In Silico Evidence and the Future of Clinical Evaluation

Hello and welcome, everyone. I'm glad you could join us for today's Friday Unfocus call. Before we get started, I have one thing that I need to do, and that's to promote Francesco. Beautiful. Co-host. More… Make co-host. Yes, confirm co-host. So, Right, and then I'm gonna do a couple announcements, so let me maximize my screen, share screen. And… go to… this… so these are the announcements. Upcoming events. Let's see, while we're waiting for people to join, Wednesday, June 17th at 11 a.m, user critical packaging, 7 Decisions That Make or Break Your MedTech Launch.

With ClareMed and Canyon Labs. And then this is new, we're gonna be, so I wanted to take the Friday, July 3rd, because I figure… you know, a lot of people in the US might not be working, what, with July the 4th, they might be taking that Friday off, so I figured, well. it's not fair to have a speaker on that day, so you're getting me. But what we're launching, is a new program that I think might interest a lot of you, because it's really around the idea of, you know. how do we support MedTech Leading Voices, and how do we create MedTech Leading Voices, where… where does expertise belong in the modern AI-driven world, and how do we distinguish ourselves from the machines?

carbon-based life forms, being, endangered species at this point. So, with that in mind, we're, gonna show you some things, in an hour that you can do without becoming a full-time content creator. The other thing that I wanted to bring up, just really quickly, is, a little photo, just to embarrass our guest. Downloads… Are you seeing my screen? So… I don't know what you guys think, but I think that this is just about as cute as it can be with the Kermit the Frog, and so we're gonna ask him about his,

his little… his little pet, when… when we get around to that. In the meantime, I'm gonna go ahead and get started now. So, Today, we are lucky to have Dr. Suresh Suresh, clinical reviewer from Tuvsood. I'd like to point out that, disclaimer, his views that he's sharing on the show today are his own, and do not necessarily reflect that of anyone that he works for or with. That said, so, I think a lot of people, on this call have, having a nodding acquaintance with in silico. I'd love to know from you guys, like, you know, how much you know on a scale of 1 to 10, 10 being, you know, I'm an in silico expert, to, 0.

And feel free to put that in the chat. You may have heard the term digital twin, you know it exists. You may have… you may not have worked through, what changes for the patient, where about… and where we're about to go. Our guest today is Rare. He's a neurosurgeon. He's also a lawyer. And before he was a regulator, he, sits inside the standards that are shaping this field. So instead of a lecture, this is a conversation that he and I worked out together, and what we're gonna do is go back and forth, with a series of questions and answers, but there's also room for all of you, to join in in the process, and I encourage you to raise your hands or

you know, say, you know, I got, I got some questions here, and we'll entertain that. So, B… Just looking at my notes. So, before we do that, I want to go back to the Kermit photo and say, Suresh, as a small child with your little green puppet, what did you dream of being when you grew up?

So… At the age of 5, I think I would be anything from a garbage collector. to in the army, or working for the police, I think I just went from any possible… Job to job. In fact, I didn't actually settle upon neurosurgery until I was about 13. So yes, I, I think I was more interested in playing. Than I was actually in studying or a future career. I would like to think I've made up for lost time in that sense.

That's pretty… that's pretty good. Now, where do you live?

Right, so I live, in England, near Newcastle. For those of you who don't know Newcastle, we're, as far enough as possible in the near… near the Scottish border.

That's great. Well, thank you for joining us today. I know it's late in the day on a Friday, and we appreciate, your time and having you here. you know, We've talked that you're a neurosurgeon, medical law, you've worked on the standards, What made you walk out of the operating theater and into regulation, and what did you carry with you?

Right, so… There's the long story, and there's the short story, and I'll try and bridge between the two. So, I was always very passionate about neurosurgery. In fact, I… for my generation, I became a consultant at a fairly young age. I was in my mid-30s, which for… at that time was young. So I was in… as a consultant, which is, I think, equivalent to your… to your attendings in America. I saw a lot of change in the NHS over the 12 years that I… that I was there as a consultant. You know, it changed from being a vocation for what you passionately want to do, to basically being, well, this is just a job, which was, unfortunately, the disillusionment of a lot of healthcare workers across the NHS, and I'm sure that's true around the rest of the world.

But I was also doing a lot of national work, a lot of lobbying nationally, to try and help improve workforce conditions, especially for junior doctors. But I noticed there was a change. There was a definite culture change, and it lost the appeal that it had for a lot of us, a lot of people of my generation. The year I resigned, it's sad to say, two consultant neurosurgeons committed suicide. And several neurosurgeons had quit for various reasons, but mainly because of the way that the system had gone, and that was just neurosurgery. But then, I'll never forget, I've got this random phone call.

When I was in the car park of the hospital, from a recruiter. I wasn't even expecting it. Saying, have you ever thought about the medical device regulatory affairs industry? And being a clinician for over 20 years by that stage, it's just like, well, what on earth is regulatory affairs? And I think this is a discussion for another day, is for the actual impact of what we do on the clinical side. So after initially started telling her, I wasn't interested. She kept coming back. And after a few calls, I, started to think more about it, thinking to myself, okay, well, what does this mean? What… what… how can I, you know, if this is something that I could actually pursue?

And I'd done a, as you said about the law degree, I'd done a master's in law in medical… in clinical negligence, in medical law. And for… the more I started looking into regulatory affairs, I started to realize you could combine your… your new surgical clinical expertise with the regulatory side, the legal side, which is actually what I'm really quite interested in. And I think, you know, I remember speaking to somebody around that time, and they said, you know, you go into an operating theater, and you're trying to help as best you can one patient at that time. But if you think about regulation, what you're trying to do is help a thousand people. You're trying to help a million people, you're trying to help people all over the area that you're working, whether that's Europe, the UK, or the States, wherever it is in the world.

