Integrated evidence-generation represent a fundamental shift in the way evidence is generated across functions, geographies, and phases of the product or asset life cycle, and as such entails considerable effort to implement. Manuela Stauss-Grabo, PhD, leads the Global Biomedical Evidence Generation Team within Fresenius Medical Care’s Global Medical Office.
Dr. ManuelaStauss-Grabo joined Fresenius Medical Care in 2013. She earned her Biology Diploma from Julius-Maximilians-University Würzburg and her Ph.D. in Pharmacy from Philipps-University Marburg, both in Germany. With more than 15 years of practical experience in Clinical Research, Manuela is the Head of Global Biomedical Evidence Generation. Utilizing her biology background and research expertise, Manuela is dedicated to generating clinical evidence aimed at better understanding kidney disease and thus to improve the outcomes of renal patients world-wide. Located in Bad Homburg, Germany, her team manages numerous national, international, and multi-regional clinical studies from start to finish, including the development of strategic concepts on implementing and actively using state-of-the-art digital technologies. In 2024 Manuela became a Member of the Supervisory Board for Fresenius Medical Care AG.
[Music] welcome to Global Medical Office [Music] dialogues making better choices about health and health care 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 manuell staso the head of global biomedical evidence generation for fenus medical care joins us today to discuss this important topic manuella welcome to Global Medical Office dialogues thanks so much thanks for having me let’s begin with the basics what in your view is biomedical evidence generation biomedical evidence generation is uh 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 uh to enter a market to gain regulatory approval but also to consider many more aspects for clinicians for patients so it’s uh it’s a v variety of different things that we actually include in that 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 medical evidence generation and can you talk a little bit about that randomized clinical trials they cover the basic the most important thing as I said uh 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 and also uh convincing them that that might be an innovative treatment approach or a novel way of uh offering care for patients but we kind of forget about the patient so it’s a whole journey that we actually have for the patient in mind so really randomized control trials where classically a product a drug or a device is compared to either Placebo or control group um 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 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 real world evidence generation includes already real world so what does that mean it means that we actually look at the clinical setting so how treatment is being done and when I refer back to the journey of a patient it’s uh not that artificial setting that we have with randomized control 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 um how treatment should be adopted and probably changing for a specific indication or or disease um so realwood evidence does cover that part we collect data whilst clinical setting is uh um that’s the standard setting that we observe here with many more patients for randomized control trials you classically have like maybe a couple of hundred or even thousand patients but with r 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 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 that’s such an important question um just recently it gained more and more attention real world evidence uh particularly also by The Regulators but it’s looked as more to augment really what we have as as strong data and evidence from RE from um randomized control trials um remarkably however uh 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 control Trials take uh take place it randomized uh real world evidence will not replace uh randomized control trials but it’s 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 control trial 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 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 shortterm basis but really on the long run so along the entire life cycle for a product classically 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 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 I think actually starts way before the first design of a clinical trial because it sort of sets the stage for what’s going to happen 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 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 lounge or entering a market for a product or a device and uh then along the way we certainly also be adopted accordingly um designed for each and every trial within this integrated evidence generation plan uh 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 our uh indication here so I imagine this is something that really requires an interdisciplinary team yes for sure so that’s uh um nothing that we can cover really in a medical Department as as we have it with INF feno’s medical care but we collaborate very closely with other teams or with other partners in that in that way so um it’s essential that we know about what are our Strat iic uh Focus topics as a company here and and trying to then to develop the best plan and in within this plan the best designs for the respective studies um 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 then and uh um you’re so so much more efficient another important aspect about um integrated evidence generation is that it will hold down costs for for development um you prioritize you have to allocate resources it’s very costly to develop uh Innovative uh therapies and treatments so um by integrating from the start what makes sense for from a marketing perspective economically also then we can come up with a distinct plan to develop a truck or device trying to hold on cost and at the end of the day making an Innovative Treatment available or accessible for a patient 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 yeah to actualizing this it’s all about communication to start with and communication encompasses so many different aspects can be within different teams uh in a in our company for example but also um 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 they have to um make very complex decisions these days because um Health costs are so enormous so taking into consideration what are this the needs and uh and the settings in different geographies makes it so important so that’s that’s one layer of complexity here definitely avoiding that anybody would work in a silo so it’s it’s a team effort it’s a joint goal that we we are uh having in mind and certainly also not forget the 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 and their advice included at fenus 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 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 GE Geographic or Regional locals 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 uh you’re touching on on a very important aspect here so um structure-wise we have to allow for this transparent information flow a database for example where this information can be can be fed in fed into and uh data analytics and other and other methods available to analyze uh the information that we get accordingly um we have to have and that’s what we do a governance process that allows for us really to monitor things to uh to stay in contact and but last not but least we we have those skilled highly trained colleagues that are in the respective countries uh that are in close contact with the clinicians with the patients also with uh payers and uh with Regulators of course so um it’s uh it’s a very detailed structure that’s needed but uh with a um with a clear plan where we’re heading at work working together collaborating U very closely that’s key so this is a relatively new approach to generating a much broader sloth of evidence um 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 yes for sure so there are many aspects to that really so of course course uh 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 uh offer new opportunities here again to augment our data sets so what we classically uh 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 [Music] pisticci 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 how does he she feel really within the treatment and how does this also change along the patient Journey we treat patients that chronically ill uh very often over a long period of time and their needs change and we need to capture that so um we are working really on that data set also to um to capture all these different aspects and then again reflect on them with our clinical development plans so this sounds like you’re generating quite a lot of evidence additional evidence over above and beyond what would be usually collected in traditional clinical research which raises the question about the applicability of L Lang large language models uh Andor machine learning to try and take in all this new evidence and make sense of it do you think that that’s a a useful application of these novel artificial intelligence tools so that’s uh what for example the at the renal Research Institute our experts are very intensively work on to to explore this Uncharted Territory so to say um we’re all fascinated by uh artificial um intelligence these days and for sure we’re we’re testing how we can apply this wisely and uh um 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 um costs in mind otherwise payers also won’t make this accessible so certainly artificial intelligence will help us along the way um I briefly mentioned also simulations so um again adding to the core what we talked about the classical set of evidence generation is is so important it’s a very creative field and uh um I feel we just begun to understand what we can do what is forus Medical Care doing to get ready for this new and ambitious approach to evidence generation so we Foster change management so that with uh not only changes within our company structure-wise to to set up a more ideal structure really to for us all to work as a global team or in in a global setting uh but really also to train um our our colleagues and all the staff members uh so that they have the adequate skill set um and we actively talk about it so it’s it’s really um a steep learning C 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 uh 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 uh it’s a highly dynamic field where we have all to meet all the the challenges that we have these days using however also the new opportunities with u 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 anything you want to add to this very interesting and wide ranging conversation today having the patience at our heart is really what we do on a daily basis in uh um working in such a global setting with uh with a with a large team enables us really to offer patients better more Innovative treatment not only today but hopefully also tomorrow and that what’s what’s motivating to all of us and uh um it’s uh it’s fantastic to work in this field I’ve been joined today by Dr Manuela strabo and we’ve been talking about grated evidence generation and its application in Renal Care manuella thank you again for joining us on global medical office dialoges thank you so much it’s been a real pleasure [Music]