Good afternoon, everybody. Vivoryon Management welcomes you to the Virtual Kidney Disease KOL Event, where we will discuss novel pathways to address the unmet medical need in diabetic kidney disease and orphan kidney diseases. I would like to remind you that during this conference call, we will present and discuss certain forward-looking statements concerning the development of Vivoryon's core platform, the progress of its current research and development programs, and the initiation of additional programs, as well as results of operations, cash needs, financial conditions, liquidity prospects, future transactions, and strategies. Should actual results differ from the company's assumptions, ensuing actions may differ from those anticipated. You are therefore cautioned not to place undue reliance on such forward-looking statements which speak only as of the date hereof. With us. Next slide, please.
With us today, as speakers, Tobias Huber, who is the chairman and director of the Third Clinic Department of Medicine of University Medical Center Hamburg-Eppendorf. Florian Jehle, who is a seasoned pharmaceutical industry expert. And Kevin Carroll, the CEO of KJC Statistics and a well-experienced statistician in kidney disorder development. Next chart. I'm Frank Weber. I'm the CEO of Vivoryon Therapeutics. We are hosting this event today because of major, major three reasons. One is we have Varoglutamstat, which is a molecule which inhibits glutaminyl cyclase, and through this novel mechanism of action, we reduce inflammation and fibrosis in kidney disorders. Secondly, we have obtained clinical data in VIVIAD, which are extremely promising to improve kidney outcomes. Thirdly, we have results from preclinical animal models, which result in consistent outcomes with the mechanism of action and with the clinical results of VIVIAD.
Having looked through those, we thought it is a good point in time to invite external advisors to discuss the current data situation and how we should move forward from today. With this, I want to introduce Tobias Huber. Tobias is the chairman of the Third Clinic in Hamburg-Eppendorf, an internal medicine and nephrology specialist, who has worked intensively on signaling processes of the kidney on the molecule level. He was also involved and published COVID-19 effects on kidney failure, and I would say he is one of the leading nephrologists in the world. So I pass to Tobias for his presentation. Thank you.
Yeah. Thank you, Frank, and welcome, everybody. I'm happy to entertain a few thoughts with you. Next slide, please. I would like to disclose that my life is dedicated to improve patient outcomes of kidney disease patients, and as such, I'm advising multiple different companies at early stage in preclinical stages, and I'm usually basing my decision to help these or support or advise these companies based on the excitement of their discovery pipelines. And you can see all the different companies in pharmaceutical industry which I'm advising. I also holding patents myself, and I'm currently acting as the President of the International Society of Glomerular Disease, a nonprofit organization. Next slide. Now, as some of you might be aware, kidney diseases are a silent epidemic of our societies.
They affect almost, in some countries, even more than 10% of the population. What is very worrisome is that kidney diseases are being predicted to further rise as a major cause for mortality and morbidity. As of 2040, kidney diseases are expected to be among the key factors for shortening lifespan. Next slide, please. Now, from a perspective of nephrology, of a nephrologist and clinician scientist, the reason why such a large proportion of our population is being affected by kidney disease is that kidneys are being affected by fairly common factors such as diabetes, hypertension, aging itself, obesity, cardiovascular disease, and there are no very specific biomarkers or early indicators. What we usually observe is the glomerular filtration rate estimated from creatinine values, as a measure to observe a decline of renal function.
Most commonly, this happens silent because the kidneys are usually not sending any signals or pain signals. Now, the impact that I was just alluding to is dramatic. It's really impacting premature mortality. Many of these kidney diseases are progressing to end-stage renal disease, requiring dialysis or transplantation. Kidney diseases altogether account for more years lived with disabilities than all cancer diseases together, and they usually go along with a significant reduced quality of life and also impacting and inflicting psychological factors. Next slide. Now, we witnessed progress in the field of nephrology. 30 years ago, it was the advent of angiotensin receptor blockers or ACE inhibitors that obviously slowed kidney disease progression beyond lowering blood pressure. Now, in the last decade, a few more medications came on the market, truly benefiting kidney diseases.
These were mineralocorticoid receptor antagonists, SGLT2 inhibitors, and most recently, by the FLOW trial being published this year, actually, GLP-1 receptor agonist. Now, this was truly exciting news for the field of nephrology, and all of these medications slowed down kidney disease progression. And interestingly, most of them start with an initial dip of kidney function, and then at the later stages, reduce the rate of progression. However, as you can see on these graphs, on these four different columns of therapy, is that none of them is holding or reversing kidney disease progression. So we're always talking on slowing the rate of progression, but not on halting or reversing.
And currently, we're of course trying to combine these different kinds of treatments, and the outcome is still open, whether this will further slow the progression, but I guess hopes that it will halt or reverse are limited. Next slide. Now, from a scientific perspective, the next and maybe overlooked field of unmet need are rare kidney diseases, and this is very nicely showcased by the U.K. Biobank and very recently published RADAR study in Lancet. So looking at the U.K., relatively diverse U.K. population of 2.8 million people. And what could be observed here is that rare kidney diseases, which are 5-8% of all kidney diseases, account together for more than 30% of end-stage renal disease and kidney failure.
Meaning that a few rare kidney diseases together make up a huge unmet field in terms of impacting terminal kidney failure in a population. And you can witness on the right graph to the right side. This is illustrating the decline of GFR, that this decline is rather steep in many of these rare diseases, which could be immune-mediated or genetically mediated. Next slide, please. Now, from my perspective, in a relatively simplified or maybe even oversimplified view, this highlights the two largest unmet areas in kidney disease and kidney disease progression. First, we are in need of drugs that will halt or reverse the decline of kidney function, particularly in an aging population, and particularly in more advanced kidney diseases. And secondly, we need more drugs and precision drugs arming us and facing rare kidney diseases.
Now, jointly together in rare kidney diseases and the most common forms of chronic kidney diseases is that many of these diseases are being driven by inflammatory pathways, be it as initiators or being as progression factors. Once epithelial cells are being damaged and sending inflammatory signals, the progression, even regardless of the underlying cause, usually is being underlined by inflammatory pathways. The next slide, please. Now, this is basically, again, showcasing what I was just saying in the case of diabetic nephropathy, which is a metabolic disorder, but this metabolic disorder leads to epithelial cell stress, and this epithelial cell stress leads to a secretion of pro-inflammatory factors, and these pro-inflammatory factors are then driving, are drivers of a chronic kidney disease progression of interstitial, fibrosis. Next slide, please.