To try and get the best kind of devices of healthcare that… that they can… they can receive. So, hence why… why I moved, you know. Do I miss the surgical part, you know, for cutting into patients' brains? Yes, yes, I absolutely do, but, you know, I am a keen, passionate landscape gardener. I'm a keen, passionate DIYer. I've got a workshop that's converted to my own operating lab. But I've swapped that for being able to do a job where I speak to people, you know, like yourselves, who are all over the world. I get to engage in meaningful conversations, which I would hope would mean that we can try and drive healthcare.

on a more global expanse, but in a positive way. So, yes, here I am. Free and a bit years later.

I like that framework, I like the idea of moving from one to one to one to many. I haven't ever really heard that with regard to regulatory affairs, before, that, you know, the job of regulatory affairs is to serve thousands. If not hundreds of thousands of patients, and I think that's… that's a beautiful way of putting it. So, moving on to the working definition of in silico, for anyone hearing in silico for the first time, or, you know, not terribly familiar of it, could you give us a… sort of one paragraph description, what it is plainly, what it is not, you know?

Yeah, that's a very good question, because I have this discussion with family and friends all the time, you know, what is in silico? Because, you know, AI is… it's straightforward, it's artificial intelligence, but in silica, you know, some people call it in silico, some people call it synthetic, some people call it digital twins, but what does it actually mean? Well. In silico, silico, as in silicon chips in computers, just means that work that's done within a computer. But what does it actually mean? You know, if you go beyond the fancy jargon, what it actually means is using a computer simulation.

to try and replicate what you do in the real world, to then determine what those outcomes are and how you can better improve what you do in the real world. I mean, there's clearly a branding issue in terms of the jargon, as you quite rightly say, but in simplistic form, it's using computer simulations, a bit like SimSetting. I know that's a bit of a horrendous analogy, but using computer model to work it right. How's this real world working? Can we simulate that real world? And can we find better ways to use a computer model that can then help the real world? I hope that's helped.

So… When it comes to governing law. the regulators have started naming it in silico explicitly. MDR revision, FDA precedent, the ISO, you're a part of a working group of. Give us the 90-second lay of the land when… let's get down to, you know, the regulatory impact of…

Okay.

Where we are right now.

Right. This is impossible 90 seconds. Essentially. If you want it summed up to the 90 seconds. It's a two-tiered world problem. Which is, you've got, a huge amount of evolution in the States, with the FDA, with the ASME VMV40, which is accepting in silico. It's got methodologies for looking at the computer modeling. And then you've got the… let's just say the EUMDR, for example, where you've got the first iteration, which had some reference to it. You've got various MSG guidance documents, which have now really improved the discussion, and therefore the inclusion of in silico. And you've got the proposed MDR 2.0, which we're obviously waiting for the final version of, but the proposed version does have InSilico computation modeling within its lexicon, which is basically saying, right, consider adopting InSilico if you can't get the clinical data elsewhere.

So that's your two-tier problem. You've got the US versus the world, and I know that sounds very grandiose, but unfortunately, in silico it is, and I know I'm going over 90 seconds, so I apologize. But you also have international frameworks as well. You know, there's an international working group. On clinical investigations, which has started defining that with invent documents. But there's also been a silico technologies ISO standard, which is currently being worked on. And that's looking exactly at the terminology, you know, why is it so… call in silica, what is… what's the symbolic data? What does zero twins mean? And it's also looking at credibility systems to try and unify and harmonize the process. So, that's in a nutshell. I have gone over 90 seconds, so I'm sorry, but you have this two-tier problem, but there are things developing, especially over the last year.

Which is helping to change that landscape, and harmonize that landscape as well.

That's very interesting. There are a number of, private organizations as well that are championing this, if I'm not wrong. Do you know… can you tell us who they are, or, like, you know, what… what some… some of the items…

Absolutely. So, we can narrow this down between your global players and your more localized company-focused academic institutions, say. If you look at the more global players in this, you've got the Avaccino Alliance. Avaccino, of course. was a scientist from, I think, 9980, Common Era. And essentially, what the Avatuna Alliance is there for is to try and promote the understanding of what computational modeling can provide. And how that can then be, you know, changed in terms of legislation, in terms of regulation, not just in one regulatory territory, but across the world. You know, I sit on meetings with Avocil, where you have people from states, from Europe, from the UK, from Australasia, so it completely… Avocida, you know, tells us that entire area.

You've got some UK, based groups as well, like the UK Searcy, led by people like, you know, great work by people like Alex Frangier and his entire team, which are driving, in silicone adoption within the UK MHRA framework, and they're doing some great work with Mark Gumbridge at the MHRA. And then, of course, you've got the VPH, which is very much a conglomerate of very academic institutions, which are looking at the fundamental science behind this, and trying to advocate, and trying to see how you could push forward. So these are the… you know, the big players, and I apologize if I've not included anybody in that… in my discussion, but these are your big international players who are really pushing forward for the understanding, the communication, the lobbying.

of incident trials. But you also have academic institutions. It'd be unfair for me to name a few. There are quite a number, I have to say, who… and I've had some great conversations with them, and they're all very… They're all doing some brilliant work at actually working with manufacturers, look at computer modeling, going, right, how can we put that into a real-world context? What can we do to help your design of, let's say, a knee implant or a shoulder implant? So, there are several actors on this stage. You know, it's not where things were 5, 10 years ago. It is extraordinarily promising just how much discussion there is going on.

And that the regulators are actually taking note of what's happening as well. It's brilliant. I'm an optimist in this era.

They're also private companies. Moving the ball forward, yes. Are you seeing more and more enter the market? What, what is, what, what is, what is the appetite from investors, from the, you know, the community, for this, this kind of, this kind of,

Yeah.

loose.

