Simon Sonntag Presents Virtonomy at LSI USA ‘23

Transcription 

So at Virtonomy, we're accelerating in derisking medical device development with the use of digital patient trends. So the last two days, we have seen a lot of medical device companies here presenting, they all share the same challenges bringing the device to the market, this is estimated was very long time can be over 10 years, high cost which can exceed $1 million. And a lot of these device devices fail during the process in a clinical trials or being in the clinical practice. And it's not getting easier, with new regulations coming from FDA, but also in Europe as the MDR was making it more and more challenging for these device developers. That's why within the last years, the FDA and other regulators have seen another area coming up. Besides in vitro in vivo testing of medical devices does this the so called in silico testing. So coming from the computer, this is not as safe as a fourth pillar besides the other three areas. And this is why when you develop a medical device, the later you fail during this process, the more difficult it gets for you and the more cost intensive gets. So a lot of these device developers failed during the validation process. Having a design change during the step is much more expensive than in early stage here. 1000 times more expensive even when you're in productions is over 20,000 times more expensive. Now with neuro aspects like computer aided design and also rapid prototyping. So 3d printing, this is shifting a bit more to the left. This computer simulations as we are doing, this can be shifted even more to the left. So during concept stage, a lot of these iterations can be done, de risking the process to failure in a later stage of medical device development. And this is why the FDA is pushing it forward, they are predicting the revenue next year is over 40% of the regulatory process will come from such virtual patients and simulations. They are saying when you can only reduce 10% of the risk to fail later stage, you can already save a lot of money $100 million per medical product or even for large enterprises a lot more. For example, imagine that the stock market's falling because of a failed clinical trial. And this is our solution. We have created V patients where we have created a huge database of several 1000 of data sets. Focusing currently majorly on the cardiovascular domain, we have different patients from different pathologies like, severe heart failure patient but also different valve diseases also coming from different ethnicity groups and also different gender and this because there's a still a huge need for covering this underrepresented populations that is there during clinical trials. So based on this patient patient cohort coming from imaging data with other data about material properties, EEG signals, they can then create a virtual cohort for performing statistical analysis and statistical testing for in defining inclusion exclusion criteria, but also worst case assessment. Combined us with simulation technology we have built up like solid mechanics, fluid dynamics, electrophysics, to investigate and how to devise an explicit body. The results of this can be defined in a report. This digital evidence is submitted to regulators to substituting preclinical and clinical evidence more and more. So, this is how the software looks like you define your patient cohort which can be human but also we have gathered an animal database in the US you can integrate your device so for example, a standard heart valve but also assist devices orthopedic devices and then perform the virtual implementation of your device in the body. And the first time to understand how does it fit into the body? How does it work, there was the Anatole mean and as a second stage to better understand the anatomy, then a statistical population analysis can be performed here, for example, to also devise a for and cluster between male and female patients to see how does the different parameters of volume diameters and so on, interact then also the patient cohort, which will then in the end also cover the whole population, you can also create statistical models like here average models, this variance analysis to see what are the worst cases of this for example, here the order and then based on this also set up and the simulation outcome, so the user has a very simple environment here to be used. So we define this pre that for for you to then have the set up you can define different parameters and then simulate the outcome like for here, in this case, it's the fluid dynamics road, a heartbeat of industrial hot wells. This is for use of an ISO standard assessment of this for hemorrhages anomic investigation, like you can see here then also the fluid dynamics throughout the heart valve. You also look at electric physics, electric physical signals with muscle contraction expansion and also for different cases of healthy and diseased hearts. And all of these assessments can also We'll be done then using virtual reality for them. That'll give you an understanding about the patient anatomy, the outcome and also simulation outcome in this to better see also how does this work in the patient cohort. So one example is for trans femoral access route. So in this case, it's IVC it is interested in also then the diameter and us we have a complete population also severe triggers with heart failure patients. So in this case, the user was interested in defining these diameters of the opening angle the opening of the IVC. So we have done the full cohorts to also see what are the small patients or patients with small diameter, medium diameter and large diameter to assess here on the one hand and better understand the anatomy, but then also to second stage also perform the simulation on all these patients. This case here for the access route, and then also the cannula pathway to see versus critical curvature. Or critical stress areas are when you have steering capabilities to also see that the steering capabilities also work in a more complex set up and situation for example, coming from IVC throat, the trickiest bit valve to the pulmonary valve. Next set up can also then be the simulation of for example, crimping of the stand in insertion into the vessel wall for one individual patient, but also for the whole population cohort that you have built up and based on our database. So, in this case, these are 50 patients, that then the simulation is performed to then also understand the correlation between different material properties like stiffness, but also then the geometric components of the otter and outcome that is done on for example strain or stress. And as to see also other areas of birds not working or do you need to define and set up different designs of a device for example, maybe four designs to cover the whole population and not get in any troubles and also in the outlier regions of this, this is defining more an ISO standard. So 5 840 Very off to heart valve that is under conditions of hammer dynamics to investigate, then the flow conditions. And here this can be done also research simulation outcomes to see the dynamics of the heart valves, we have also validated us against experiments, it's very, very good agreement and us. So this can be used to assess for example, hammer loser, but also thrombus formation risk, and then also orrefors Areum. And this is possible then to investigate not just in perfect round shape as very often the experimental testing this is done, but also an oval shapes and all kinds of different parameters in this to see also under these conditions that are reflecting more and more also than the physics of the body. Then how does this work? This is also more demanded, know by regulators to have this evidence for your approval. Another example is surficial heart. So this is one customer developing heart for them it was needed to understand and what population does my diverse device works if you have severe heart failure patient data here, where they then can do them the virtual implantation of the device to see in what area and what patient group does my device fit or does it not fit, for example, also assessing T 10 measurement, which is the space within the thorax, and then also to change the device in a way to make it more for more patients eligible to implant this device into the body. And also here's an electric physics can be included there to see how does a healthy patient or healthy heart compared to a heart failure patient for example, for pacemaker devices, but also other devices in this. Here you can see some of the outcomes from our customers. So they all say that does have significant insight and time, but also the process and thus speeding up the process giving evidence just not possible as conventional sources of evidence also providing more understanding of the population of the outcomes in this and being done a key tool in the product development but also then, in our approval process where this data from search customers were used for FDA approval, European Commission but also a Japanese agency for their approval. Thank you very much. If you're interested in learn more, please contact me or visit me somewhere around here. Thank you very much.