I'm coming in now. Good morning, everyone. Welcome. All right. We'll go ahead and get started. My name is Tanya Marvin. I'm a Senior Marketing Manager at Simulations Plus. I'll be hosting today's webinar behind the scenes and just making sure we don't have any technical difficulties. In this webinar, our Chief Science Officer at QSP, Dr. Scott Q. Siler, will discuss how quantitative systems pharmacology can increase the efficiency of clinical development efforts. Using the OBESITYsym model, Scott will show you how to simultaneously predict weight loss and nausea for a given treatment in a simulated population of patients with obesity. Our Senior Principal Scientist, Dr. Christina Battista, will serve as our moderator today. Just a few housekeeping items before we begin. We take your privacy rights seriously.
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All right. Thanks, Tanya. I am pleased to introduce Scott Q. Siler. As Tanya mentioned, Scott is the Chief Science Officer of our QSP team here at Simulations Plus. He's been in the field of QSP for many years, even predating this type of modeling being called quantitative systems pharmacology modeling. He knows a thing or two. With his background in nutritional biochemistry, he has expertise in a wide variety of areas. In particular, for today's webinar, he has expertise in metabolic diseases. With his expertise, he has led and oversaw development of many QSP models. Importantly for today, he led and oversaw the initial development of OBESITYsym, and he continues to oversee further improvements, refinement, advancements of the model, especially as new modalities and new data have become available.
I think with that, I will turn things over to Scott to walk us through today's webinar.
Thank you. Let's see. I shall advance some slides and let me just start with a 40,000-foot view of OBESITYsym. This is, I think, the second time I've given a webinar in this series and also a few talks at various conferences. I want to set the foundation here and then build upon that as far as what we have and how we continue to expand upon that and looking forward, how we will continue to expand upon that. OBESITYsym, as the abstract indicated, is QSP, a mechanistic model of calorie balance and nausea that allows us to simultaneously predict weight loss and nausea in the world of obesity treatments. A wide variety of different modalities.
We have built it within our in-house proprietary QSP modeling software, Thales, and it really does a fantastic job just giving you a snippet of our ability to simulate and predict weight loss and nausea prevalence, so the amount of nausea at any point in time as the data are reported. We have been able to, on both calibration and validation, incorporate some of the approved injected medications, semaglutide, liraglutide, and tirzepatide, as well as oral agents, semaglutide and now the more recent small molecule Orforglipron. Then expanding into other realms in terms of mechanistic contributors. For instance, the GLP-GIP-GCG triple agonist retatrutide is also represented within OBESITYsym. We'll show you examples of our ability to simulate each.
In addition to being able to simulate on the clinical side, we also have some experience in translating preclinical data into first-in-human and get a sense of what we might expect from various compounds. This is a powerful aspect here, particularly as so many different groups, so many different companies launch their clinical development pipelines in this obesity area. Then, as is the case with any good QSP model, OBESITYsym, as we're showing with the retatrutide, has the ability to expand and accommodate new mechanisms and targets as well. That's the 40,000-foot view here.
I thought it would be useful, again, just to set the stage, that foundation, to review the pathophysiology of obesity because that fundamentally informs how we designed the model, as well as to place it in the context of the metabolic syndrome because really it is, speaking of the word foundation, the foundation for many other maladies as well. I'll discuss just the QSP modeling and how we approach things here at Simulations Plus, give you a deeper glimpse at the methodology then, and then we can dive into OBESITYsym, some of the recent advances, the new medications we've added, as well as a really interesting application, again, zooming back in on retatrutide to quantify the energy expenditure effects. It's a really nice pairing of, if you will, data and modeling to answer a fundamental mechanistic question there.
When it comes to obesity pathophysiology, I imagine everyone is familiar with the time-honored Energy balance concept whereby really your set point, if you will, for body weight is the balance between food intake or caloric intake and energy expenditure, caloric expenditure. There are three primary macronutrients, of course, protein, fat, and carbohydrate that deliver calories. Although truly in some cultures, we also have to account for alcohol intake on the caloric intake side. On the energy expenditure side, there are a few different categories, basal metabolic rate being the largest contributor, but thermic effect of food, non-exercise activity thermogenesis, and exercise are all appreciable contributors as well. There are some nuances. In addition, nutrients can get lost along the way. They do not always end up as CO2 or storage. We are able to track all of the above as well.