I mean, I can't talk on behalf of how the investors work, that's not my… it's not where I come from. You know, I come from a clinical and regulatory aspect, but what I would say is, you know, I've had some great conversations with companies who are dedicated in this area. There is a huge amount of great work, as I was saying to you before, that these companies are doing. It is now about really burying those partnerships up with the right manufacturers to take those modeling compare it against real-world data, and actually see that provisioning. There's a pipeline in all of this, and we're… and I say this in the other talks I give, which is about where we're at in terms of the adoption, therefore, implementation of InSilica. We are very much at the lag phase of adoption.

But this is a pipeline, this is a 5-10 year pipeline, and I do believe in the next 5 to 10 years, we will see more and more medical devices being applied for in, you know, in various regulatory territories, which have got some component of an in silico clinical trial. As part of the paradigm. And you know, I have to say, having conversations with multiple companies over the last few, especially the last 6 months, there's some promising work going out there. It is going to take time, but you've got to start somewhere. You know, AI wasn't invented out of blue. AI didn't suddenly evolve from absolutely nothing. AI's been around for decades, but you see what's happening with AI now.

But in silico is something that's going to have real-life applications to how healthcare is driven and it is delivered, and the way that manufacturers will therefore embrace it and therefore take that on. But it is a pipeline, and we have got, you know, we will see things evolve over time.

As a clinical reviewer at the notified body, and, what you know about the other notified bodies and the clinical reviewers, you know, what is the general, mood towards, or, you know, openness to this kind of evidence?

It's interesting, you know, because I've done a couple RAPS presentations on… on in silica. The first one I did was in Pittsburgh, and the room was probably about a quarter to half full. And the last one I did… I've got another one coming up in Charlottesville, this September, but the last one I did was in Lisbon, where I met you. And the room was full. And it was packed. And it wasn't just packed with manufacturers or consultants, and so forth. There were a lot of people from other Notify bodies.

Hmm.

And I've had some fantastic conversations with other members of the Oven of Voice going, you know, they've read my… I did a white paper on this area last year. And I think there's a huge amount of interest. But, you know, the thing with no football is they're very reactionary. They're reactionary to what MDR and the MBCG Government's papers say. But as I said to you before, we're seeing evolution in that. We're seeing things evolve in terms of what can be included and have from an MCG guns perspective. So Netflix is more and more, you know, asking questions, well, how can we approach this? How can we adopt this? What does that look like?

And I've, you know, I've been very happy to have conversations with people. You know, I'm trying to look at a more coordinated approach with Team MB, and that would be extremely good to be able to try and harmonize what all notified bodies do, of course. But yes, I think excitement and interest is growing. It is going to take time. We do need to start with something. We need to be able to actually have something palpable to go, right, this is how it would work, this is how it will run through an application MGR process, and this is the upshot of how this can then apply to what it is in the real world.

Because we need that evidence. We need to start somewhere.

So, if we wanted to get a copy of that white paper, is it possible to do so?

Absolutely.

Just write white paper in the chat, and I'll figure out a way to get it to you. That… that'll work, and then we'll spend time on that. So, you've told me that the… told us that the real, selling point isn't regulatory, it's clinical, from a surgeon's chair. What does it mean to expose fewer high-risk patients to unproven… to an unproven device?

Okay, so talk about the patient reality, and this is… this is a hobby horse, and I kind of think this is kind of where we need to drive the narrative. So I'm just going to go back 5 to 10 years and talk about the frustrations that I had. So I was… yes, I was a neurosurgeon, but my main area was neuro-oncology, it was brain tumour research. And I was often asked to give second and third opinions, because there would be some patients with bad brain tumors who… and unfortunately, these are devastating, consequences. They're devastating pathologies. And you'd have people who would go on the internet, and Google is a great resource, but it's also a very disastrous resource for people who are desperate, desperate for

For very, very good reasons. And they'll find some private clinic somewhere who will say, right, we have got this, and we'll be able to give you a vaccine, for example, but it will cost you 150,000 to £1,000 to have a treatment. Which is not in the, you know, the capacity for most patients, for the vast majority of patients. And I had a patient come to me and say, listen, we've had to remortgage our house, we've had to sell our house, because we, you know, we want some kind of new thing that could help us. And it was absolutely disastrous. You know, I remember my first year as a junior doctor having to break bad news to a patient out, you know, even as a junior doctor in neurosurgery, telling a patient about brain tumors, and over 20 years, nothing had changed. But can you imagine if you were in a situation

where you have these high-risk patients, so I did a trial which looked at, fluorescence. These are high-risk patients, and, you know, we went through the ethics, we went through the grant applications, we did patient focus groups, and the thing with patient focus groups is that people… patients are so eager to hear about research, they're so eager. But equally, what is their main priority? Their main priority is to have something that's going to help them. Okay? But what is the vast majority of of trials, the vast majority of research, isn't necessarily going to end with a success, but has to be…

Prototype has to be, somehow. You know, tested, doesn't it, on patients? And if you're looking at high-risk patients on high-risk devices, that is a huge ask. And I think this is where, you know, trying to go take a step back and go, right, what are the alternatives? What other ways can we find in order to try and mitigate that risk, to try and improve their outcomes. And I think, you know, computational modeling, or in silico, however you want to call it. It has to be part of that conversation. It has to be part of the way that, you know, clinicians who are undertaking research, or healthcare institutions, or healthcare, or, you know, natural societies who are driving research, they need to be talking about this, because these patients are so desperate that they will subject themselves.

Two, high-risk procedures which are unproven. But can we find a better way? I hope so. And I think computational modeling is one possibility of where we can drive that.

So, you're talking about pre-market at that point. Now let's talk about the life cycle of the product and the application of in silico testing throughout the life cycle of the product for a moment.

