Trials with Maya Z

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Hello everyone. And welcome again to Trials with Maya Z. I'm super happy today because first, we have lovely weather, and second because I recently was introduced to Kimberly Tableman that we have today. And, I just learned what a superwoman she is juggling between kids and business and all the things she did in the past, and I'm very excited that she's here today to tell us more about the ways she sees we can improve clinical trials, the struggles that we're going in clinical trials, and just generally to understand more about herself.

Kim, giving the word to you, can you just give us a little bit more intro, like who are you and what brought you to clinical research?

Yes, thank you. I spent 25 years in technology. So my undergraduate degree was in management information systems. And the early part of my career was in large-scale IT system implementations across industries, but still primarily in healthcare. I did do some in telecommunications, but then I quickly moved into doing work with companies like Cigna, the state of Hawaii, and the state of Arizona as they were implementing a claims engine. So in both instances, dealing more on the claims payer side of things. And then as my career evolved and I also, you mentioned, I decided to become a mother, so traveling 100% of the time as a management consultant, you know, wasn't going to work for me.

And I lived in Connecticut at the time. And one of our big employers in Southeastern Connecticut is Pfizer. And so I spent a large number of years at Pfizer, almost 10 years. And then I went to Glasgow Smith Klein as I originally went as a senior director in the clinical data domain space, but then I quickly took on the position of head of digital clinical trials.

So collectively, I've spent about 15 years in pharma now, and I have combined my technology experience and background with my pharma knowledge from living on the inside, if you will, uh, to create. A Spiro and the IDP platform, which is in the protocol design and development space.

Kim, I have this strange question, but sometimes I wonder about these titles in pharma. And now that you mentioned digital clinical trials, what does it actually mean, digital clinical trials? Do you refer to that as a DCT or digital like process or just tell us more about this?

Yeah, so the role ended up being about 3 years in duration and the concept of the role was, how do we transform drug development using technology? That was a significant area of focus. And at the time, this was pre covid. So it was 2017 to 2020 and I was arguing internally that we could reduce the amount of time it takes to run a trial from 7 to 10 years down to 3 to 5 years.

And people said I was crazy at the time. However, now, obviously, after living through the pandemic and Pfizer, Moderna, J and J proved that we could get a vaccine to market. I know in the case of Pfizer, it was 9 months. Um, from the point of time that the vaccine was developed to testing to FDA emergency use approval.

And so now we've just seen this transition in the industry where they've already proven to themselves that they can do it. So it's no longer. Change agents, uh, saying, Hey, we can do this. We can reduce the amount of time it takes to run a clinical trial. There's a lot of administrative waste, a lot of administrative overhead.

Obviously, you still need the amount of time to measure the biological response in the body. But outside of that, there's a lot that we can do to accelerate getting therapies to market. So, now that they've proven it to themselves, you see a lot more pull in the marketplace. Versus a push where, you know, we're telling them this is, you know, possible now.

They're like, well, yeah, of course it's possible. And now they're saying that now they're looking for the tools. Yeah, to help facilitate that acceleration.

Yeah, two questions actually to elaborate on what you said. My first question is, they said that they're now more open, and they now believe that the timelines for getting the drug to the market can be reduced significantly. They know that this is possible.

And the good news, if I understand is that they no longer say the regulatory are the guilty ones that we have this such a. Like long, uh, like direct development, they actually are looking inside and seeing that they can improve that, which is great. My question actually is, do we think that the tools are the only things that can actually improve our speed?

Do we need more tools?

I don't think it's necessarily more tools. I think it's the right tools. So I think that our industry tends to be slow to change around the adoption of new technology in particular. So, if you think about it, I'll just give an example around the EDC. So the EDC was developed, you know, 23 years ago and the purpose of the EDC was to digitize a Word document. So you had a paper form that was the case report form, and it was literally a Word document that they used and filled out for the visit. Right? And so from a technology perspective, initially, they said, well. We can digitize this paper form. And so they did that and that was the advent of what we now know as the EDC.