This is, I think, a really interesting and important bit of data that leads to, I think, some really fundamental implications as we consider obesity. Simply stated, in some ways, it is sort of a well, no surprise in that there truly are genetic differences between individuals who have obesity and those that do not. Some of these really interesting imaging techniques, the fMRI, for instance, really delineates between the healthy weight and the overweight, HW and OW, in terms of how patients or individuals respond to a cue of just looking at a map, say a milkshake, something that elicits a certain response, a desire, if you will, for food. You can see there is certainly a fundamental phenotypic and genotypic difference between those two types of categories of patients, also a spread within each category.
Again, describing that there truly are different people arriving at the dinner table, if you will, with different genetic backgrounds. I bring this up for a few reasons. Now I am going to proudly wear my nutritionist hat. First and foremost, for decades, sadly, in the world of obesity and the world of nutrition medicine, we did not properly acknowledge those genetic differences. Rather, we assigned the entirety of the obesity burden to the environment, to a variety of different, if you will, kind of lack of willpower sorts of scenarios. Certainly this is, as is the case with most diseases, a combination between environment and genetic predisposition, but acknowledging that different folks will arrive at different set points, steady states based on even presented in a similar environment is super important.
One of the reasons I say that is there is a strong mental health component that comes along with this disease, many other diseases as well, but we need to certainly acknowledge that. I always firmly believe that everyone should be comfortable with what they see in the mirror, comfortable in their skin. Different people are in different levels when it comes to that. When we discuss the potential for obesity treatments to help someone arrive at that state, I think that is an important point to acknowledge on an individual level. There are also those that are comfortable in the absence of treatment. Again, we need to acknowledge the mental health component just as much as we need to acknowledge the physical health component.
That's my segue to move to the next slide, where the late 1980s, early 1990s, it was really nicely established, initially with epidemiologic data, followed by some fundamental mechanistic data, that there is this metabolic syndrome. Some of my heroes, Jerry Reaven, Scott Grundy, and many others, of course, really helped establish this concept whereby obesity or, if you will, excess adiposity has its tendrils in many other associated diseases. In fact, I think whilst that was really nicely established in kind of the pathogenesis of the metabolic syndrome, we now come to appreciate a few additional aspects.
I like to visualize this as sort of a spoken hub model, where obesity or excess adiposity has its mechanistic ability to interact with a variety of other associated syndromes like chronic kidney disease, hypertension, type 2 diabetes, cardiovascular disease, MASH, and the recently rebranded polyendocrine metabolic ovarian syndrome, or previously known as PCOS. All of these are intertwined with that mechanistic component that excess adiposity delivers. Again, instead of looking at it pathogenically, rather we can look at it from the treatment point of view. As we are treating these patients with obesity, reducing the adiposity, we are seeing relief of these additional conditions as well. Again, the choice to consider and pursue treatment for obesity extends as well beyond, if you will, the mental health components, the sociologic components, and now into public health components.
As we transition from the individual to the populations, really what we see in data such as this from the Khunti et al. paper are fantastic at showing even a modicum of weight loss, 5%-10%, which 20 years ago was thought to be almost unachievable, now is almost routine with the medications we have available. It leads to significant improvements in all of those same associated maladies. Fantastic news, again, from the public health point of view. It also then kind of serves as a motivator, shall we say, for this massive drug development effort across industry to treat obesity. I believe this is already out of date, being I think just 18 months old.
I think it's already widely acknowledged that maybe the two to three-year goal for any of these potential treatments is on the weight loss, but the five to 10-year goal is really to treat the metabolic syndrome. That, of course, has economic consequences as well, with the spending expected to be decreased in the other specific indications that are dependent upon that adiposity. Really it's a fantastic, an almost unusual situation where we're able to treat the source, and then allow the downstream consequences to as well be relieved. The ideal in medicine, however, not always achievable. Really exciting in terms of the stage it sets, the motivation, the fundamentals of understanding the pathophysiology and the factors that go into the decision making. It also lends itself, in this case, quite nicely to mechanistic modeling, such as we do with quantitative systems pharmacology.