Okay, so we could break this down into, you know, the pre-market and the post-market. In the pre-market, you divide that even further into the actual design of the device, so your prototyping, and then your pre-market clinical investigation. So let's look at the prototyping. you know, we've… there's a lot of controversy around… around animal testing and animal models, so could Incidoco, you know, redefine that? Yes, absolutely. Could Incidoco replace animal models? Yes, absolutely. You know, your prototyping your feasibility is a huge aspect of your medical device development, and that can be done in some other way, which doesn't involve unnecessary harm to animals, or doesn't involve unnecessary harm to humans in the first pilot stages.

The majority of that's got to be a game changer. And also, if you can try and reduce that, because it's a significant amount of time it's taken to actually do your prototyping and your feasibility studies, or even for you to do an exploratory clinical study or a pilot study, that's a huge lag stage in your medical device, you know, pathway. In terms of your pre-market clinical investigations, you know, I'm certainly no advocate of completely replacing clinical investigations in its entirety. You know, you still need your defined endpoints. But if you could do a smaller trial, if you could do less patients, if you could do it over a shorter time period.

But supplement that. With an in silico clinical trial, we could run thousands of simulations amongst all different types of groups and different types of scenarios. Then again, you could reduce the lag fees to market access, you could reduce the harm. to patients, you know, let's not forget Hippocrates oath, which has been on the jury, which is, first of all, do no harm. Surely this is what we need to be doing. We need to be driving alternative methods to reduce the harm and the impact on patients. Now let's jump ahead, and let's jump ahead at the post-market phase. And let's say, for example, you have a rare disease, and you get this rare complication that you've never come across before.

What are you going to do? You're going to say as a regulator or as a manufacturer, right, we've got this really weird outcome complication, nobody's ever heard about it before, it's never came in any of our pre-market things, but we need to somehow work out how we're going to do about this. Do we need to do another 100 patients, and do another 1,000 patients in order to assess that? Or… Could you run that through your simulation? You know what your endpoint is, you know where your other endpoints are, could you run that in your simulation as part of your post-market phase in order to determine how significant is that complication, put that through your risk management process, and then determine it that way. So again, it's running computational modeling alongside

Your real-world data, your real-world data collection. So, you know, throughout your entire pathway, from your design, your pre-market clinical investigations, to then your post-market phase. There's a huge amount of areas which can be aided, can be enhanced. I'm not saying at any point in this, certainly not at this point, or even the next 5 to 10 years, that in silico should replace this, but I do believe that there are areas which can be augmented by computational modeling.

So, Daniel posted an interesting question in the chat. He said, tell us how in silico testing influences factors such as quality, efficacy, safety, and purity. Also, how does it eventually set the stage for GXP compliance expectations?

GSPR expectations. So, essentially. When you're doing your endpoints, you're looking at the safety, you're running modeling on the safety, you're running to determine what are the possible outcome complications. from using that device in that particular area. You can run physiological modeling to… anatomical modeling to look at that, and the cardiac area is a big area where you… there's a lot of hemodynamics evidence. So you can look at efficacy, because if you know what your efficacy endpoints are from your real world, if you know what the safety endpoints are from your real world, you can then compare the like-for-like and therefore be compliant with your GSPR, but

The thing to remember is you're not doing this just purely on its own merits. You're doing this validating all this information against your real-world data points, so you still need to have Clinical data to run alongside it to show that whatever data you're producing, generating from urine silico testing is therefore similar to, equal to, or better than your real-world data sets.

Yeah, and the question I had was… when you are doing these computational studies, will those factors also be part of what you're evaluating, or is it just safety? So I was looking to see if.

Good.

You're actually looking at all of these other factors, and you're.

Yeah. So, yeah. So, you know, as a disclaimer, I'm not an engineer, I'm not a statistician, I'm not a model arrivor, I'm purely someone who's got two hats in terms of the regulatory and the clinical aspect. So, from my perspective as a clinician, what I'm looking for, because the independent verification validation steps need to be done by the manufacturer. We are there to say, right, first of all. Can we… do we agree with the validation? Which is, do we agree that the safety of performance endpoints that the manufacturer declaring are similar. to the real-world data sets. So we look at the real-world data to do that, and then there's a verification, step.

which is… and we have people in-house who do this, who basically look at the model, and look at the verification that the manufacturer has provided, and go, right, we agree that their verification steps are appropriate. This is where we are in the EU, obviously very different from from the US. But that's where… that's what we would look at. You know, I don't pretend to be a model or a statistician, I couldn't tell you about the programming, that's not my area of expertise. But I could tell you about what we would look at.

I appreciate the response, Suresh.

I'm not sure if somebody had their hand up or not. I saw a hand raised. If you have a question and you want to ask a question, please raise your hand. In the meantime, why don't you, tell us, what do people get wrong about in silica? Let's start with the biggest one. Let's bust some myths. Mythbuster.

I think there… there are… there are many… oh, sorry, there's a hand up there.

Oh, yeah, Maria.

Maria E. Donawa, MD: Yes. So, if we're thinking about a new device, as an example, let's say an implantable device, non-active device, just as an example. Maria E. Donawa, MD: Are we at the point where we can define what Ensilica can and cannot do?

In what sense? Sorry.

Maria E. Donawa, MD: where you're going to actually need clinical testing versus being able to use non-clinical testing. I mean, actually, the terminology simulated use is going through my head, because that's, of course, as you know, where you actually have the device Maria E. Donawa, MD: and you're not using it in patients, it's simulated. So now I'm wondering.