Now, for that reason that I was being approached by Vivoryon, I thought this might be interesting to have a small molecule inhibitor inhibiting glutaminyl cyclases that have been known also in the field of kidney and have been upstream regulators of pro-inflammatory cytokines, such as CCL2, which have been long known to be drivers of kidney tubulointerstitial inflammation and fibrosis. And me and my colleagues in the translational scientific field have been looking for several years for such upstream regulators, including epigenetic regulation and regulators and other factors, and including also glutaminyl cyclase inhibitors. And with that we can go to the next slide. Now, this is now published work which used such inhibitors on a rather aggressive inflammatory kidney animal model, a rat model.
At the time, this, it could be shown that, it inhibits basically or reduces glomerulosclerosis, it reduces tubular interstitial damage scores, and this is being associated with the reduction of pro-inflammatory cytokines such as, CCL2, and also with the reduction of inflammatory markers like, and injury markers like KIM-1 for the tubular compartment or urinary protein excretion for the, glomerular compartment. Next slide. Now then, when Vivoryon approached me, we discussed also of including more preclinical models to get a bit more robust understanding and evidence from a preclinical side. At the time, we then discussed the adenine-induced mouse model, which is a model mainly affecting the tubular interstitial compartment, leading to progressive fibrosis and eventually end-stage renal disease.
Now, when using this model again, with glutaminyl cyclase inhibitors, it could be shown that it led to an improved kidney function in the first graph showing by increased GFR. It could be shown that there is less extracellular matrix production in the middle graph, showing by less collagen deposition, and also further kidney functional markers showed an improvement. Next slide. Now, again, these were data that I first saw when I recently was asked to advise Vivoryon, data coming out of a trial which was not aimed towards kidney diseases, but neurodegenerative disease and Alzheimer. But as an unexpected effect, of the Vivoryon team recognized an increase in GFR.
So we looked together at this data, and I found it at the time remarkable that we observed in this elderly cohort an increase of GFR, something that we rarely see, which sometimes can be associated with hemodynamic effects, such as increased blood pressure or going along with increased proteinuria, things that we didn't observe on this data. And then we ask ourselves, what happens if we look into more kidney patients in sub-cohorts that are at higher risk of kidney disease? And this was a diabetes cohort. And you can see that the numbers are rather small, placebo 12 and the treatment group, 20. And here, these effects of an increased kidney function were even more pronounced. Next slide, please.
Now, with these data at hand, we reasoned that it would be interesting also to look at inflammatory markers, and it could be seen that the CCL2, similar to the preclinical models, was decreased in the treatment groups, in the non-diabetic, as well as in the small diabetic subgroup. Next slide. Now, from my perspective, seeing the unmet needs of patient and seeing nephrology from the daily clinical practice towards basic research and international basic research, and also translation. I can say that we generally need the expanded portfolios of compounds, particularly aiming on different stages of kidney diseases, particularly compounds for advanced kidney diseases that not just slow progression, but halt and reverse kidney disease.
Of course, the clinical effect size will be key, and particularly in the sense of stabilization or even reversing kidney function or increasing GFR, which we rarely see. Then, of course, in kidney diseases, which are slow and chronic progressing diseases, everything is being based and aimed at long-term efficacy. And while we need to apply these drugs for a longer time, they need to be extremely well tolerated. And from these very first initial results that we saw, with our glutaminyl cyclase at the preclinical, but also in the small clinical cohort, I would rate these results as rather promising, in terms of these types and columns of treatments that we need to have. Next slide, please.
Now, we also should disclose that we started a joint research project with my university and Vivoryon, and this was a research proposal that we academically could suggest. First of all, we feel that it's important to better understand the distribution of glutaminyl cyclase in human diseases, across human diseases. We have large human cohorts, so right now we are deeply looking into the distribution of the expression pattern, number one. And we are also very interested whether the expression of glutaminyl cyclases is being dependent on kidney diseases and whether this might be up or down-regulated, for example, in diseases like diabetic kidney disease. Secondly-...
Coming to kind of a rare kidney disease aspect, from our own results, we saw an overlap of downstream signals to a glutaminyl cyclase activity and a lysosomal storage disease, Fabry disease, which shares many features of Alzheimer's disease. Particularly, we see an accumulation of alpha-synuclein. So for that reason, and this disease, while there is enzyme replacement therapy available, still our patients are progressing to end-stage renal disease, and we are lacking efficient treatments. For that reason, we are now looking whether Varoglutamstat might inhibit and slow down this rare disease, this genetic rare disease, based on our observations in the preclinical models previously and the potential overlap in the pathways. And we are doing the same with kidneys, which is AM three kidneys in the dish based on also accumulation of injury markers. Next slide.
Now, again, talking from the perspective of our patients, we really would like to see forthcoming new treatments and options and opportunities for our patients, not just slowing, but holding and reversing disease. Of course, this needs to be based on double-blind, placebo-controlled studies. I think what we need is particularly drugs that aim for more advanced kidney diseases, hoping that in this population, we could prevent this population from ever reaching the stages of dialysis. Of course, such a trial need to be done on top of standard of care, and I mentioned all the new drugs that have been entering the field, and that, of course, put the bar higher of what a new drug needs to do, to not just slow, but eventually even halt progression.
If you would like or we suggested, a suggestion comes actually from Vivoryon, a subgroup with non-alcoholic fatty liver disease. We need robust endpoints, and I myself traveling to the FDA next week to discuss such endpoints, again, from an academic perspective in rare kidney diseases, but we need hard endpoints, GFR, kidney survival, and a surrogate, eventually proteinuria. I would like personally to learn more about the biomarkers and the mechanisms of action from this new compound, and also would like to see this included in the clinical trial that we can academically gain more knowledge, and of course, as always, it needs to be sufficiently powered.
With that, thank you very much for attending, and I'm really happy to take later questions and to respond or to being engaged in further discussions. Thank you very much.
Thank you to Tobias for this very comprehensive and eloquent presentation. We have opportunities after all three presenters have spoken to ask questions to Tobias and to other presenters. With that, we come to the other, Florian Jehle. Florian Jehle is a many decades expert in pharmaceutical industry, has worked there intensively with a focus on kidney disease, and without further ado, I hand over to Florian.