Let me just describe it. As I was introduced as being one of the QSP modelers before we had that name. Certainly, that's true. In fact, in those early days, one of the things that we were modeling is fundamentally energy balance. Really nicely, what I've found over the years is, as this technology has become increasingly adopted, there have been different variants of it, so to speak. I thought it would be useful to describe how we're approaching it with OBESITYsym, how we approach it at large within Simulations Plus. Fundamentally, we're talking about a mechanistic representation of the disease of interest, the pathophysiology, again, the mechanistic contributors to this. That allows us to get this really fantastic understanding of the disease, and those interactions. Of course, there isn't one patient. I just made the case for genetic variability, right?
Certainly, there are a wide range of individuals. For us to account for that inter-patient variability, we develop what are known as simulated populations. I'll elaborate on that and how we do that in just a few moments. Once we have our simulated population, our representation of the disease, we're able to then overlay or introduce, simulate, the exposure of drugs, the dosing, and the concentrations. Depending on the modality, it's sometimes more applicable to use PBPK modeling, other times more simpler, if you will, compartmental modeling. Of course, certainly within Simulations Plus, we have fantastic tools that allow us to do either, depending on the nature of the question. Combining that with the ability for the drug to interact with its target. Sometimes that's truly described as pharmacodynamics, of course, PD.
For us, as we consider the system at large, what's perhaps more important is how that pharmacodynamic effect interacts with the system, i.e., the mechanism of action. We're able to allow all of those three components to interact, and it's really the intersection of them that allows us to predict efficacy and adverse events within OBESITYsym simultaneously. Our group, our QSP group at Simulations Plus, has a robust library of QSP models. Certainly, today we're zoomed in on that cardiovascular, renal, and metabolic area. You can see we certainly have safety, pulmonary, oncology, inflammation, immunology. It's probably obvious, but I'll make it even more obvious. We have the ability to build models in new areas as well, new disease areas, right?
We have a unique group of people, unique in the fact that we have so much experience building new models, we can certainly expand into other indications as well. We have, in addition to versatility across indications, we have versatility in applying different QSP modeling approaches, utilizing different QSP modeling software as well. That versatility allows us to work with numerous clients and answer numerous questions in numerous ways. It also then allows us to match the appropriate approach for answering a question With the appropriate approach, with the appropriate question. That's the way I meant to word that. In this case, with OBESITYsym, we utilized our proprietary in-house QSP modeling software known as Thales. Let's see if I can advance the slides. There we go. Thales is really fantastic software. We've been using the heck out of it, as you might imagine.
There are, I would say, two attributes that particularly stand out, especially in its use and application for OBESITYsym. One is the simultaneous fitting of the optimization. Really, we're able to fit to numerous clinical trials on both the placebo, to capture the lifestyle components, as well as the treatment components and various dosing modalities to allow us to generate that single simulated population that agrees with the totality of those data. Within the realm of obesity and obesity medications, certainly up titration, down titration, and complicated dosing protocols are the name of the game, and our ability to simulate with relative ease complex simulation pipelines is, again, a fantastic attribute, and really allows us to explore these different modalities, different treatments, with a great deal of efficiency.
The way that we use process-wise, that software, the Thales software, is I would say relatively standard across QSP groups. Certainly we evaluate the amount of data that we have available, separate into the fitting versus the validation, generating within the context of that mechanistic model, identifying the parameters that could and should vary, to capture that inter-patient variability, doing that, and then generating a batch of simulated patients and then applying prevalence weights to them to really capture the appropriate distribution for each of those patients within a population. Again, that single population has been fitted to a very discrete set of clinical data. Then we prune away the ones with the minimal, if you will, prevalence and then make sure that we achieve that really solid and good fit quality and then proceed with the validation.
This allows us to have great confidence in that final population and its ability to capture then, to simulate and predict within that parameter space, those treatment modalities. It's been put to practice in a number of different disease areas, and I'll show you now how we've done so with obesity. Again, coming back to the model and its design, I mentioned at the outset that really fundamentally, we're talking about calorie balance and food intake, whether it's carbohydrate, fat, or protein, the primary macronutrients, or energy expenditure, looking at, again, DIT, basal metabolic rate, NEAT, and exercise. All of those components are accounted for and integrated. Frankly, we are utilizing a published model that has been extensively used by an old colleague, a good friend, Kevin Hall.