Yeah, yeah, that's very, very important. Yeah, absolutely. So, you've got two parts to this, you know. What can computation modeling look at? And let's take a… a knee prosthesis. Look at an implant. Let's look at a knee prosthesis. So, you've got two parts to what a computational model can look at. The first part is, well, what has been asked me? What's the biomechanics of that knee? How's that knee going to work without an implanter and with an implant? But the second thing is, okay, let's say you're… You're looking at, range of motion. So, and again, you know, I'm not a modeler, I cannot give you full clarity on all the clever science, I'm not a clever scientist.

But what an in silico clinical trial would look like would be to look at, right, well, what would be the range of movements? What would be the pain scores? What would be the risk of infection? What would be the risk of, you know, revision rate surgery? So that's what the Enceilco clinical trial's trying to weed out. is the clinical data sets that we are interested in. So when a manufacturer applies, of course, to… to us as a regulator, we're asking, what is the safety, you know, what are the adverse events? You know, what can go wrong, you know, from their soda, and then what it is for their device?

And then we're asking, what's the efficacy? So, how good does it work? Does it improve a range of movements? You know, things like that. So, when we then look at what clinical trial. is trying to give us. We're looking for the same safety and efficacy endpoints. We're looking to see, right, how good is that trial compared to an actual conventional clinical trial? The big difference is if you have, let's say, you know, coming back to our high-risk device and a high-risk patient, let's say you've got a brain tumor patient, and what you really want, you want… and this comes back to the whole concept of sufficient clinical data, which is, you know, banded around by a lot of regulatory authorities.

Which is, well, how do you judge? Is 5 patients who've got bad brain tumors sufficient? Or could you perhaps take, right, can I have… Let's say, 10 or 15 brain tumor patients, but perhaps run alongside that. X number of computer simulations looking at safety and looking at efficiency. So that's where the… the enablement, the augmentation occurs, is when you've got… when you… there's the need for doing Either. Devices on high-risk patients, for example, you don't necessarily have the right numbers in terms of patients because of difficulties with recruitment, difficulties with the performance you're dealing with, but instead, what can you do? Well, could you possibly do a computer simulation to run alongside that to help augment

And complement the data that you gain from your real world.

Maria E. Donawa, MD: Or… but if you don't have real world? Maria E. Donawa, MD: So, you know, you don't have real world. It's not out there yet. Maria E. Donawa, MD: I've got so many questions, I'm not even gonna go forward, because…

It's a business…

Maria E. Donawa, MD: Yeah, didn't matter.

It's a blank, yeah.

Maria E. Donawa, MD: Are you.

Yeah, and this is actually a big issue when you look at the breakthrough in the new innovations. Okay, let's go back to the COVID pandemic. you know, why was there such a massive delay? Because we needed the clinical data sets. But, you know, it's not that in silica is necessarily going to go right on day one, we've got no clinical data, therefore we're just going to do an in-silica model, and we're going to send another. I cannot imagine that a regulator in the EU or the UK is going to accept that. We're not at that stage. that may be a future utopia, you know, many years down the line, but we're not at that stage now. But if, let's say, instead, we're at COVID pandemic March 2020, right?

We have one or two months' worth of preliminary clinical days. I'm purely hypothesizing, I'm not saying this is the answer, I'm purely hypothesizing. We're now at May 2020. We've got a couple months' worth of clinical data. Why don't we run a computer simulation? on that clinical agent, see what the endpoints are. Will that therefore help us in developing a vaccine? Now, I don't know the answer to that, but this is the kind of scenario I'm trying to talk about, is I don't think you can do it with no clinical data, and certainly we wouldn't look at it with no clinical data. maybe in 10, 20 years' time, that discussion may have shifted. But at this stage.

you get some early preliminary results out there. You run, you come to your simulation, see, right, how will that pan out? So these are the conversations that needs to happen, and until you actually start looking at those comparators, it's difficult to see how they will play out. But at this stage. We certainly, and I don't know anybody that would, take it with no clinical data whatsoever, but we're not going to say we're going to go from March 2020 till 2021, when the first vaccine, you know, rollout program's actually occurred. What we're saying is, well, when we go from March 2020 till May, June, July of 2020, have some preliminary data, and then run computational models. Could that be a game changer?

So, on that point, I would like to ask Diane Legere from DNV to, ask her questions. She's a clinician as well.

Yeah, thank you for this great presentation, and looking forward to seeing you in Charlotte. So, last year, I presented at an OSMA, conference, and there was someone from, obviously orthopedics, and when she presented using in silico, she… her bottom line was the amount of effort and man-hours and cost at this point, for her and her team, she felt that cadaver and animal models, she couldn't make the business case for it at this point. Yeah. And so I just wondered if you had any thoughts on that.

Yep, this is a really, really good point, which is the initial startup costs, and they're not insignificant, as you will appreciate. you know, you first of all got to look at the software and the hardware. You need to look at the expertise, you need to look at the personnel. You know, the people who are actually going to be running the models, the people who are actually going to be… you know, interpreting the data as well. So, first of all, you know, and obviously this is a big issue if you're a smaller size, or a smaller or medium-sized company, which is, you know, you first of all got to have that outlay.

Which is, you know, investing in the hardware, investing in the software, investing in the personnel, you can actually interpret that and take that on in terms of your quality management systems, in terms of your regulation, in terms of your clinical data set, as you recognize, there's a huge amount, which is absolutely true. But we all… it is about working. Where do we start? Because is animal testing? Is… Calovet testing is about providing unnecessary delays to innovation, to the way that medical advisors are coming to the market. Are there alternative strategies? Is it that manufacturers, especially SMEs, excuse me, sorry, SMEs can actually work with independent academic institutions, and there are plenty of them out there, who really have that investment, who really have that infrastructure, who can work with that manufacturer.