If you look at the technology available to us today, so 23 years later, it's much more sophisticated with the amount of data that we can collect, the actual data itself that we can collect so that we have this more well-rounded view of the patient. So I think of it more as patient data capture and being more inclusive about the data that needs to be captured that will help us as we're conducting our research and understanding all of the different factors that go into A patient's experience kind of overall as they're moving through a clinical trial.

But the technology is available, but it's going to be up to us again, as an industry to embrace the idea that we can move away from these concepts, you know, that we're of old and the way that we've always done things to. Okay. We've got to move forward and embrace new ways of doing things, and new ways of thinking. And that even includes new job opportunities and changes.

So whereas before we had a lot of programmers who needed to build the case report forms, hopefully as that process becomes automated, those people can transition into more of like a data scientist type of role. So there's still jobs, there's still opportunities, but it's how those opportunities evolve.

And we evolve as well.

Yeah, makes sense. When we met Kim, I think a few weeks ago, I asked you the normal question I usually ask my guests, what makes or breaks clinical trials? And then you mentioned something that, didn't surprise me, but was definitely spot on, processes. You mentioned that in your opinion, the processes that we have while conducting our clinical trials are actually what stop us from being more efficient and more successful.

Can you elaborate on these processes? Which are these top three processes, for example, that we currently have that are really inefficient and we should focus on them in order to improve our success rate and our efficiency?

Yeah. So, 1 of the primary processes that requires a shift in thinking is that when we initially developed, protocols, and again underpinning the trial itself, we would select sites and then we would hope that those sites would recruit patients. And so, if you think about that there's really an opportunity to turn that upside down and on its head, because we have access to a lot more data now, and we actually know where patients are. So, if you take a combination of data from ClinTrials. gov and a combination of data from Google Trends, which is more patient heat mapping, where are people searching on a specific topic, and I would say disease state, okay?

And then if you bring in claims data as well, which we all have, I mean, every pharma has access to claims data typically that they're using in the real world data kind of space, evidence generation space. So if you bring in those three pieces of data, you have a much more robust view of where patients are.

And so there's definitely an opportunity to change how we think about that process. And instead of, you know, using the same methods that we've always used and, taking a dartboard and throwing darts at it to pick sites and then hoping they recruit patients appropriately. You actually can use all this data.

So companies like Trinetics and Trialbee, and other companies are using and starting to use these data assets to help facilitate patient recruitment. And I would argue in the protocol design space, we're bringing these data assets in so that as you're building the protocol itself, you're actually thinking about.

Where are the patients? And what I hope is that that will remove this contention between the scientists and operations in the protocol design space because the scientists are very focused on the science, the hypothesis, what are we trying to study, but then we go to operationalize it and try to find patients and we fail, right?

And so there's all this, yeah. tension internally and delays. I mean, so if you can restructure your thinking around that business process, there's a massive opportunity for acceleration and success of getting those patients into the trial much faster, reaching the patients that we need to reach because We used to joke as an industry that you were on a wing and a prayer that you, if you needed a clinical trial, that you would walk into the right doctor's office that actually knew about a clinical trial that was going on.

And it shouldn't be like that. I mean, in this day and age, if I'm out here and I have a specific condition, whether it be migraines or cancer. Either 1 or, you know, or any of the above, I should be able to find a clinical trial for myself and be able to see, do I qualify to participate in that clinical trial?

So those are the things and I think as an industry, we intrinsically know. These things, but we also have to be willing to. Make the changes and not just kind of stay in our comfort zone of how we've always done things, but challenge internally to say, Hey, I think there's a new way to think about this.

And there's new solutions in the marketplace and new opportunities just generally speaking. So I would say that's one example, but certainly there's a number of those throughout the clinical trial development process.

And you already mentioned that a little bit, but once again, like, what is the main challenge? Why actually aren't we changing our approach today?

Yeah. And I think it's just human. You know, nature, I mean, it goes back to who moved my cheese. We can all read that book, right? And we all read it probably 20 years ago, but people for some reason are generally very resistant to change. And this industry has holistically been very resistant to change.

And there's also confounding factors, which is if you have a lot of money, which this industry typically has, there isn't a huge motivator. So what is the motivator to change if you are continuing to have good returns and you're very profitable as an industry, then, is there a motivator to create that efficiency or become more efficient?