We integrated it into this model, allowing us to then now introduce the pharmacologic components, the PK, the potency, other potential treatment attributes as well. Now, within the world of obesity, and particularly for the gut peptides like a GLP, like an amylin, to allow us to rebuild in a sub-model for nausea. One of the things that sets apart OBESITYsym from other energy balance models is the ability to simultaneously, within that same ordinary differential equation environment, predict both nausea incidence, nausea prevalence, as well as weight loss or potentially weight gain, depending. Again, design-wise, we understood the nature of the disease, the nature of the treatments, and the perpetual trade-off between those gastrointestinal adverse events and the ability to drive weight loss and having to balance those two. This model allows us to do that.
As I mentioned earlier, optimized and validated with semaglutide, liraglutide, tirzepatide, Orforglipron. You'll see those simulation results. We can support and have supported the translation of preclinical compounds into the clinic. Again, just to emphasize, expandable and hence can be updated to include novel targets and approaches. When I mentioned the genetic variability earlier, I was really fundamentally thinking about it from the calorie balance point of view and the motivation to consume food upon receiving certain cues. Interestingly, the genetic variability also applies really to the target of many of the obesity drugs, the GLP-1 receptor. There's been some fantastic initial GWAS analyses that were followed by some true biochemical explorations, i.e., now phenotypically, we understand the variability, whether it's the EC50 or the Emax in GLP-1 receptor variants across the population. This is super important.
Now we understand when it comes to obesity, what are some of the interpatient variability that we need to account for on the pathophysiology side and on the pharmacology side. Pulling that all together, we were able to develop our global OBESITYsym Pops. Again, we've been able to, as you can see in the circle on the top, integrate, rely upon the clinical data from many of the different key treatment modalities. Of course, those that are most advanced in the clinic, again, to use for calibration and validation. Well, that was fun. Hold on. Let me come back and it appears that my computer was so excited about this slide that it wanted to give you a special showing of it.
The other thing that I think is important to point out here is we have a discrete population of patients with obesity, but also because patients with type 2 diabetes have a different weight loss response to the same exposure, the same doses of drugs. We've taken the time recently to as well generate a population of patients with type 2 diabetes, again, from the energy balance and nausea point of view, and then a disease like MASH that has a mixture of obesity and type 2 diabetes, and hence propensity in the differential responses to weight loss. Really what you see there is our initial movement towards recapitulating and simulating the metabolic syndrome in addition to just obesity at large. It's a really nice step forward in that direction.
When it comes to the treatments and the calibration and validation, again, I've spoken to, in previous sessions, some of the original semaglutide, liraglutide, tirzepatide components that we use for calibration. Today, I'll give you a glimpse at that as well, but I'd really like to highlight how we've now integrated Orforglipron and semaglutide in the oral delivery system, as well as, again, retatrutide, and show you simulation results for all of those. We'll start with tirzepatide and that fundamental calibration. Certainly, the vast amount of clinical experience is with, I would say, tirzepatide and semaglutide at this point in time. We were able to simulate the phase III clinical trials with up titration to various stopping points. In this case, the stopping point was 5 mg QW.
You can see on the left-hand side, we have the placebo, on the right-hand side, the active treatment, and we're taking the classic pharmacology approach of overlaying the treatment on top of the placebo. So that, again, accounts for the lifestyle effects, if you will, on the weight loss component. Also, the white coat effect, whereby despite having no active ingredient, some individuals feel nausea. So we've overlaid the pharmacologic response on top of that. The other aspect here is to note that really what we're looking at is the prevalence in these simulations, the prevalence of nausea and i.e., how many people, what percent of individuals are reporting nausea at any given point in time. This is calibration. It's meant to line up quite nicely.
Again, as we consider this global population, it is not only calibrated to this treatment, this dose, but also larger doses as we consider the 10 mg QW. Same sort of fantastic agreement between clinical data and simulation results. As we expand it to even higher doses, the 15-mg QW, same thing, really fantastic alignment. Now as we expand that circle into other treatments, for instance, semaglutide that is injected subcutaneously. Same paradigm you can see within that calibration, considering all the different mechanisms of action, different patients, different studies. The calibration comes out just really, really solid across the board.