You know, we know that manufacturers work with consultants, this is just a slightly different type of consultant, which is, could they not work with these, fantastic academic institutions and go right? We want to bring this device out. At the moment, we are struggling because of cost. We, you know, it's… let's say, for example, you know, all for medical device, few are going to be ever, ever produced, and therefore going to be, manufactured, therefore the costs are not going to be significant. So. How can we find a different way to do this? And I'm purely putting these thoughts out there. Which is, right, well, why don't you work with one of these institutions who are actually doing this kind of work all the time.

You can work with them to do the Molly Baker, they've really got the personal, they've really got the software, very good with the hardware, and they can help you along that part. So I completely understand what you're saying, and you're absolutely right. You know, your initial setup course is huge. But, there are other ways to be able to approach this as well. And you've also got to balance that argument. This is why I think, you know, the ethics behind all this is actually really fascinating, which is balancing it against, right, what are the ethics between calaveric and animal testing? What is the ethics of delaying treatment to the market, if you can get the market on there?

What are the ethics? For example, if you… and I gave an ethics talk the other day about this. I'm sorry, it got me into a hobby horse, which is, okay, right, let's say you had a device. And you had in silico, As a possibility. To use, but you as a manufacturer chose not to. You then market your device, and a catastrophic complication happens. Now. This is, again, a question of the law, and this is me with my… my… My paranoia in the legal sense come into mind, which is, how do you defend your position If you could have run that simulation. via an in silica model, and therefore prevented that complication from happening.

So there's lots of things that have to be balanced in this discussion, and I completely, completely understand and support what you're saying, but I think there's lots of other parts to this as well. And then looking at the bigger picture, looking at the long-term picture, and going, right, well, how's this going to affect us long-term? Is this actually going to improve things for patients? what's the return on investment going to be on if we invest in this part? Could we perhaps instead… work with the institutions who have dedicated solely to in silica data generation, as perhaps as a kind of a halfway house for now.

To actually start using that technology.

Yeah, sure, you know, I see, you know, I read your white paper, and the collaboration part comes through clearly. And as you're talking, I started thinking about earlier day registries, and some of the modern as well, where lots of different plays, actors come together and share… share that data, so… Taken up a lot of your time, I see lots of questions, so thank you.

Perfect question, thank you very much.

So someone asked, I have two related questions on the methodology. First, on the virtual cohort itself. How do you actually select the population for an in silico study? That's number one.

Okay, right. So, as again, you know, for disclaimers, I'm not a modeler, I'm not a statistician, but let me… let me give you… a different way of looking at this. You know, I can't tell you how the verification, validation, individual steps work. I can't tell you how the various computer models work. That's not my area, but let me give you a different scenario, if I may, and I know I'm going off at a tangent. You talk about model populations, and I think that's a really important question. So, what is the fundamental problem? with a conventional clinical trial. It is, if it is a single center trial.

you were looking at one cohort of patients. So, for example, if you were to take it to… and I'm going to talk about the UK here. If you were to take it to London, you've got a multicultural population, therefore, you know, with the best one in the world, you're going to hit as many different ethnic, diverse groups and age populations and gender populations as possible. You take that out to the countryside, for example, where I live, where I am literally the only Asian person in my village. You're not. You're not gonna hit diverse populations. So, conventional trials really struggle at being able to look at population recruitment. Therefore, there is a significant amount of population bias.

Which means that you don't actually quite know how your device is going to work, or whatever it is you're trying to develop, in terms of populations, not just in the UK, but in Europe, in Asia, in Africa, or in America. If you could have the correct models in terms of computation modeling. which could have those endpoints, and I'm not saying they're available at this stage, but this is the aspiration. Is could you not, therefore, do your model. in these different population cohorts, going, right, let's have people from Asia, people from Africa, from America, from Europe, and then look at how the outcomes work in each one of those… in that scenario.

So, yes, I cannot tell you about how the science… I will not blind you with science, because I don't know it, but what I'm saying to you is, what is the potential? And what is the struggle of a conventional clinical trial, and what could Ensilico potentially do to try and overcome that natural selection bias. But it is important. You know, you look at AI algorithms and facial recognition, we know how AI is fundamentally flawed. You look at dermatology programs, software programs, which are AI-driven, which are looking at skin lesions, but they are fundamentally flawed because they cannot do racial… differences in racial skin tones.

So, you know, could you perhaps consider, if the endpoints were available at some point in the future, and I don't know whether they are, could you therefore run your dermatology program on different population groups and go, right. This is how this would look. You know, SATS probes, fantastic example. We know that SATS probes are designed for Caucasian skin. We also know, from my own personal experience, that when a SAT probe comes on my skin, I get 90%. It's not because I'm ill. It's not because I've got bad lungs. It's not because I've had a bad lifestyle. It's because SATS probes are not designed for people with my skin color.

Do you see what I'm trying to get at here? And I think there's something here that could be really helped and benefited. by using modeling. I'm not saying that's where we are now, because I don't know, but this is what I'm saying, is what is the potential?

That's interesting. So, speaking of diversity, you know, when I posted about the fact that you were going to be speaking, and I said, you know, we declared a device safe 300-patient trial for, you know, hundreds of thousands of cardiac patients, somebody wrote, duh, it's called physiology, you know, like, and I think it was intended, is it, you know, a ding against my comment, you know? If it works in the human body, it works in the human body, you know, and… I think that falls into the category of myths, I think it falls into the category of misconceptions, and I think it falls into the category of…

That may be true with some types of devices. For example, a screw is a screw is a screw. However…

I read that comment, and I answered where it was coming from, but do you know the first thing that came to mind?

So let's… let's assume it comes from a place of generosity.