Thank you very much, Frank, for the introduction. Good morning. Good afternoon, everyone. My name is Florian Jehle. I'm an advisor to Vivoryon, and if you go to the next page, please, I would like to disclose that I'm the CEO of Vifor FMC Renal Pharma, a pharma company with a focus on therapeutics usually prescribed in the nephrologist's office. Prior to VFMC RP, I was in various senior roles with Fresenius Medical Care, among them Unicyte, CEO of Unicyte AG, and Fresenius Medical Care Ventures. And before FMC, I was a management consultant and partner at Catenion, a boutique consulting firm focusing on the pharmaceutical industry as well. I would like to highlight that the views and the opinions expressed here today in my presentation are my personal views.
They are not those of my current or my previous employers, and also not necessarily of Vivoryon. So let me say a few words about the market perspectives of CKD and opportunities for Varoglutamstat from a market's view. As Tobias Huber has already pointed out, chronic kidney disease is a significant global health issue today. The prevalence of the disease ranges around 36 million patients in the U.S. alone, and similar number of patients in the EU, and adds up to estimated 800 million patients globally across the different stages. So patients, chronic kidney disease patients progressively lose their kidney functions, and as a last resort, many of them suffer from an end-stage kidney failure, as the disease is largely non-reversible, as Tobias has already pointed out.
What makes the therapy for these patients challenging is the silent progression of the disease. So many patients become only aware and are diagnosed when CKD is already progressed to more severe stages. For those end-stage renal disease patients, kidney replacement therapy does exist in the form of transplantation and dialysis. It is different from many other organ failures that patients are suffering from. However, transplantation, both transplantation and dialysis, are complicated treatments and are often not everywhere available in the world, especially not in developing countries, where we see significant incidence rates of the disease today. But also in our regions, in the Western world, the availability of organ donations is very limited.
In the U.S. alone, there are more than a hundred thousand patients currently waiting for organs across the various different organs, but many of them will die before they receive a transplantation. So go to the next slide, please. As you certainly know, the level of chronic kidney disease is generally determined by two different tests. There is the glomerular filtration rate, GFR, that Tobias has already mentioned as a necessary endpoint for clinical trials. It is a blood test that is measuring the flow rate of filtered fluid going through the kidney. So what you see in that chart is the lower the filtration rate, the lower the functionality of the kidney, and the more advanced is the disease. A second parameter is the urine test, measuring the albumin-to-creatinine ratio, that is protein levels or measured protein levels.
The higher the protein levels that you find in the urine, also, the more advanced is the disease. Now, you see the patient numbers next to those different stages. Most patients actually in CKD stay on these various levels of chronic kidney disease for some time, and most patients actually die before their kidneys fail completely for various other reasons. However, some of them progress. A lot of them actually still progress to late stage, and especially for the fast-progressing patients and these late-stage patients, they are at very high risk of requiring a kidney replacement therapy at the end of their life. In the U.S., we are talking about more than eight hundred thousand patients that are currently on kidney replacement therapy, both on dialysis as well as on transplantation, and actually, the number is still growing as we speak. Next slide.
Along with the growing number of patients, the financial burden to the healthcare system is also increasing. What you see on this chart is the enormous cost of CKD patients, especially at later stage of the disease. While in earlier stages, the mean cost per patient in the U.S., this is an example from the U.S., range still below $20,000 per year, but they easily escalate and increase by a factor of five and more when patients reach end-stage renal diseases and have to go through dialysis, hemodialysis, and peritoneal dialysis and transplantation programs. As a result, less than 1% of the Medicare beneficiaries in the U.S. suffering from kidney failure cause more than 6% of Medicare spent in total. So we're talking about more than $50 billion.
Private funding comes on top, as most countries are using a mix of public and private funding for financing, the cost of chronic kidney disease. And on the right-hand side of the chart, you see, two examples for the U.S. and similarly also for Europe, the high cost of end-stage renal disease. You remember, eight hundred thousand patients causing the cost of this end-stage renal disease bucket versus 35 million patients, a lot of them diagnosed, but just in relation, 35 million, the lighter blue cost of healthcare - overall healthcare burden in the U.S. driven by chronic kidney disease. So what is driving the disease? Next slide.
When you now look at those major drivers or risk factors that Tobias has already highlighted for developing chronic kidney disease, so you see that this is not only a prevalence or from a cost burden perspective, a static or a stable situation, it actually gets worse in the next decade globally. Two-thirds of the end-stage renal disease patients suffer from diabetes and/or high blood pressure as major causes of their kidney failure. WHO estimates today around 800 million CKD patients around the globe, a number that will further increase. If you look at the increased rates of aging population, hypertension, and diabetes over the next decade, they are enormous, and as a result, also, we expect significant increase of the chronic kidney disease patient population.
Now, you could argue as Tobias Huber also mentioned on the next slide, please, that some exciting new treatment options have made it to the market over the last decade. While they indeed provide additional treatment options for patients they only delay disease progression, and there are studies that expect even more patients in later stages of chronic kidney disease because these novel therapeutics have also positive effects on the cardiovascular side, so they improve or increase the number of survivors who would have died from cardiovascular events, and those patients continue to decline in their kidney functionality and adds to the patient population on the kidney side, so indeed, more patients survive potentially with progressing kidney disease in the future.
As we have already said, what we need are indeed drugs that address a significant unmet medical need by stabilizing or improving, hopefully improving the kidney function in the future. Let's have a look at the pipeline and what happens in the pipeline today. In the current clinical development pipelines, there seems to be indeed emerging and growing activity in the field of rare kidney disease, and people are trying to innovate in the field. For the diabetic kidney disease patients, there seems to be only one therapy candidate at the moment that has shown in a phase II clinical trial, stabilization of eGFR, that is, ProKidney's stem cell replacement therapy, which comes, as we know, with its own set of challenges as a stem cell therapy.