That piece has been missing historically. However, I continue to argue that just like with every industry, we're not too big to fail. So no company is too big to fail. So if you take the learnings from a company, Like Kodak, who were the leader for a million years in pictures, right?

Taking pictures and they completely missed the opportunity to evolve as an industry into what was then happening around digital pictures and cameras that, you know, our phones, et cetera, and they are now out of business. So, I try to say that as a cautionary tale for these big cash cows in our industry where they're making a lot of money, but I still would argue.

There will come a point in time that if they do not evolve as a company and as an industry, the companies that do evolve will win the day.

I think we're at this inflection point, Kim, when things are changing for multiple reasons. First, we're more and more speaking about value-based healthcare. That's on one side. Personalized medicine. You know, personalized medicine is... It's quite expensive. I was trying to help a mom with her kid who has Duchenne disease and basically, we're looking at one of the latest approved drugs on the market.

It's a genetic therapy, and it's the best on the market. It has really great promise. F D A recently approved it. However, it costs something like, 2. 6 million or 2 million or something like that. I don't remember the currency because the mom is based out of Eastern Europe, so that's like not easy to reimburse this type of money.

And it's not easy even for either governmental authorities or let's say private insurance companies. So probably this pressure will come from the payers and when the payers are not so happy to reimburse. Like happily to reimburse all your treatments, then you have to become more creative and think okay How am I going to actually earn my money now?

And how do I improve my manufacturing and how do I improve my investments and so on and so forth? Because I need to make sure that I keep having this great return on investment And right now we all know that clinical trials are pretty expensive investments. So I guess that there is this inflection point, like I mentioned, coming from multiple parts.

You also mentioned, starting like reversing the process, starting with where the patients are. And what about what the patients want? Do you think companies are good at understanding what the patients want? So they go with the right value proposition for them with the right clinical trial?

Yeah, so I still think we have an opportunity there too. And historically, again, if you think about our industry. We tend to keep an arm's length away from the patient simply of, there, we don't want to have any perception of a conflict of interest or some of the issues that happened with the Tuskegee experiment.

And, again, that patients truly are patients and that the goal of our industry is to get therapies to the patients that need them, but because of history, and some of the things that have happened that are not ideal, you know, the not ideal side of the equation, I think there's a sensitivity to how we listen to or approach patients. So I think what I've seen is there's just this reluctance and so what that provides, though, in my opinion, is an opportunity for 3rd party. Companies to be an intermediary. So again, I don't think that it's that pharma doesn't want to know, or they don't want to hear they do.

They just want to make sure it doesn't appear as, you know, direct recruiting or. You know, leveraging groups of people from an experimentation perspective, right? We had some of that back in the day. From an international perspective there's a careful approach to how we listen to patients.

So is there an opportunity? Absolutely. Do I think, at least at this stage, it really needs to probably be managed by a third party? Yeah, I do. I think that there's an opportunity for third-party companies to package up some information data, help facilitate that deeper understanding of the quality of life issues, things that patients really care about and provide that to pharma.

But, on some level, I understand pharma's, kind of arms-length approach.

Yeah. No, I completely understand and actually, this fear of not being compliant and going, let's say, not against the regulations, but let's say, being fully compliant with regulations, that's probably the thing that really stops the industry of Innovating like other industries for example, the airline business, they also have similar regulatory setup.

They also have these authorities and agencies and strict rules exactly for security reasons, but even they can make different changes, let's say. So I completely understand the companies, but at the same time, speaking about inflection points, there is a second inflection point that's happening.

Actually, it happened. Many years ago, but I think that we will see the effect of this inflection point now. We are seeing it already. The fact that there are fewer and fewer doctors, and you actually mentioned that Kim, that now you need to, you're, if you're a patient having no matter what disease, you'll be extremely lucky if you end up working with a doctor who also knows about clinical trials.

If I'm not mistaken, I read that somewhere. A few months ago, only 10% of doctors were also investigators. That means that you have really, you have to rely on your luck to be with the doctor. That's also a part of a clinical trial so that they actually offer your clinical trial. And that makes it extremely inefficient.