Absolutely, absolutely right. Yeah, no, and that's what I was saying, you know, I completely understand where it comes from, but the first thing that sprung to mind was something I was taught in medical school. Now, obviously, that was 25 years ago, but… And please forgive me if I've got this wrong, and, you know, please correct me if I've got this wrong, but the Japanese have got a rare animal called E4E4, which makes them more susceptible to alcohol, which means that if they drink, they get drunk fast. Now, that may be complete nonsense, of course, but… I'm not going to get into any ethical debate about racial diversity. I'm not going to go into any debate about how humans evolved, whether that was from the out-of-Africa, Lucy Etheria, whether that was because of the five separate populations, but we cannot assume that there's going to be complete, homogeneity

amongst all populations. We have to understand that there's going to be differences. We know, for example. When you look at communicable disease profiling, you look at immigrant… first generation immigrant communities, and you look at second generation, third generation, what happens, you get a dilution effect, where finally the second and third generations almost assimilate for the people of that… well. I use the word Indigenous very loosely, but the people of that country who aren't immigrants. So we know that, you know, over time, things do change, but we also know that there are clear separations, and I think, you know, this is… I completely understand where it's coming from in terms of physiology's physiology, but actually, there are differences.

None of those differences are bad, you know, differences are to be celebrated and to be empowered, but it's about, right, how do we check for that? How do we, you know, do we… this is something we need to look at, but it's what we can't necessarily achieve, and I gave that… The dermatology example and facial profiling as a very similar idea to that, which is, well, it doesn't work all the same. You know, we know about skin cancer, we know melanomas don't work in the same way that it works for people from the African subcontinent, or from Asia, you know, they get a different type of melanoma. Disease works differently.

in different cohorts, and I think we need to make sure that that's in our minds, especially when it comes to clinical research, when these things are looked at.

It seems to me that there would be an almost greater application for IVDs. Then.

Yeah, I think there's… I think across the board, there is its uses, and I think, you know, I wouldn't say it's necessarily one compared to the other. But I do think, you know, its application, you know, in the medicinal area, in MDRs and MDRs, it's this huge amount of scope.

So, I want to make sure, before we go to the crystal ball section, where you're going to give us your, predictions for the future, that we get a chance to, have anybody who has any, burning questions the opportunity to come on the camera and ask them. Does anybody have, like, a burning thing that they're dying to know? Or say, or… Okay, Christine has raised her hand. I'm… while I'm working on Christine, anybody else? Panelists, okay, so… Maria asked to unmute, yes, okay. I'm working on Christine, too.

At the moment, I'm just a little…

Maria E. Donawa, MD: Go to Christine. Oh, I put mine in the chat. It has to do with, pie in the sky. Maria E. Donawa, MD: Kind of thing.

Sorry, I'm not seeing the chat. Do you want to read it out to me? I can't…

Maria E. Donawa, MD: I was just trying to.

during the time You can't save time.

Maria E. Donawa, MD: I was just trying to find it. It was basically, given the costs of… I think I said I was imagining that someday in the future. Maria E. Donawa, MD: Before doing a clinical study, you would do some kind of an in silico study, because you could get so much additional, different kind of information from that. Maria E. Donawa, MD: But then I said, well, how… then the question was, well, how expensive is it, to… to actually, set up and validate Maria E. Donawa, MD: and validation, for me, is like, you know, how do you know that the data are actually valid? So the validation of the system has to be a critical issue, and since I know zero about

Maria E. Donawa, MD: how to actually do this. It was just occurring to me that it could be… I mean, first it was like, no. And then it was like, well, wait a minute, maybe, universally, yes, before you actually go into a very expensive, risky clinical study.

Yeah, I mean, you know, I think the validations, you know, it's 50-50, it's an absolute key part of this, is that 50% of the whole basis for it, which is, you know. Yes, you will need to be able to understand what your known endpoints are, and, you know, apart from the innovation areas, there's a lot of world data out there. But, you know, if you look at your device development, your device prototyping, couldn't you not, you know, use in silico to go, right. This isn't quite working. Could we perhaps change it, run it through the simulation again, and then see how it works? And I think, you know, you look at design engineers, you look at the aerospace area, you know, they're doing this day in, day out, but again, healthcare seems to be on the back foot.

So I think you're absolutely right, you know, that you can talk about your initial stages, which is your physiology and your biomechanics, your anatomy, and there's some great papers on this area. I've read some great research when it comes to computer modeling. But then, you know, you look at your prototyping, which is, right, this is how a device is going to work, let's run it through the scenario. If we need to tweak it, can we tweak it before it goes to a formal clinical trial? And then you do the clinical trial with real-world clinical data, run it alongside your in-silical clinical trials. There are multiple points, and this is what I was saying to you before when we talked about the life cycle.

There are multiple points where Instiloco can help help the manufacturer, help the clinician, help the healthcare institute. You know, I'm not an exponent of completely removing all of this, but I do believe that there is a huge amount to be gained from running it alongside.

Maria E. Donawa, MD: Okay, sorry about this, and I promise… quick, quick, quick. I just found my question. If you're starting from scratch, how difficult and costly is it to conduct an in-silico study? If you don't want to go there, fine.

Yeah, no, I couldn't tell you about the actual cost, because unfortunately that's not my area, but, you know, as with the other lady, I was asking a very similar question, you know. This is obviously a significant issue for small and medium-sized enterprises, where… or if you've got an orphan device, and you've got low manufacturing production, therefore low revenue ROI in the future, is, right, well, think about… a bit like consultancy unit. Think about the institutions that are actually dedicated to this work. Can you work alongside them? Who really have that infrastructure, who really have the SOPs? Can… can we work alongside them?

In order to develop the device that we want to. And I think that would be a good starting point. I do not have a solid answer in terms of cost, because as I say, that's not my area.

Maria E. Donawa, MD: Understood. Thank you.

Hang.

Gonna cost money. Christine, then Steph.

All right. Yeah, so my question was around, usability. So how does this in silica testing concept fit in with the requirements for usability data?