For rare diseases, the development seems to focus on IgAN and FSGS, where we see the highest number of candidates in the clinical pipeline. For other indications or patient subsegments, such as for Alport or Fabry and other segments, significantly less candidates are currently in clinical stages. Thus, even with these increasing activities in the field of clinical development and research in chronic kidney disease, the unmet need remains high, and that is despite the high reimbursement rates that could be expected to drive actually these R&D activities in the field. Now let's have a look at Varoglutamstat and how it could fit from a market's perspective into this environment.
Varoglutamstat could be a very strong addition to the treatment landscape for kidney disease patients if Vivoryon can successfully demonstrate, with a new mechanism of action, the stabilization or even the improvement of the kidney function in stages three, four, and 3 B, as Tobias has highlighted. There is still a significant market potential. The other treatment options that are available start, in most cases, much earlier, once patients hopefully are diagnosed, identified, and diagnosed. But it still leaves a very attractive room for Varoglutamstat, especially in these later stages, 3 B, four, with still very limited competition in the field. The route of administration as an oral product will certainly help to get acceptance by patients, especially in this segment 3 B, four, and is a clear advantage over, for example, biopsy-based stem cell therapies, as just discussed.
For such a small molecule drug, with this positioning in stage four and fast-progressing stage three patients, at least from a market's perspective, I could see a significant market potentially going forward. And also, on the next slide, please. From a big pharma's perspective, there is increasing interest in strong assets in the kidney field, and this interest is continuously increasing, as we have just seen with most recent deals by Novartis or Vertex, also Novo Nordisk, who have shown that they are willing to and able to pay significant deal premiums for biotech companies developing assets in that field.
Just to highlight the Novartis acquisition of Chinook for $3.5 billion based on a lead asset in phase II, and similar Vertex acquisition of Alpine at $4.9 billion, also for an asset, a lead asset in a phase II, show and demonstrate exactly this interest of the industry. So those are not the only examples for premiums that are paid for later-stage assets in the market, but they highlight this interest of the big pharma companies. So in summary, I would argue that the prevalence of kidney disease and disease, the disease burden, are increasing globally, and this still requires innovative treatment solutions to come to be developed and to come to the market.
The existing therapy options only delay disease progression, and unmet needs still remains high, and that opens a very attractive opportunity for an oral Varoglutamstat in the described stages of chronic kidney disease with a significant market potential and also attractive partnering options with big pharma. With that, thank you very much. Very much looking forward to your questions. Handing back over to Frank.
Thank you, Florian, for your deep insights in the current and future markets of kidney disorders and, with that, I want to come to the last speakers of our event, and that is Kevin Carroll. Kevin is a very seasoned statistician and has been very, very frequently at the FDA to discuss successfully marketing authorization and scientific concepts and he is also, of course, responsible for delivering the statistics around our kidney program, as we rely on his expertise, so with no further words, I want to hand over to Kevin. Thank you.
Thanks, Frank. I hope you can hear me okay. Yeah, I'm very glad to be here today, and I just wanted to in this final presentation, just go back a little bit, to the data at hand. And in my relatively short presentation, there's three things I'd like to try and cover in summary. One of them is the methodology for the evaluation of eGFR and renal function in clinical trials, the contemporary methodology, how do we do it? What is the appropriate endpoint to rely upon? The second was just to make a passing comment on the strength of the data we've seen in VIVIAD, to try and help you see that data, I think maybe, in a different way.
And finally, just make some comments on the potential for a prospective proof of concept type trial to follow up on the results we've seen in the VIVIAD trial. So, without further ado, just a few comments on the analysis of eGFR in CKD trials. In the past, and by this I mean, in the eighties and nineties, there was usually a fairly straightforward analysis that would take place in clinical trials. You just look at the change in baseline from one time point to another, and that would constitute your change in eGFR at some particular point in time. Now, of course, eGFR is measured serially over time, and just looking at a single time point isn't necessarily the most insightful.
So in the early 2000s, with the advancement of some newer statistical software packages, and there was a shift toward using mixed models, where we have repeated measures in the subjects over time, and that's better because it uses all the data points a subject has over a period of time. And if you're interested in a chronic disease like CKD, then of course you want to be looking at what's happening to the patient over a period of time. So we began to see methodologies being used in the early 2000s that went beyond a simple change from baseline at some time point. And then I think more reason...
More recently, I guess, we began to see that type of repeated measures analysis evolve into something called a random coefficients analysis, which sounds pretty complex if you've not come across it before. But that is the main method used to analyze eGFR over time, this random coefficients analysis, and doesn't rely on data at a given time point, and it uses all the available data. From that analysis, you get a simple measure, which is the rate of change of eGFR over time. That's useful, certainly is used a lot. I mean, many of the nephrologists I've spoken to tend to use this measure, you know, to monitor the patient over time. How is their renal function faring? What is the rate of change over time?
That tends to be the primary method for evaluating eGFR in contemporary trials as compared to those in the past. There are some alternatives that can be considered, of course. You can, like, take an AUC of somebody's eGFR profile over time, or you might take the average of eGFR values over some period. You know, from one year to two years, what was that average eGFR? Those methods can be used, but on the whole, they're not as satisfactory as looking at the rate of decline. Can I go to the next slide, please? This is just a very simple illustration for those who are not familiar with this way of looking at eGFR. I should say that what I'm gonna say now isn't unique to eGFR.
You can use you can look at the rate of change of any variable over time, you know, lipid levels, A1C levels, blood pressure levels, any data that in a disease that's chronic and worsens with time. You can use this method for any any of those types. COPD, for example, is another good example over time. Here, what you see on the left and the right are just three patients on the left and three on the right. We have three on control or placebo, in this example, and on the left and on the right, we have three on drug. So what you see are these the profiles, the actual eGFR data points joined up for each subject.
So we have light blue, dark blue, and then a very pale blue, so you can see the three subjects. A similar situation on the right for drug. And what we do is we effectively put a regression line through each individual subject. So we end up in the table at the bottom with three values per subject, and you can see what they are. You can see the blue control and the red for drug. So we calculate the rate of decline for each subject using all of their data, and then we take an average of all the control patients and all of the drug patients, and then it's those averages that we compare to see if there's a difference, if the rate of decline is or is not improved with drug relative to control.
So that's effectively what a random coefficients analysis is, and it's, I think if you see it this way, it's maybe not as strange or confusing as it might seem. Can we go to the next slide, please? Okay. I mentioned this briefly already. This approach, this random coefficients and measuring the rate of change of eGFR over time, that you know, it does provide for you know, a thorough comparison of data over time because it's not using a single time point. So clearly it captures more information, and it should be more insightful.