And the inflection point I was speaking about is actually the fact that doctors are less and less in terms of numbers. There is less time for each doctor to spend with the patients in the UK alone. It's, I think the average time the doctor can spend with a patient is 10 minutes.

Do you think that for 10 minutes a doctor will know how do you feel like today? How is your life going in general? And they will exactly know what the patients are going through day to day. Well, probably they won't, uh, and probably we have too high expectations, how much doctors know and how much they actually experience what the patients really go through.

So that's actually, what requires the companies to think out of the box. And instead of going and asking the sites and investigators and like the doctors, what do you think patients want to actually find a direct approach or with a third party, as you mentioned, but it's a must nowadays. It's not a luxury.

It's a must. Um, and you mentioned the protocol design. But last time you also mentioned that you're already working towards a solution. How can we improve the protocol design? Can you tell us more about that?

Yeah. So, I mentioned a couple of different things, but what the Spiro IDP platform does is it brings together insights and then eats protocol. So, capturing the protocol data in a digitized format. So getting it out of Word, right? And then reusing that data and downstream systems. So, again, it's that idea of creating that efficiency.

And I'm, I think that there's probably an opportunity for about 30% efficiency. It could be even more than that, but essentially with the schedule of activities, which is the primary component of the protocol design, right? You have your visit schedule and all the procedures associated with each visit, and that data is used by so many different downstream processes.

But 1 of the primary ones is. The case report form creation. So historically somebody has taken a hard copy document, they take it to their desk, their coding, and building those case report forms that can all be automated now. So we've created a platform where we have a resting API that can connect to Viva or metadata, which is, I think, 85% of the market share in the EDC space. And again, the idea is that we get to this automatic creation and there's a huge cost savings associated with that too, because right now, many pharma companies have a large number of data managers on staff that are doing that.

And as I mentioned, those roles can evolve into, data science-type roles so that you're creating, you mentioned Maya, this idea of value creation. And so when you move to more of a data scientist, leveraging those people, not so much to be doing something that can be automated by computer systems these days, but moving into insights generation, right?

What are the insights now you're starting to get to competitive advantage? How, what can I understand about how to run my trial better, how to find my patients better, how to, to your point? Take in some of that information that you know, about what patients care about better. There's just a lot of opportunity there that hasn't even been touched yet around doing what I call analysis, more so analysis of the data, right?

Most of these companies are sitting on large amounts of data that they haven't even begun. To mine even targets, I mean, going back to the science and targets in the body and using that, that kind of data that was large data sets to really understand more about our therapy areas, what has worked historically, what hasn't.

So I just think again, freeing people up to do higher value-added activities. And certainly, with our platform in the IDP, you know, the IDP platform, if data's locked up in Word, you definitely can't get anything out of it, right? So you have to have the data in an electronic format to be able to do anything at all with it.

And so that was really the concept behind getting this foundational, you know, component of clinical research into an electronic format that is already up and running like this.

Yeah, yeah, great. Thanks for sharing. And you actually got me wondering, uh, you mentioned data and, uh, you mentioned that in the past you were a digital clinical trial leader, I think, GSK, if, if you're now the digital clinical trial leader, no matter what company you're in, which would be these technologies that you will be very focused on to integrate, like to actually follow and probably bring into the company.

Thank you.

Um, well, I think there are opportunities throughout from protocol design all the way through to clinical study close out. I would be looking at something like a Spiro IDP, obviously, to start the process around protocol design. But then, companies to partner with to accelerate in the patient recruitment space.

That's obviously a big opportunity. Um, I think, you know, even how we do some of our scenario planning around operationalizing a trial. There are opportunities there. So there's been a market leader in that space for a long time, but I think there continues to be an opportunity to push the envelope.

Um, and then all the way through to how do we capture patient data? How do we integrate patient data for a more holistic view of the patient? How do we capture investigator data? So a lot of these companies have old school legacy CTMSs that are kind of, you know, uh, It's an old or dated way to think about how we capture that data.