In fact, so solid that one of our validation simulations was then to transition, use the same molecule, semaglutide, but in that oral delivery system, that unique sort of SNAC, S-N-A-C, not the foodstuff, that allows the peptide to be absorbed orally and hence drive weight loss at these higher doses, to account for the lower bioavailability. Again, on the validation side, we do a really solid job of predicting the weight loss as well as, in this case, the incidence of nausea. So the total number of reported nausea over the clinical trial, in this case of 64 weeks. Staying within the oral delivery realm, now if we transition back to Orforglipron for calibration, we were using this phase II study that, again, by design, our ability to simulate the weight loss and the incidence of nausea lines up quite nicely with the clinical data.
Now transitioning to within that same phase II study to validation, really nicely predicting weight loss and nausea. The more recent phase III data as well. We are looking at simulation results that are predicted results that again really nicely aligned with data. Step by step, methodically, exploring different sorts of mechanistic contributors, different sorts of delivery modalities, calibration to validation. This sort of methodical approach that is really allowing us to have great confidence in our ability to predict both weight loss and nausea simultaneously. Note, the other thing I should probably now emphasize, the different types of uptitration schedules, whether Orforglipron being on the weekly uptitration schedule, four-week intervals. Really, there's a whole myriad of possibilities here.
We've simulated what was done in these clinical trials. More importantly, perhaps as we look forward, is our ability to then consider almost any type of uptitration protocol to define the optimum one for a given treatment. Again, all of those were examples of us working with clinical data and incorporating them into the model, which sets a really fantastic foundation for us to now expand a little bit on that and extrapolate to some extent by making translational predictions.
In this case, by knowing so well, having characterized so well the response to a semaglutide, a tirzepatide, that allows us to then basically take, so long as the potency, the EC50s are measured in the same cells, in the same experiments, to take the ratio of potency for a given treatment, and link it back to that positive control in semaglutide, tirzepatide, Orforglipron, and then bring that in and basically apply that same ratio of potency from the in vitro studies to the in vivo potency that we've gathered from our simulations, and then make those sorts of initial predictions of weight loss, of nausea. Again, what we are seeing is really solid predictions.
Much like anything that is translational, it may not be perfectly hitting the center of the target, but it is right in the middle of the target, if you will, which is providing a fantastic source of information to inform, again, those sort of clinical development pipelines. Let's see. Moving on now, I want to show this really nice example of how we have continued to use that paradigm that has long since been discussed and was published by Kapil Gadkar and other former colleagues, showing the interactive nature, the iterative nature, the almost evolutionary nature of modeling and data collection, whereby some data are needed to generate the modeling, some modeling is needed to highlight the next phase of data collection. Really that iteration is, especially in an area like obesity where so many different treatments and clinical development is fast and furious, is required.
It allows us to keep pace with the obesity field. I find this to be a really nice example of that in that, certainly as we consider this GLP, GIP, and GCG, or glucagon receptor agonist, triple agonist compound of retatrutide. Whilst this is really impressive, gosh, I think there have been reports of as much as 30% weight loss at the highest doses. This really seems to truly have some impact beyond, and really expand the portfolio of options available to patients with obesity. In addition to the food intake contributions from the GLP and the GIP receptor agonists that we certainly see with a semaglutide, with a tirzepatide, we also have this glucagon receptor component that drives also increases in energy expenditure.
Conceptually, we know that from a modeling point of view and, i.e., quantitatively, there was a bit of a data gap, at least in the public domain, whereby the exposure response relationship on GCGRA, the glucagon receptor occupancy, and the downstream effects on energy expenditure were not reported. As we considered how we might approach this, we needed to delineate the total weight loss into the respective categories of food intake and energy expenditure. Basically, we applied that translational approach that I just described. We knew that retatrutide's potency for GIP and GLP were measured against tirzepatide. We could use that sort of ratio scaling to account for the food intake components. Take that into the next step. Obviously, we need that pharmacokinetic model of retatrutide as well.