And that kind of random coefficients, or maybe we can just call it slope analysis, might be an easier term to use, is generally preferred if the rate of decline of disease over time, or at least in the near term, is approximately linear. It's an obvious choice. Now, there are some instances you have to be a little careful if there's an acute hemodynamic effect, which causes a short-term rise in eGFR and then a subsequent decline. You have to be careful in those settings when you think about slope. That you can go a little more sophisticated in that setting, and you can have two slopes, like an initial slope for the hemodynamic effect, and then a chronic slope that looks at the slope of eGFR thereafter.
So you can just modify the approach to cope with a situation where there might be a short-term hemodynamic effect. But if it's really, if you're really in a situation where your data are grossly non-linear, then you can use an area under the curve or take an average of data points, say, for example, from one year to two years. But most of the time, and I'll show you on the next slide, in many trials, the rate. We don't go to the next one yet. Just go back. I'm gonna speak about it in the next slide. In many, in many situations, in most trials, we use the rate of decline. It's not very common to use some other metric for eGFR change over time. And there's a couple of little minor things to.
Could be important things to highlight is that over the years, I've been dealing with the FDA and EMA in these trials many times, and there was, I think, something of a misconception that if you compute rate of decline of eGFR, which is what physicians use essentially to look at, you know, how treatments are, how patients are doing and how effective treatments are, the FDA was a little concerned if you use that approach, you might increase the risk of a false positive finding. But that is actually untrue. You will not increase the risk of a false positive finding.
And I spent some time with some colleagues, describing in full, in peer-reviewed publications, why the use of the slope or random coefficients for eGFR is perfectly fine and does not at all affect the, you know, the risk of a false positive finding. It might have an impact on power, but it certainly won't lead to a situation where the regulatory authorities make a licensing decision, which is in some sense in error because they use the slope. I mean, that's just not true. So next slide. Okay, so, just very briefly, and we've heard this mentioned in the previous talk, that there's obviously a lot of activity at the moment in CKD. There's many phase III trials ongoing, particularly in IgAN and FSGS, but other areas as well.
I just list on here some of the, those companies who've been working in CKD that I'm sure you'll be aware of. Travere, for example, and their recent full approval in IgA nephropathy. We have, we've heard about Novartis and the purchase of Chinook and Novartis meeting the surrogate endpoint in their study. Alexion, Otsuka, Vera, there's a whole bunch of them. I should say I have to be careful, but I have, in my general consultancy work, I've had contact and discussion with all of the sponsors on this slide in terms of how to design trials, what should the right endpoint be, how to negotiate, you know, the pathway through FDA.
In general, certainly, it tends to be the rule that we're using rate of decline, this random coefficients, eGFR rate decline over time, as the primary assessment of eGFR. Indeed, the most recent approval for the Travere, you look at the FDA labeling, the analysis of eGFR that the labeling is based upon is a random coefficients analysis measuring the slope of eGFR over time. As I say, the other ongoing trials are all using similar methodology. I think, you know, there's one other thing to point out, is that over the time that I've spent with many sponsors in CKD dealing with cardiorenal, it's a division in FDA.
First thing to say is cardiorenal are absolutely excellent, and they have absolutely fantastic leadership, and they give extremely good scientific advice to sponsors, on the whole. And of course, anybody who is walking in through FDA with plans for a phase III trial, you know, you're going to have questions and queries and, you know, one needs to be prepared for, questions from FDA around: Do you have a short-term hemodynamic effect associated with your mechanism? That you should expect to have the FDA tell you that whatever pivotal trial you're doing, it needs to be well conducted, and it's critical that there's complete follow-up of subjects right throughout the duration of the trial to obtain eGFR.
Related to that, there'll be, you know, commonly there'll be questions from the agency about what you do if a patient doesn't make it to the end of the trial. They drop out for whatever reason, and how are you gonna deal with that information? Typically, you'll get these kinds of challenges and questions from FDA, whatever your setting is in CKD. They're all excellent points that the agency raises, and the sponsors have to provide full and adequate responses, you know, when those questions arise. They're common questions, but not difficult to deal with, but you do need to be prepared because they're definitely gonna come up. Yeah, next slide, please. Okay.
Just turning now, there were general comments about how you do analyses and, you know, some of the, you know, the way some of the sponsors right now are designing their trials and the eGFR endpoints they're using. Now, flipping back to the VIVIAD trial results, I'm not gonna go through them in detail, but I'm just gonna highlight some key points. Those data, those eGFR data, were extremely thoroughly analyzed. And here I only give some of the evaluations that were done. Looking at the data in the overall experimental versus placebo populations.
Looking at it in terms of dose, looking at the data in terms of risk group as defined on the slide, and also involving complete reanalysis of the blood creatinine, and also executing the analysis of these recomputed data. So we looked at it very thoroughly inside and out, and as you will know, there was and is a substantial treatment effect on eGFR when you look at drug versus placebo in the overall population, and that is actually driven by an even larger effect in subjects with diabetes. I'm going to, on the next slide, please, just going to try and illustrate this in a slightly different way in terms of the magnitude and strength of that result. Let me take a moment just to explain what this slide is, and then there's like a...
We're just gonna run through some slides after this, and it kind of works like an animation. But on this slide, what are you looking at? These peaks, we're looking at. So on the X-axis, it's the treatment effect, the difference between drug and placebo on eGFR. So numbers that are positive indicate an improved eGFR relative to placebo. So the X-axis is simply the treatment effect. The curves, which I've colored red, blue, and green, the green curve represents the eGFR data in all subjects. So you can think of that as a kind of histogram, where we're looking at the range of treatment effect observed in the overall population on drug relative to placebo. What was the range of treatment effect?
You can see by looking at the X-axis and looking at that green peak curve, that it runs from about four to eight millimeters of mercury. Sorry, mL per minute improvement over placebo, with an average improvement of around about. It's hard for me to see the slides. It's tough, but an average improvement, I would think it's about four or six, sorry, six mL per minute. So that peak is telling you the line in the middle is the average treatment effect, and the green curve is showing the uncertainty around it. What is our confidence in that result in the overall population? As you can see, that curve is peaked and narrow, so you have a large treatment effect, and it is a long way from zero.