So I think there are new ways to do that. And I guess, more broadly, for me, the headline is ‘Moving away from documents and moving towards data’. So, historically, in the clinical trial space, there's a lot of documents there, that we have historically been shifting around and, yeah, and I would argue that the future is data.

At the highest level, a clinical research process is data in and data out at the very highest level. We have to collect data and then we have to submit data. To regulatory authorities and having any of that data locked up in documents. That's impossible to access or impossible to is not the future. So I would say the headline is at the highest level.

Let's get away from document, you know, document-based processes and move towards data.

We're actually fighting a very similar like problem, but more of the documents that are created when doing patient engagement initiatives, for example, like, it's such an inefficient process in these documents. Basically, they get buried somewhere. So I completely understand what you're saying.

But what I'm actually surprised is that you speak up a lot about data, but you don't mention, for example, things like. Generative AI, or let's say these two twins. So, any thoughts on these technologies and their promise?

Yeah. So I think, I think the promise is there. I think that for me, what I've always said, and , I have Midwestern roots, so I'm very practical about how I approach things. And, so for example, with the generative AI, I mean, we can do it today. So in the Ispiro IDP platform, we can use Chat GPT and generate a protocol.

Is anyone going to use it now? Absolutely not. So there's a little bit of humor for me that we talk about these things. We are, we're not even, you know, walking yet, let alone, you know, let alone ready to run, so can it be done from a technology perspective? Absolutely. Is it the future?

Yeah, 100%. Um, is the industry ready for it now? They are not ready for it, so they need to grasp what I would call some of these foundational concepts 1st around data again. I, you know, I hear people talk about AI and machine learning, and here's the deal. You can't have an artificial intelligence model.

You can't apply machine learning unless you have the data in a format that allows you to train. The machine, you have to have the data in a format that says, here's what I want to train the machine to see to understand. And so if you don't even have the data in a format to be able to do that. You can't apply things like AI and machine learning and anyone who says that they are, I'm always like, no, you're not right.

We know how it works from a technology perspective. So, those things absolutely are the future and we'll get there, but let's get some of these foundational pieces in place first.

Yeah, you're absolutely right. And while I'm enjoying this conversation a lot, Kim, uh, but I say let's wrap it up and my afternoon coffee is almost done. So, uh, but I have one last question to you. Um, and if there is one thing that we change in clinical trials that would have the biggest impact on the success rate, the efficiency of the clinical trial, what would that be in your opinion?

Well, I think that we've hit on a lot of them today, there just is an opportunity throughout the business process overall at the highest level. So you can look at any really, you could pick any point within that business process and say, what's within your control. So again, bringing it back to people who may be listening to say this all makes sense, but what can I do?

Well, for them any process that they have an influence on or that they have input into, they can be looking at that and looking for an opportunity. To improve that process. So I think that's number 1. Number 2, we've talked about, um, you know, documents to data. So again, anything that any 1 of your listeners can do to influence this idea that we're going to be moving or we need to move from documents to data, you know, a date, more of a data driven process, data driven decision making, you know, things of that nature.

And the 3rd thing I would say is. Being willing to change. At the end of the day, that is the biggest blocker that we have as an industry. It's never been that the technology doesn't exist. It's never been that we don't have a lot of really smart people in this industry. It's never been that the patients aren't waiting because they are right there, wait, not they're waiting on us.

Okay, that all of those things are true. So it's that we have to be willing to change as an industry, and we have to be willing to take some risk. We have to be willing to try some things. Not all of those things will turn out all the time. You know, the way that we would like them to. But through every single risk that we take or new solution that we deploy, we will learn something that much.

I know for sure. So, that would be what I would argue is a couple of opportunities and call to action.

Yeah. That's a very powerful message, Kim. Thank you very much for sharing that and thank you very much for your time in general. I want to finish like this episode on the same note. Hey guys, it is hard. We all have these old habits, but. Unless we change them, we won't change anything else. And like Kim said, patients are waiting for us.

So we have a really good motivation to do something about it. Thank you once again, Kim. It's been a pleasure.

Thank you.

Great. It was awesome, Kim. Thank you.