The third and final step was to expect that with just the food intake components, we would not predict the same amount, the same magnitude of weight loss as the clinical data might suggest, with that difference being attributed to the glucagon and energy expenditure components allowing us to quantitate that. Let me show you how we did with that. We developed a compartmental model of the retatrutide compound. We did this using certainly the Simulations Plus software, Monolix, very efficiently. This went pretty darn quick and we were able to take the equations out of Monolix, incorporate them into the Thales, into software, into OBESITYsym readily. That same fit was preserved as we moved between software. We did our initial simulations of weight loss.
Again, unsurprisingly and by design really, we did not predict the same extent of weight loss as was reported in the public domain. It was at this point that we realized that gap, in order to get the weight loss all the way down to what was reported, that difference was due to energy expenditure, the glucagon receptor effects on that. Now the next step was for us to allow that parameter, the glucagon receptor agonist parameters, and their effects on energy expenditure to be optimized in a subsequent set of simulations. That allowed us to then define exactly what that parameter is, what the range of, more importantly, energy expenditure was. The increase in energy expenditure to accommodate that totality of the weight loss, incorporating both the food intake and the energy expenditure components.
Really exciting, nice quantitation application of QSP modeling to answer a fundamental, at least at that point, unanswered question. Certainly, there are ways to measure energy expenditure in these scenarios, and for some compounds, for some glucagon receptor agonists, those data are available. I wouldn't be surprised if for retatrutide, those data will be available relatively soon. They weren't as we embarked upon this, we were able to, again, use our QSP modeling to fill in that gap. To give you a sense of how much it changed over time, this is again accounting for some of that uptitration as well. Let me just remind everybody, one of the basic adaptations to a reduction in food intake, to a reduction in body weight, is also a reduction in total energy expenditure.
The cost of movement declines, there are some endocrine adaptations with weight loss, with reduced food intake that also reduce the basal metabolic rate. You can see that initial sort of boost and then reduction as the weight loss is incurred. That's also followed by an increase over time as we scale it, and mind you, the energy expenditure for the body weight. As we're losing body weight, we are seeing relative to body weight, increases in energy expenditure, again, due to that glucagon receptor component. We're able to quantitate here that specific effect, visualize it, I think this is a really nice, again, example of that. Retatrutide does have the GLP and the GIP, each of which can have their role in the nausea side as well.
Here you can see we do a nice job of simulating the nausea response as well. There's not a wealth of clinical data here, and some of it is frankly conflicting within it, which is not unusual. Conflicting within the study, not unusual for clinical trials. What we can see here is, whilst we do a fantastic, superlative job of capturing some of these dosing, the adaptation, the tolerance to the nausea signals. In other cases, maybe we aren't capturing it quite as well, right? There's certainly room for improvement and with more clinical data, more prevalence data, more incidence data, we're able to expand that capability. That's that sort of evolution iteration component to which I was speaking earlier. We have done all this with a phase II report that I think was released about a year ago now, give or take.
In that period of time, Lilly has, by means of press releases, issued sort of updates, if you will, on the clinical experience, mostly on the weight loss side, with a series of phase III studies known as the TRIUMPH studies. We thought, well, let's see if we can predict in this sort of validation way that response. We simulated the uptitration to 9 mg and 12 m of retatrutide over 68 weeks, which is what was done in the TRIUMPH-4 study. Based on this, the statistics clearly aren't reported, but you can see that 26% and 28-ish% of body weight was reduced over 68 weeks, which is right in line with our predictions of the weight loss for each as well. Again, a really nice validation of this.
There are other permutations, TRIUMPH 1 through three, and I think even five as well. We're continuing to expand our reach here, our ability to predict weight loss and nausea, across all of those specific retatrutide studies as well. I imagine my enthusiasm here is probably evident. I really find this to be a fantastic example of using simulations to understand the science better, to pair simulations and science, to really have the tools in hand to understand a compound, to inform decision-making, and beyond. Again, I just want to return back as we close the presentation portion, to the fact that in addition to the patients with obesity population, for which I just showed you many simulation results, calibration, validation. We have the same thing occurring as well with patients with type 2 diabetes that are also obese and/or MASH and are also obese.
Patients with type 2 diabetes tend to lose less weight for a given level of drug dosing than patients without type 2 diabetes. We've accounted for that mechanistically to some extent and have been able to undertake the same calibration and validation approach that I just showed you for these distinct populations as well. We hope to have another webinar in the near future, where we're able to highlight some of these simulation results as well. When it comes to the utilization of OBESITYsym, and frankly, all of our QSP models, we have two dominant paradigms that aren't mutually exclusive. Certainly, you're able to license the software, the models, and do the simulations in-house, as well as you can allow our expert users to run simulations on your behalf, within the context of services projects.
