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Ben Hippen: Making better choices about health and healthcare requires the best possible evidence. As rich and diverse sources of digital data become widely available for research, and as analytical tools continue to grow in power and sophistication, the research and healthcare communities can quickly and efficiently generate the scientific evidence needed to support improved decision-making for patient care.
Dr. Manuela Stauss-Grabo, the head of Global Biomedical Evidence Generation for Fresenius Medical Care joins us today to discuss this important topic.
Manuela, welcome to Global Medical Office Dialogues.
Manuela Stauss Grabo: Thanks so much. Thanks for having me.
Ben Hippen: Let's begin with the basics. What in your view is biomedical evidence generation?
Manuela Stauss-Grabo: Biomedical evidence generation is first of all, all about efficiency and efficacy and safety for drugs and medical devices. But it really includes so much more. It's like a classical aspect to enter market, to gain regulatory approval, but also to consider many more aspects for clinicians, for patients. So it's a variety of different things that we actually include in that.
Ben Hippen: The traditional approach that I think we're used to is, as you mentioned, the randomized clinical trial. But biomedical evidence is, in the way you define it, is much broader than that. Are there different methods that are now being employed to generate these other types of biomedical evidence generation? And can you talk a little bit about that?
Manuela Stauss-Grabo: Randomized clinical trials, they cover the basic, the most important thing, as I said, efficacy and safety.
And that's sort of the start for many different things. First of all, the life cycle, what we call a life cycle for a product. It all starts with entering a market, gaining market authorization. With talking to clinicians and also convincing them that that might be an innovative treatment approach or a novel way of offering care for patients.
But we cannot forget about the patient. So it's a whole journey that we actually have for the patient in mind. So really randomized controlled trials where classically a product, a drug or a device is compared to either placebo or control group, that's the core of it. But that would be more in the start, classically for development, but would not consider the whole journey for the patient.
So we need to cover more than that. And that's what we do with biomedical evidence generation.
Ben Hippen: There's a term that's developed a lot of currency lately, real world evidence generation. Can you talk a little bit about what real world evidence generation is and how it's different from traditional evidence generation?
Manuela Stauss-Grabo: Real world evidence generation includes already real world. So what does that mean? It means that we actually look at the clinical settings, so how treatment is being done. And when I refer back to the journey of a patient, it's not that artificial setting that we have with randomized controlled trials, but actually really looks over the shoulder of the treating physician, asking also and including actively the patient how they feel about their treatment, what are their concerns, what are their needs. And that might change along this journey, as I said, also what clinicians have in mind for how treatment should be adopted and probably changing for a specific indication or disease. So real-world evidence does cover that part.
We collect data whilst clinical setting is, that's the standard setting that we observe here with many more patients. For randomized controlled trials, you classically have like maybe a couple of hundred or even thousand patients, but with Real World Evidence, you can actually collect data from a large cohort, so including many more facets and variants. And that's the strength of this tool, really.
Ben Hippen: Do you see real-world evidence generation as replacing traditional research methods, or do you see it more as a supplement or an addition to traditional research methods?
Manuela Stauss-Grabo: That's such an important question. Just recently, it gained more and more attention, real-world evidence, particularly also by the regulators. But it's looked as more to augment really what we have as strong data and evidence from randomized controlled trials. Remarkably, however, regulators these days take it more and more into consideration.
In 2020, for example, the FDA granted in 75% of all authorizations that were granted, they included real-world evidence. And that takes into account that we can't only look at this first phase of the development, where classically those randomized controlled trials take place. Real-world evidence will not replace randomized controlled trials, but it's an important addition. And in some cases, it will give us really more evidence on what's taking place actually than in this very strict setting of a randomized controlled trial.
Ben Hippen: This seems like a good place to introduce the concept of an integrated clinical plan. Can you talk a little bit about what an integrated clinical plan is and what its uses are?
Manuela Stauss-Grabo: Integrated evidence generation starts ideally very early in the developmental process. And from the onset includes different functions, different stakeholders, a much longer period of time, really. So integration in that case means a lot more communication, more transparency, a lot more planning, not on short-term basis, but really on the long run. So along the entire life cycle for a product classically and being adopted readily as need be. So not only looking at distinct phases for development but trying to plan along the way. So integration in many ways, it also takes into consideration different geographies for examples. We have different ethnicities. Different aspects that we need to cover when we talk about one disease. So this is why I feel integrated evidence generation is so important these days.
Ben Hippen: So in developing an integrated clinical plan, is that something that has to start at the beginning of the design of a clinical trial, or is it something that can be imported later on? What's your view on this?
Manuela Stauss-Grabo: I think actually it starts way before the first design of a clinical trial, because it sort of sets the stage for what's going to happen. Ideally, we talk about clinical development plans in different phases and stages stepwise. So there is not one trial that will cover all the questions that we have. Therefore, integrated planning will take into consideration whatever clinicians feel is needed, what we need to cover from a regulatory point of view, but also what the patients need.
So ideally, it's a plan that starts like two to three years before we even start thinking about a launch or entering a market for a product or a device. And then along the way, we certainly also be adopted accordingly, designed for each and every trial within this integrated evidence generation plan will cover separate aspects or different aspects. I talked about geographies.
It's an ideal idea to have global clinical trials. Question only is, does this fit what we need for this specific treatment or indication here?
Ben Hippen: So I imagine this is something that really requires an interdisciplinary team.
Manuela Stauss-Grabo: Yes, for sure. So that's nothing that we can cover really in a medical department as we have it within Fresenius Medical Care, but we collaborate very closely with other teams or with other partners in that way. So it's essential that we know about what are our strategic focus topics as a company here. And trying to then to develop the best plan and within this plan, the best designs for the respective studies. It's a team effort from the start.