So the consequence is the P value is extremely small. Now, right behind that, it's hard to see it, but right behind that is a blue curve, which are all the non-diabetic subjects. So it's the green minus the diabetic subjects equals blue. And you can see it's very similar to the green, slightly to the left of the green, so a slightly smaller treatment effect on average than the overall population. And you can see that, too, has a narrow range. The bell curve is a long way from zero, so that's a large treatment effect. And then in red, we see the data in patients who are diabetic. Of course, the sample size there is lower, so that's reflected in that curve being flatter and wider.
Yes, but you can see on average, that you can see the red vertical line on average shows a huge treatment effect of about nine mL per minute over placebo, which is huge treatment effect in CKD. Now, if we just go to the next slide, if that's okay. Can we go to just page to the next slide, please? I, I. Okay, that's, that's good. It's okay. It wasn't refreshing on my side, but I can see it now. What I'm doing as I move from slide to slide here is I'm just asking a simple question, which is: What is the likelihood of there being a certain size of effect? So on this particular slide, you can see I've selected a cutoff there of six.
So I'm asking myself, on this slide, what is the probability that the drug has at least a six mL per minute treatment effect over placebo? What is that? You can see from the box in the top right, that if we look at all subjects, the probability of having that size of treatment effect or more is, I think it's about 60%. Again, it's difficult for me to see the slides. And then if we look at the non-diabetic patients, the probability of a treatment effect of six mL over placebo is about 40%. So they're still substantial probabilities for what is a huge effect. A six mL effect is very large. But when we go to the diabetic subjects, that probability is extreme is very high.
It's 85% that you'll have a very large treatment effect. Now, normally, when we look at data, we quantify it with p-values. So the p-value here is the probability that your treatment effect is bigger than zero, and that is about 99.3% or more, whether you're diabetic or not. So the probability that your treatment effect is positive in this dataset is very high. But what I'm doing here is I'm actually saying, what if it's... what if, what is the probability it's not just better than placebo, the treatment effect is bigger than zero? What if it's six mL on average better than placebo, which is what this slide is showing.
Now, typically, you'd expect that probability to start falling off, but because the treatment effect is so strong in this dataset, we still see very high probabilities of treatment effects that are extremely large, and that probability is even higher in the diabetic only. So, if you just go to the next slide, please. And the next again. What needs to happen here... Just one second. Just for the purposes of so people can see it, if you quickly page back up, maybe to, I think, three or four slides, just page back up real quick. So we get to the first of these bell curve slides. Okay, and then just go to the first one, slide number 42, and now page down fairly rapidly. One, two, three. Just page down, so you go through them.
So you can see with that little animation, you can see how as we increase the size of the treatment effect, do we still have a high probability of a large treatment effect? And the answer is we do, and that probability is particularly high in those who are diabetic. So that takes a little bit of looking at those kind of slides. Hopefully that helps somehow to see the strength of the data and the size of the treatment effects we're dealing with, which in my experience, are, I mean, they are, they're very. It's very unusual to see treatment effects of this size. I don't think I've ever seen treatment effects of this size in any CKD study to date. It is very unusual. Okay, so let's just speed up now.
If we go to slide, the next slide. Okay, so having just highlighted that data that we have in hand, and the strength of that data, there's still, obviously, the company is still thinking about how can we execute a sensible POC trial, to corroborate these findings. And here, I just highlight really briefly, what we've been thinking about doing. So, on this slide, I just restate what the data is in the diabetic subpopulation. And then we've been thinking about, okay, given that size of treatment effect, what can we do? And it looks like we can do a relatively straightforward POC trial, taking about a year, in which we have follow-up of patients for at least a year.
We're looking at the AUC in the second half of that year, if you like, between 20 and 48 weeks. We're also thinking that we can do an interim in this trial after the last subject reaches 24 weeks follow-up. You might be thinking, how can we? Is that reasonable? Can we do a trial of that size as a POC? Well, we can, and if we go to the next slide, we can just quickly see how that would work. Again, a little complicated, this slide. We just look at the left-hand side, the yellow-colored side. When you look at this slide, the data are kind of grouped. The rows are in pairs.
So I'm gonna look at the first two rows, let's say 3.5 and 3.5, and what we're looking at there is, if we do a study, and we assume the true effect is 3.5, bearing in mind that 3.5 is a lot less than we already observed in VIVIAD. So you're already building in some level of conservatism straight away. Now, this is less than half of the effect that was seen in VIVIAD. So, but if we assume that modest level of effect, and we take our variability, and so on, from the data that we've seen in the previous trial, then we can construct a trial with about 80 patients per group.
If we observe a difference of about 2.3 mL over the period 20 weeks to 48 weeks, then we can say with confidence that we have an effect. We've seen an effect in the POC that reflects that corroborates that which we've observed already in the VIVIAD trial. On top of that, on the blue side shows you what you can do in terms of an interim. An interim can be done in the way that was described. What we would do is, at that interim, in simple terms, if you look in the first blue column, there's a number of 1.25, another one of 1.49.
If we did the interim, and the observed treatment effect was 1.25 or 1.49 or smaller, then that trial would be declared futile because there'd be no likelihood of being able to show a hypothesized effect on the left-hand side in yellow. So the 1.25 mL would represent the futility rule if we designed a study with eighty subjects. Now, that futility rule corresponds to something called conditional power of 5%, which is in the last column. That means that if we did this study, we did an interim, and we saw a difference of only 1.25 mL, there would only be a 5% chance of that trial then resulting in a positive outcome. So if there's only a 5% chance, you might decide that that's futile.
The next row down, you'll see it says 10% in the blue. That is... That, so the 1.49 in the next row says, if we did the trial, did an interim, and we saw a difference against placebo of 1.49 or less, then we'd only then the probability of a positive trial would be 10% at most, and that, again, could be a reason to trigger futility. So this table is, if you... The, the next rows are all the same, and what it's trying to do is describe, can we do a relatively small POC, and what would be, the success what would be a success? That's the, what's labeled critical value. That's how big the eGFR would have to be for the study to be a, a success at the end.