Really, I think what we see works best is a combination of the two. I'd like to borrow the edict from the classic medical school situation of learn one, do one, teach one. Really, if we're able to do a services project and teach your in-house QSP experts how we're executing, how to use this model, then they can do one on their own, and the next several can be done in-house. It's a fantastic way to climb that learning curve. That is where we are today. Going forward, lots of different treatment modalities to explore in the obesity realm. On the modeling side, one of the things that we're really making nice steps towards is pairing QSP modeling with artificial intelligence within the same software. We discussed Thales and the methodical approach with which we pursue QSP modeling.
Certainly involving expertise, experience, all of the above. Now we're pulling that together in a more efficient way in this pairing of AI and QSP. The software has the name Turing, and really it should make the whole process more efficient. We're developing the software and the approach in real time right now. We're hoping to release it into the wild here in short order. I just wanted to give you a glimpse of what's to come. Depending on the question at hand, depending on the disease at hand. Presently, we have the ability to take the bottom-up, real methodical approach, the Thales approach, where we're able to really utilize lots of clinical data. Soon enough, we'll be able to do even more of that in this sort of different workflow that relies upon agents and GPUs and the like.
I want to conclude by acknowledging, while I get to stand here and share all this with you, it's really the result of the hard work of many people, not the least of which, the person who introduced me, Christina Battista, the moderator of this talk. Also, I want to acknowledge some of the folks with whom my earliest colleagues that I mentioned, that we were also in the early 2000s modeling energy balance. I want to make sure to acknowledge Kevin Hall, Dave Polidori, Jeff Trimmer, because that early work has very much informed this second iteration as well. With that, I say thank you, and I'll open the floor to questions.
Just real quick. I just want to jump on real quick. We have a poll that we'd like to launch before we do the Q&A. Let me launch that. All right. Poll is up. Give everybody a few seconds. Go ahead and answer it. Got some answers coming in. Give it another 15, 20 seconds. In the meantime, thank you very much, Scott. All right. I don't see any more coming in, so I will end the poll, and then I will turn it over to Christina Battista for the Q&A.
All right. Thanks, Tanya, and thank you, Scott, for the lovely webinar. We do have some questions. Feel free to add more into the Q&A as we're going through these. We'll address as many as we can in the time remaining here. The first question is related to, it looks like the retatrutide predictions. How much confidence do you have in simulation results that infer energy expenditure effects on GCGR agonist component of retatrutide if there are not direct measurements of it in the public domain?
That's a good question. In this case, and maybe I didn't describe it well enough, but there is a rich literature of nutritional intervention studies as well as metabolic chamber studies that allow us to understand quantitatively how energy expenditure change can be captured, as well as food intake reduction can be captured, again, resulting in body weight. Really what we have is a series of studies whereby metabolic rate has been adjusted, food intake has been adjusted, allowing us to effectively build a QSP mechanistic model of energy balance. Now, really the answer was how much energy expenditure was required to pair with that food intake component and within that framework, that is almost a modeling 101 program. The sort that you might assign to an undergrad. Really, it's a nice application. I think we have a great deal of confidence.
Certainly, we can hopefully get somebody in a metabolic chamber and validate those predictions.
Great. The next question is, are you able to capture nausea severity or other GI adverse events with OBESITYsym?
Yes, we could. We have not yet built it into OBESITYsym, I think really it's an important question and one that I think we will ultimately pursue. The data aren't as rich when it comes to severity, nor when we look at the other GI adverse events. Nausea is the most robust of the signals, we pursued that on the modeling side first. Also, to some extent, it is the canary in the coal mine, which is many of the other GI adverse events, the severity, et cetera, follows. If we're able to track the nausea, we know that within that also are some more severe adverse events as well.
Great. Thank you. This one, I think you kind of teased the type 2 diabetic population a bit, this is probably where this question's stemming from. To what extent can OBESITYsym be used to predict the HbA1c lowering in a type 2 diabetic population?