So, again, the usability data… depending on… if you're… so if I… when I look at usability, I'm looking at the clinical usability data, which is how well does that device handle in the operating theater, how well do the surgeons work with that? And at the moment, that is still going to be how well do, you know, if you survey your… the last 10 surgeons for use your device, how do they find it? Because that is really, really important information. So it's not really going to be there to… to… to replace that usability, you know, your survey information. What it's there to do is try to help augment the clinical data to support your device actually being used. You know, if you, you know, I have this all the time, and people come to manufacturers, or, you know.

Manufacturers will come to me and say, right, Suresh, we've got this new neural navigation system. You know, and I've done virtual reality and abdominal augmented reality in neurosurgery, but I'd say to them, right, well, what's the evidence? You know, what's the publications on this? What were the trials on this? You know, who else has used this? But, you know, if you have a high-risk group with small numbers, you can then say, well, actually, we've got this, but we've also got computational data which helps support that. And then it's about, you know, making sure that clinicians and patients understand that. It's for buy-in, and then them taking it forward to actually use it. But the usability in terms of the clinician usability data.

from my own personal feelings, and I have a tremendous amount of bias in this. I still think usability clinical data still needs to be… how did the… how did that surgeon find using that device in that context?

Yes, 100% agree.

I would echo that and say that, the user, who is not always a clinician, is where the things can really go sideways, and that's, that kind of research is more about discovery than about, Proving a point.

I was certainly thinking more about the clinical context of usability, for sure. My second question earlier was, sort of. maybe it's just a… maybe this is an obvious answer, but, you know, is the trend towards this in silica testing model also about challenges with patient enrollment? Because certainly in the U.S, it's not just about cost. It's actual enrollment problems. You know, you can't get people to enroll in trials, you know? Obviously, during the pandemic, we went to these more virtual trials where they could and things, but even so, you know, we're having difficulty with enrollment.

I absolutely agree, you know, especially if you're looking at small populations, high-risk populations, how do you get those patients involved? You know, what is ultimately the priority for the patient? The patient wants to get well. Do they want to be spending their time getting enrolled in Multiple other trials, perhaps not. So I completely agree, and I do, you know, when you… when we… certainly as a regular, when you look at sufficiency of clinical data and what people are putting through, it is reflected in the numbers, and how do you therefore enhance the numbers? How do you therefore support those numbers? And there has to be a different way of, perhaps, doing that, and this is where… this is the potential. I'm not saying this is how necessarily people, this is the potential one, and the typical clinical trial could do.

aspirational. I can't hear anything at the moment.

Okay, yeah, can you hear me?

Oh yeah, sorry, it's definitely higher.

Okay, hi, it's Stephanie. With, this in-silico, testing that you can do. I'm assuming that's now a big topic, because we actually have the computational power to do it. But I guess my main question is, I'm assuming that this type of testing could actually eliminate all the animal testing, which is sometimes required, right? I mean.

Yeah.

carrying on from what Maria was actually asking you before, also, in terms of biocomp studies using animals, I'm assuming that, you know, you could do…

You know, why are we, in 2026, still doing animal testing? You know, I understand there are difficulties in, you know, doing prototyping in humans, but surely, if there's available alternatives, is it… Is it morally? Is it ethically right to still be doing animal modeling? And I don't believe it is. I think we have alternatives. And we need to pursue those with full vigor.

But I guess one of the key factors is, and maybe you've already addressed this, apologies if I missed it, is that the source data is really, really important, that it's, you know, as close to the reality as possible, right?

This is…

It's sort of like garbage in and garbage out.

This is exactly the… yeah, no, this is… no, you're absolutely right, but this is for the problem of animal testing, so… If you've got testing on pigs, if you've got testing on primates, if you've got tests on rodents, you've got massive interspecies differences. You've then got to somehow convince that if you're taking a pig valve, or… I mean, I know they say it's simple, but let's, you know, I'm aware of testing in primates, but if you then, you know, there was a test particularly on looking at cerebral nerve stimulation, cerebral lobe stimulation. Is then, how do you then take that, translate that into the human world?

Because it's difficult. You know, you do not have the same anatomy, you do not have the same physiology. Accessing into somebody's brain in a primate, you know, I've looked at my dog's brain MRI scan, and it's jolly different to my MRI scan. You know, it is not the same thing at all. So there are significant Translational problems in animal testing. Which, if you can do a simulation, you know, you can do physiology simulations on humans, you can do biomechanics, anatomy simulations on humans, so therefore, you've got endpoints that you can use in terms of your pre-market, your prototyping simulations.

It's not unreasonable, it's not unfathomable, but, you know, animal testing Morally, ethically significant. But there are very significant challenges in taking that data into the human populations.

there were… I believe there was… I mean, there's one researcher that really showed multiple examples where animal testing actually didn't give the results that reflected human beings, so I really welcome, actually, this, you know, that we're going in this route and in this direction, because I think It's a fair comparison, and it's a lot better for a client. trials. Thank you.

So, thank you, Stephanie. Thank you, everyone, for your time today. A special thanks to, Dr. Sirash for joining us today. I don't know Where else on the internet you get the opportunity to join in a discussion like this and participate. If you know of someplace, please let me know. I'd love… I'd love to… I'd love to see how they do it. This is a work in progress. We're building a community here. The more you contribute, the better the community gets. So I encourage all of you to, participate if the mood strikes you, and that, I look forward to… we have a bunch of new stuff coming up. And, please, please take a look at the, MLV100 link that I put in, along with the new program, that we're launching, to help people like you, experts like you, be heard. So, thank you again,

For your time, everyone, and Suresh, closing thoughts?

It's a brave new world, but like anything, the journey has to start somewhere.

Thank you. Goodbye, everyone. Until next week. Thank you.

Take care, bye-bye.