And in the blue, we could do an interim, and if the interim falls below, the observed effect that you can see in the first blue column, then we might decide not to continue. The study is futile. So we can do a POC with a futility assessment, given the data I showed you from VIVIAD, we can do that with around, well, anywhere between forty-eight to eighty subjects. Around about 60, I would think is reasonable, or forty-eight. And even in those instances, we're still hypothesizing a much smaller difference than we're seeing in VIVIAD. So that would provide an opportunity to execute a POC in a reasonable time, with a reasonable size, and we'd have a proper rules for assessing whether we had a positive outcome or whether we were futile. Very last slide for me is the next one.
I only talked about futility in that study. There could be questions and thoughts in such a trial as to whether at this interim, where we check if we are futile or not, maybe we could also check if there was already efficacy, because we might expect it, given the results in VIVIAD. You might see a big result sooner, and of course, it's perfectly possible to engineer in to any POC at the same time as assessing for futility, you can assess for, you know, strong efficacy. That can be done, it just has a few extra, you know, considerations. You'd be a little bit careful about that. It's effectively an alpha test. Consequently, that may have an impact on...
You might have to adjust the alpha to a certain extent, and you have to be careful about the public release of interim data, 'cause it can impact the ongoing study, but it certainly could be done. It just would require some additional thinking about the best way that an efficacy analysis, interim analysis, would be introduced, and how to do that in the best way without really impacting the ongoing study, but that's something certainly that we can think about, so that's my last slide. I hope that was helpful, and in summary, the eGFR data from the VIVIAD study are statistically very strong, and even though the subject group is small, the diabetic subgroup shows a strong result.
Those data analyzed rigorously using the most contemporary methods possible, and we even reanalyzed the data themselves, and then executed statistical analyses and still had very strong results. And that all allows us to do a POC study of reasonable size with appropriate risk mitigation built in. So with that, I'll bring my presentation to a close, and I'd be happy to take any questions, you know, in the Q&A session. Thank you.
Wonderful, Kevin. That was a very important presentation to make complex statistical analysis very clear and understandable, and also it provides a good background how evidence is generated and then discussed in the regulatory and scientific context. With that, I wanna go to the Q&A section, and the first question goes to Tobias, and probably commenting how important inflammation and fibrosis is in the progression of kidney disorders, DKD and orphans. How much is inflammation and fibrosis driving or being a bystander, and where is it observed?
Yeah, thank you very much for this question. So what we know is that every progressive kidney disease is being characterized by fibrosis and the loss of function in nephrons. So fibrosis is replacing functioning nephrons. Now, what happens in the steps a bit in between, and this is something which we are understanding better and better, and it is driven by often driven by injured epithelial cells, sending pro-inflammatory cytokines, stimulating fibroblasts in the interstitium, leading to progressive fibrosis. And this seems to be more or less uniformly being part of most of the kidney diseases, even genetic diseases like ADPKD, or cystic kidney disease, where we now also evidence cytokines like CCL2 and others driving this organ fibrosis.
I like your question in the sense that of course, in some of the diseases, this is even the initial driver, like in acute interstitial nephritis, where we have an inflammation or in some forms even of transplant, maybe fibrosis. So it might even start in the tubular interstitial space, and in other, it's rather a response to epithelial injury or, for example, to proteinuria, right? If we talk now about the glomerular disease and podocyto, we know that the proteinuria itself inflicts massive stress on the tubular compartment, and then the tubular compartment sends pro-inflammatory signaling, signals to the interstitium, leading to fibrosis. I hope this answers the question.
Very nicely. Now, there is another question in terms of how much do patients progress in kidney disease in terms of annual decline when they're in stage three, four? What is the range of progression you usually see annually?
Yeah, again, a good question, and here it really depends on the underlying disease. You know, chronic CKD is just a classification or a classifier for loss of kidney function, and it can be due to so many different kidney diseases. So we do have populations that remain even stable in CKD three for twenty, forty, twenty or thirty years, right? So I think this is why it's so important to include in trials, regardless what targets, to really subclassify regarding on the kidney risk factors and regarding on the GFR decline. Now, for diabetic nephropathy, we commonly see an annual decline between 4 mL to 8 mL , right?
So then you can say if you have 50 mL left or 40 left, and it takes five years, you know, you can even estimate how long it takes to get to dialysis. But it's really or we have rapid inflammatory diseases. This cannot be answered just generally. It really needs to look on the different disease origins, disease causes. In the different disease causes, we have patients sloping down on different levels. For that reason, I think it's important to stratify based on the previous behavior of kidney decline in these individual patients, and then to differentiate in fast progressors, medium progressors, slow progressors or no progressors.
Okay. Thank you. Next question goes to Florian, and maybe you can comment on the positioning of the drug in the various disease stages. So where is the highest medical need, 3 B, 4? How important is early treatment? Where would you put the current standards of care, and where you put an innovative drug like Varoglutamstat?
Yeah, thank you for the question, Frank. As we have mentioned before, from a mechanism of action perspective, and as Tobias mentioned, positioning this drug in these later stages of stage 4, 3B, fast-progressing 3B patients, could be a very interesting from a markets perspective and from a commercialization perspective, a very attractive field to position it rightly there. Why? The major reason is that there is a significant unmet need, particularly in these later stages, not in end-stage renal disease, but prior to end-stage renal disease, 'cause not that many agents are actually actively stabilizing or, well, delaying the disease progression at that stages anymore. SGLT2s, GLP-1s, ACE, ARBs, whatever has been mentioned, start in much earlier phases.
Yes, they seem to have, when taken over time, also to, into later stages of the patient's kidney functionality, there seems to be an effect. But if you can really demonstrate in these later stages that you do stabilize that, then you potentially avoid the patients or prevent patients from ending up in end-stage renal disease. This could be a significant market potential.
The second argument that speaks in favor of that segment is, if you think about the pricing levels and the reimbursement levels, if you are in that late stage, and you prevent patients from now going on dialysis, going on transplant by a year, well, you can imagine the cost savings that you bring to the healthcare system overall, and that still allows you to go for an attractive pricing level that is different from, you know, the antidiabetics, the antihypertensives that you usually use in earlier stages of the disease, so going into these 3 B, four patients could be a very attractive segment. Limited competition, attractive reimbursement rates, providing a significant benefit to the patients, but also to the healthcare system overall. Does that answer your question?