This model, OBESITYsym, is not capable yet of predicting changes in glycemia, whether it's fasting plasma glucose, whether it's A1c, 24-hour average. However, what we have now built is sort of a bridge between the effects of weight loss and the effects of glycemia. We have other models in-house that would allow us to explore that. Really, we could take the simulation results from one population in one model, bring that over into the other model, and predict the changes in A1c due to just the weight loss components. Certainly, when we're talking about a GLP-1, for instance, there are insulinotropic components, we would need to account for that as well. Again, that bridge has been built.
Frankly, we've used it to good effect for MASH, a different disease, and predictions of steatosis and improvements of fibrosis due to the weight loss.
Great. I think on that same topic, now that you have these discrete populations for type 2 diabetic patients, obese patients, how do you delineate those differences in weight loss that are observed at the same doses and treatment between those two populations?
It's a mechanistic model, so fundamentally, we have to understand the mechanisms. I'll say it's not very well characterized. Despite there being a great deal of clinical experience seeing the differences between the two populations. The actual mechanistic studies are relatively sparse. That said, there is a vein of the literature that has explored the urinary glucose excretion component. Certainly, with hyperglycemia, the ability to reabsorb glucose by the kidneys can become saturated, hence glucose spills into the urine, calories are lost. With effective treatment of glycemia, that saturation point is reduced. What we believe is, to some extent, to varying quantitative contributions, in some cases zero, in some cases as many as 100-200 calories a day, glucose is being spilled to the urine with the diabetics. As the GLP-1 or weight loss effects manifest, that is driving glycemia down.
Less is spilled over, allowing more calories to be retained, offsetting those direct pharmacodynamic effects on food intake. Mechanistically, that's one category. It may go beyond that as well. There may be other mechanistic contributions, but it serves as a good start for it. There is some data that describe quantitatively the extent of that as well.
All right. The next question is, how difficult would it be to include other non GLP-1 modes of action into this platform?
It's a good question. Something like a myostatin activin, right? Those that are designed to, if you will, preserve skeletal muscle instead of food intake. There's the amylin, which has a similar mechanism of action, albeit completely different peptide, hence pharmacology. Those are basically the paradigm that we just showed here, where if there are clinical data that allow us to characterize the pharmacodynamic over time effect as well as the potential for adverse events. Again, something like an amylin also incurs nausea in addition to the weight loss. Once we have that, and we can build in with our fantastic fitting algorithm within our population, that allows us to then bring that into the fold. One population that agrees with all of those modalities. Again, it allows us to then do some translational components and really support the pipeline late and early.
Thanks, Scott. The next question is related to the side effects within the model. How mechanistic in nature are the side effects related to the model components? Are these incident event like Markov models, or are you just trying to understand your nausea curves? Sorry, just trying to understand more about the nausea curves.
Yeah. It's an excellent question. We're not utilizing that Markov chain model. I mentioned before, this is ODE based, which allows us to generate the simulation results for nausea and weight loss within the same model at the same time. Really what we're looking at is Sorry. We start with by having a population with a range of sensitivity to nausea, i.e., GLP-1 receptor variants that are consistent with the genetic variability, hence different sensitivities. Also, we're able to account for the tolerance, whereby exposed to the same amount of GLP over time, the effect is diminished. We're accounting for it on that sort of phenomenologic semi-mechanistic level. I think this is one of those areas, because we're talking about neurons, it is somewhat understood, but there's so much room for improvement there with data coming online every day.
Lots of debates on which portions of the brain. Is it the NTS, is it the AP? Which is most responsive? All of those are in play right now. I think it's challenging to build a bottom-up fundamental biochemistry-based representation at the moment. As those data become available, I think this representation will become increasingly mechanistic as well.
Great. I think that's all the time we have for Q&A. I will turn it over to Tanya to wrap things up for us.
All right. Thank you, Christina. Thank you everybody who participated. Thank you again, Scott, for your presentation, and Christina for moderating for us today. If there's any questions that we were unable to answer live today, someone from our business development team will reach out to you with answers to those questions. I put in the chat some links to our website, and also we invite you to follow us on social media if you don't already. This webinar has been recorded for playback, and it will be available on our website and also our YouTube channel. This concludes our webinar for today. Thank you so much for joining us. Have a great day. Bye-bye.
Thank you.