And that's also what it should be ideally, because otherwise you would rather work in silos than... and you're so much more efficient. Another important aspect about integrated evidence generation is that it will hold down costs for development. You prioritize. You have to allocate resources.
It's very costly to develop innovative therapies and treatments. So by integrating from the start, would make sense from a marketing perspective, economically also, then we can come up with a distinct plan to develop a drug or a device, trying to hold down costs and at the end of the day, making an innovative treatment available or accessible for a patient.
Ben Hippen: You've already identified, just in your previous comments, some immediate operational complexities and even barriers to implementing an integrated clinical plan. Can you talk a little bit about what you see the salient barriers are or salient challenges are to actualizing this?
Manuela Stauss-Grabo: It's all about communication to start with and communication encompasses so many different aspects. Can be within different teams in our company, for example, but also enabling that there is communication between authorities, that we are aware of what challenges we might potentially face in different areas of the world. Talking about payers, for example, they have to make very complex decisions these days because health costs are so enormous.
So taking into consideration what are the needs and the settings in different geographies makes it so important. So that's one layer of complexity here. Definitely avoiding that anybody would work in a silo. It's a team effort, it's a joint goal that we are having in mind. And certainly also not forget about the patients.
We need to include them as much as possible. Many authorities do expect this also, and in their own assessment processes, have patient organizations and their advice included. At Fresenius Medical Care, we have the patient needs at our hearts. So we want to put them in the center of what we do, and that starts with asking them what they need.
Ben Hippen: One of the points you made about real world evidence generation is that it really pays a lot of attention to local factors, whether that's geographic or regional locales that have very particular ways of doing things or, in the case of patient experience, may experience the same clinical phenomenon in slightly different ways. How do you maintain fidelity to that local knowledge while also creating a data set that can be at least in part, translated across a lot of different markets or geographies.
Manuela Stauss-Grabo: You're touching on a very important aspect here. So structure wise, we have to allow for this transparent information flow, a database, for example, where this information can be fed into and data analytics and other methods available to analyze the information that we get accordingly. We have to have, and that's what we do, a governance process that allows for us really to monitor things, to stay in contact. And last but not least, we have those skilled, highly trained colleagues that are in their respective countries that are in close contact with the clinicians, with the patients, also with payers and with regulators, of course. So it's a very detailed structure that's needed, but with a clear plan where we're heading at. working together, collaborating very closely, that's key.
Ben Hippen: So this is a relatively new approach to generating a much broader swath of evidence. Are there novel ways of evidence gathering and evidence analysis in the form of data analytics that are being used in this broader conception of biomedical evidence generation?
Manuela Stauss-Grabo: Yes, for sure. There are many aspects to that really. So of course electronic data capturing does offer a lot more options for us really to analyze data. So data analytics and all the new methods that can be applied, also simulations, for example, offer new opportunities here, again, to augment our data sets. So what we classically talk about is the body of evidence of a product or a device. So it all really adds to that and draws a much more detailed picture.
Like a pointillistic picture that you see. When it comes to patients, really, it's patient-reported outcomes that becomes increasingly important here. So we have standardized questionnaires that are used in that case, and we constantly work on them to really capture what are the true needs of a patient or how does he, she feel really within the treatment and how does this also change along the patient journey? We treat patients that are chronically ill very often over a long period of time and their needs change and we need to capture that. So we are working really on that data set also to capture all these different aspects and then again reflect on them with our clinical development plans.
Ben Hippen: So this sounds like you're generating quite a lot of evidence, additional evidence over and above and beyond what would be usually collected in traditional clinical research, which raises the question about the applicability of large language models and or machine learning to try and take in all this new evidence and make sense of it.
Do you think that that's a useful application of these novel artificial intelligence tools?
Manuela Stauss-Grabo: So that's what, for example, at the Renal Research Institute, our experts very intensively work on to explore this uncharted territory, so to say. We are all fascinated by artificial intelligence these days, and for sure we're testing how we can apply this wisely. And again, to improve the efficiency of what we do. Innovative treatment needs to be accessible for patient. In order to establish this, we need to have costs in mind. Otherwise, payers also won't make this accessible. So, certainly artificial intelligence will help us along the way. I briefly mentioned also simulations. So, again, adding to the core what we talked about, the classical set of evidence generation is so important. It's a very creative field and I feel we just begun to understand what we can do.
Ben Hippen: What is Fresenius Medical Care doing to get ready for this new and ambitious approach to evidence generation?
Manuela Stauss-Grabo: So we foster change management so that with not only changes within our companies, structure-wise, to set up a more ideal structure, really, for us all to work as a global team or in a global setting but really also to train our colleagues and all the staff members so that they have the adequate skill set. And we actively talk about it. So it's really a steep learning curve for all of us. And we just don't stand still. We keep moving and we keep looking at things. And I think that's what's so highly motivating about this, that we with clinical research very often one might think about only the classical thing and I think it's a highly dynamic field where we have to meet all the challenges that we have these days using however also the new opportunities with artificial intelligence and all the other methods that are available. There are also new designs for clinical trials for example. There's a huge variety and we're all very excited about that.
Ben Hippen: Anything you want to add to this very interesting and wide-ranging conversation today?
Manuela Stauss-Grabo: Having the patients at our heart is really what we do on a daily basis. And working in such a global setting with a large team enables us really to offer patients better, more innovative treatment, not only today, but hopefully also tomorrow. And that's what's motivating to all of us. And It's fantastic to work in this field.
Ben Hippen: I’ve been joined today by Dr. Manuela Stauss-Grabo, and we’ve been talking about Integrated evidence generation and it’s application in renal care. Manuela, thank you again for joining us on Global Medical Office Dialogues.
Manuela Stauss-Grabo: Thank you so much. It’s been a real pleasure.