Yeah. And mine is very well, but this was a question of a participant, I think is also answered very well. Thank you, Florian. The next question goes or two questions go to Kevin. There is one question regarding: what do we do when the interim is not futile? That is the first question. So what is then the plan B? What is happening if the drug is better than the 1.7 mL or 1.8 mL in the futility?
The current design, as Kevin, thanks for the question. The current design, it would, the study wouldn't stop for futility. It would continue to its planned sample size, and in order to provide the final analysis in the, say, some 50 or 60 subjects, at which time we'd quantify the treatment effect. So if it passes futility, then the study continues to its planned goal. So that's how it's currently set up. But as I said, it's always possible to introduce an efficacy stopping rule or even a sample size re-estimation. And these things can always be added if, you know, with careful consideration. But right now, if you pass the futility hurdle, you just continue to the planned sample size.
And the next question to you was, from your experience with the FDA and EMA interactions, what would be a good effect size for improving or changing eGFR slope? What is meaningful from your point of view?
Yeah. So I, I'd be careful. I'm not a physician, so I'm not gonna talk about what's clinically meaningful. I'll leave that for others who are properly qualified. But in terms of what I've observed, in terms of, you know, the kinds of dialogue that happen with FDA, the EMA and others in relation to this, the magnitude of effect that might be considered, you know, to be meaningful in the context of a, of, of providing an approval and labeling. So it depends on, it depends on the disease. There's absolutely no doubt. So in IgA nephropathy, typically, you know, trials are designed and agreed with FDA in the region of a 2 mL annualized 2 mL per year improvement over placebo. So that's on an annual basis, so every, every year, there's, there's 2 mL improvement over placebo.
That can be viewed to maybe be a little bit larger, required in, say, FSGS, because patients are oftentimes they have very sky-high proteinuria when they come into the study and their eGFR may take more to convincingly have their eGFR under control, more of a treatment effect. But what I can say, for certainly, the FDA have never once, nor the EMA, requested this kind of treatment effects we've seen in VIVIAD. The treatment effects that you've seen in the overall population, diabetic population, are around three times bigger than anything that has ever been previously said to be required by the agency to get it approved. So you are very, very far north of what would be needed. And as I say, it depends on disease.
You know, IgAN, about two million, FSGS, probably a little more. But it definitely depends upon how fast your eGFR is declining and what the clinical benefit is in slowing that decline, and how quickly do you need to get it slowed?
Thank you, Kevin. The last question for you I have, and that is, any recommendation which equation we should take? Because there is CKD-EPI, there is the MDRD.
Yeah.
Any feedback from you?
Yeah, definitely.
What they like the most?
... Yeah, it, well, the first thing to say is as long as the, the measure is used clinically and is not considered to be incorrect or inappropriate, and no such eGFR measures are that I'm aware of, because the trial is randomized, blinded against placebo, the method is not as important because it's the delta that you're looking at. And also what I didn't say is we, with some of the newer, you know, if you, if you go on the NKF, you can go and see there's a webpage where you can see the eGFR formula that being. And there's some new ones that appear based upon cystatin, creatinine, and a cystatin-creatinine combination. And I should say, although I didn't say it, we analyze the VIVIAD, VIVIAD data using all those different equations, all of them, and the treatment effects remained.
So but I would with these, some of these new equations, I'd just be maybe a little careful because it's strange, there's quite a large difference between the eGFR that you obtain if you use the cystatin-based new eGFR equation, as compared to using the creatinine-based, as compared to use the third equation, which is a combination of them both. So for the same subject, the same age, same gender, same cystatin level and same creatinine level, these three equations can give you answers that differ by, you know, 10 or 15 mL. This is the new set of twenty twenty-one equations. So one has to be a bit careful with that, and most folks tend to use the creatinine-based assessments of eGFR.
And in the protocols I've been involved with, it's always been creatinine-based, not cystatin-based. So I know at least that that's FDA comfortable with that, and I would suggest that the measure that we use as the primary endpoint is creatinine-based, given, you know, the long history of acceptance of such measures in CKD.
Yeah. Then there is one question of, medical and scientific trust in the data and whether it warrants a DKD study and when we could kick off, orphan disease studies, and that probably goes to Tobias. You see the data are sufficient for starting a phase II DKD study. What do you think we need to do for starting orphan studies?
Yeah. Yeah, thank you very much. So, I mean, we discussed the data that we have from a human population, where we see an increase of GFR in the diabetic population. And so this is what we have, and then we have preclinical models also aiming on more rare kidney diseases, where we have evidence for. So I would say we need a little bit more evidence. That's what we're doing right now, eventually for orphan diseases and some for models that we are testing right now. And for DKD, we do have, and as also being laid out by Kevin, robust support for GFR increase in the diabetes sub cohort. So I think this is what we have.
You know, from my-- but this is my personal perspective. I'm very interested also in more orphan diseases, providing our patients here more perspectives and chances. So that's why the research project that we are doing right now aims really to eventually broaden the indication for potential orphan diseases.
Okay, and then the last question of the meeting goes to Florian, and this is: How attractive from a pharma perspective would be a drug that stabilizes kidney function or even improves it, and how would it look competitively?
Well, good question. It is highly attractive. I mean, we have no other option in the pipeline at the moment that is really stabilizing or improving kidney function, especially in the diabetic kidney disease patient population. The only one is a stem cell based therapy that has demonstrated data in that direction. As I said before, that comes with its own challenges with biopsies to be taken. And here, a candidate or a project like Varoglutamstat could be very, very attractive from an oral administration perspective, from a small molecule manufacturing perspective, and with a profile going into a patient population that is actually treated by nephrologists already, CKD4 and 3B. This is when patients actually see a nephrologist.
So, you're addressing and targeting those physicians would be very, very attractive. And I think, well, if clinical trials can prove, then definitely there will be significant interest in the industry to look into more details of the asset and then think about next steps as well.
With this, we want to close today's event. I want to deeply thank Tobias, Florian, and Kevin for their contributions. I want to thank the neuro team, of course, for driving the development so far. I want to thank everybody who was on this call and listens in. If you have any further questions, we have an IR department. You can send any question in, and we're gonna address that in writing or another call with you directly. With that, thank you for attending the meeting, and goodbye to our speakers and to the listeners.