn. You can sway the whole industry whatever direction you want over the next five years.
Jeff Allen: Yeah, you know, I would say the biggest opportunities lie in increasing investment in pre-competitive sciences, that can range a whole host of topics, but you know, similar to the idea of increasing data sharing, I think being able to identify areas where multiple different entities need to come together in order to try and solve a problem, and a willingness to do so is, is very important. Drug Development is a competitive industry, and that's a good thing. It helps keep the foot on the accelerator, so to speak. But having the opportunity to think further about investing in those areas to advance the science that can move the entirety of the biomedical research enterprise is really important.
The alternative, unfortunately, I think, would be continuing to exist in individual silos, and if each company were working through their own individual challenges with FDA, that would continue to put up what may be artificial barriers in some instances. Now, of course, there's times that proprietary information needs to be kept where it is, but I think there are certainly more opportunities to think about ways that, through various unique partnerships, we'll be able to solve some of these industry-wide challenges in order to, in order to move things along faster, and ultimately continue to increase the pace of getting where we need to go, and that's solving some of these scientific challenges and continuing to innovate for the development of new medicines and in advanced therapies, for, you know, for some of these very difficult to treat diseases that it's really going to require these different types of partnerships to get there.
Anthony Costello: And these partnerships come together, I imagine, kind 'on a volunteer basis, right? There's not, is there a forcing function, or should there be, in your view, some sort of mandate or forcing function to bring these partnerships together, or do they have to kind of organically evolve from willing parties that just decide to collaborate?
Jeff Allen: Yeah, I hope it's a willingness to invest in different types of research and development. Obviously, companies are are working to advance their, their own pipelines, but for some of these underlying challenges working across different companies, across different components of the industry, where there's where there's a unique opportunity for advancement, I think some of these challenges aren't solvable by one entity alone, and hopefully that's an area where organizations like ours are able to have the opportunity to engage with a broad set of experts to think about that pre-competitive space and how to, and how to solve some of these really vexing challenges, and be creative about it, perhaps even take some risks that would be difficult to do with own within one one's own individual pipeline. But the opportunities are there, you know, the science continues to advance. So I think really trying to find some of these, these win-win type of situations across the enterprise is really important.
Anthony Costello: This has been a fantastic session. Thanks again to both of you for being here. We've got a lot of information out in the public domain about External Control Arms, so some of the work we've done before, some of the work we're doing with Friends, and I believe you both have an upcoming webinar too that focuses on this same topic. So, we're going to continue to talk about this and try to push the industry in this direction again. Thanks so much for being here today. It's a complex topic, but I think you guys did a great job of explaining it to the audience, and I'm certainly excited about where we can take this in the future. So, thank you very much.
Ruthie Davi: Thank you.
Anthony Costello: Thank you for tuning in to today's conversation. If you've been enjoying this podcast, please subscribe to our YouTube channel, and follow us on Spotify, Apple, or wherever you get your podcasts. If you have questions for me or thoughts about the episode, drop them in the comments – I do read them. Thanks again for listening to from Dreamers to Disruptors, and we'll see you again next time.