G`ood morning, everyone. Thank you for joining us for this RNAi roundtable. Today, we'll be doing a deep dive into Alnylam's engine for sustainable innovation and discussing our leadership in the RNAi platform and human genetics. I'm Josh Brodsky, Senior Director of Investor Relations and Corporate Communications at Alnylam. And with me today are Vasant Jadhav, Senior Vice President of Research, Kevin Dewey, Associate Director of Research, Paul Nioi, Vice President of Research, and Aimee Deaton , Associate Director of Human Genetics. Today's RNAi roundtable is part of a series of roundtable webinars that we've been hosting to review progress across our various programs and R&D efforts. Today's event is expected to run approximately 60 minutes. Vasant will moderate a Q and A session at the conclusion of the presentations.
If you'd like to submit a question, you can do so at any time during the event by typing your question into the Ask a Question field located on the upper right of the webcast. Finally, as a reminder, we will be making forward-looking statements during this webinar, and we encourage you to read our most recent SEC filings for a more complete discussion of risk factors. And with that, I'll now turn it over to Vasant.
Thank you, Josh. Hello, everyone. On to the next one. As we have stated before, Alnylam is poised to become a top-tier biotech. We are leaders in RNAi therapeutics. We have pioneered the new class of innovative medicine. We have now five approved medicines in less than four years. This wasn't just overnight success. This is based on our 16 years of investment, starting with 2002, the foundation of the company, and then the first approval in 2018. All that investment from the beginning and later on has given us this very robust clinical pipeline across rare and prevalent diseases. We have a global footprint with strong commercial capabilities, the leading IP estate. We have a strong balance sheet, and very importantly, also that we are on our path towards financial self-sustainability. We're a highly differentiated company with a proven track record and de-risked platform .
Some of the examples of this: our platform, our technology is highly modular and reproducible, as evident with all the programs that we have put through and the clinical trials. Our probability of success in clinical trials is very high as we look at the industry standards. Our organic product engine is capable of sustaining the innovation for future growth, and this is really the topic for today that we'll get into more. And we have the track record of setting and exceeding our five-year goals, kind of starting with five-year 2015, 2020, and now on our way to P5 by 2025. So with that one, let's look into the multiple drivers that we see for our future growth. And these include TTR Franchise leadership, expansion into prevalent diseases, and the topic for today, the engine for sustainable innovation.
So, what is it in this engine for the sustainable innovation? That includes three pillars. And the first of these is platform innovation, the technological advances that we have done over the years. This will be covered separately. We're not going to get a whole lot into this in this meeting, but we're really excited about our IKARIA platform that enables robust target knockdown with the potential for annual dosing. But importantly, today we'll be chatting about the extra-hepatic delivery and the human genetics. We're taking all the lessons that we have learned over the years for the liver delivery and applying them to have the extra-hepatic delivery in the tissues of interest. And this includes CNS, eye, and others, and we'll be talking more about that. There will be an upcoming roundtable that will discuss more on the CNS delivery. We'll keep that one.
That discussion will happen in that roundtable. In the extra- hepatic delivery, we have our C16 conjugates that provide robust activity across the tissues, and now we are also looking at the targeted delivery approaches using peptides and other ligands to explore the delivery into other tissues, so that's really the extra- hepatic delivery is one of the key pillars of our sustainable innovation, obviously, in addition to what we are doing and will continue to do in the liver space. The other one that I'm very excited and also very proud to have this unique capability at Alnylam is our human genetics effort, and this has given us an ability to source novel genetically validated targets. We have secured access to large databases, which we can utilize to identify these kind of targets.
We have shown that with these kind of capability, we can identify novel gene targets, like example, HSD17B13, Inhibin E, and there are a lot more that we expect to come with this effort. So with that introduction, now I will hand it over to my colleague, Kevin Dewey, who will talk about the targeted delivery of RNAi therapeutics to extra-hepatic tissues.
Thank you, Vasant. Good morning. I'm Kevin Dewey. I'm the Associate Director in the research group, and I co-lead the delivery sciences team, which is really focused on expanding the reach of RNAi therapeutics beyond the liver into some of the priority tissues that are listed here on the left-hand portion of the slide. Now, one of the approaches we're interested in to do this is using this targeted delivery approach, where we have a ligand that's conjugated to our siRNA payload that recognizes a particular receptor with a high degree of affinity and specificity, and we view this targeted approach as complementary to the lipid platform that Vasant mentioned, which was recently published in Nature Biotechnology, where we see broad distribution across multiple cell types of tissues, including in the CNS, lung, and in the eye, so with targeted delivery, we're looking for a more restricted delivery profile.
As I mentioned, there are two components to this. There is the ligand, which we append to the siRNA, and also the receptor, which is expressed on the cell type we're trying to access. These are discrete efforts. On this next slide, I'm highlighting what we're doing to address these. For receptor identification on the left, we're leveraging technologies like proteomics and transcriptomics of cultured cells or tissue samples harvested from an organ we're trying to access in hopes of identifying new receptors that are highly expressed in those tissues. We're also interested in exploring unbiased discovery approaches, such as using AAV capsid libraries to find new receptors of interest in various tissues. Now, on the right, I'm highlighting some of our ligand discovery and identification efforts. We are open to exploring ligands of various types, including small molecules, lipids, peptides, and larger proteins.
I think we have a preference for things that are smaller and perhaps more amenable to manufacturing. But all of these can be conjugated to siRNA, and then we can evaluate their ability to drive uptake and payload delivery into all of the tissues highlighted on the right-hand portion of the slide. Now, as a representative example, today we're going to be walking through some work we've done to develop a cyclic peptide targeting a receptor expressed in skeletal muscle. Before we dive into the data, I did want to mention that we've recently refreshed our toolbox of core capabilities that are really essential for characterizing novel ligand and receptor pairs. We've established a pipeline for engineering cell lines to overexpress or delete different receptors of interest.
We've established a protein production workflow, and we're now making proteins at the hundreds of milligram scale for a variety of applications within the scope of delivery sciences. We've onboarded new technologies to allow us to evaluate the binding interaction between new ligand and receptor pairs, and finally, we've developed new conjugation methods that allow for the high-throughput conjugation of these targeting ligands to siRNAs. So as many of you know, we've established back in 2021 a collaboration with PeptiDream to access their mRNA display technology for identifying cyclic peptides for particular receptors of interest, and that collaboration is progressing very well, but we're also interested in other techniques that are available for peptide discovery, and one of those is phage display, which is highlighted on the left-hand portion of the slide.
So here, we're able to display libraries of cyclic peptides on the surface of filamentous phage and pan them for affinity against immobilized receptors of interest. Now, this is a cyclic process, and after many rounds of selection, we're able to identify a peptide hit against a receptor we know to be overexpressed on skeletal muscle. Now, on the right, I'm showing some characterization we've done for that top peptide, confirming that it does indeed bind to the target receptor. And so these are SPR sensograms, and we're looking at the monomeric version of the peptide on the top, which binds with an affinity of 230 nanomolar. Now, as a strategy for increasing the apparent binding affinity, we also looked at dimeric versions of these peptides and are able to show by SPR that we can enhance the affinity over 100-fold with KDs in the 2 nanomolar range.
Now, the next thing that we wanted to show was that these peptides bind to this particular receptor within a cellular context. They were discovered using recombinant protein through the phage display process. Here, to validate that they bind the receptor on cells, we've conjugated these peptides with an Alexa Fluor 647 dye and incubated them with cells either expressing the receptor on the top panel or cells that have been engineered to delete receptor expression on the bottom. Starting with the monomer peptide binding at four degrees Celsius, we see very nice association with the plasma membrane. Then when we look at the panel below, we see essentially no association with the cells that have been engineered to delete the target receptor.
This strongly suggests that the peptide is indeed specific for our receptor of interest and not nonspecifically binding to other things on the cell surface. When we run these experiments at 37 degrees Celsius, we see that the peptide is rapidly internalized into the cell cytosol. Again, in the cells that do not have receptor expression, we don't see any association. And importantly, we see very similar subcellular localization patterns between the monomeric version of the peptide on the left and the dimeric version of the peptide on the right. Now, the next question that we wanted to answer was whether or not appending these molecules to siRNA in any way perturbs this binding interaction. To do this, we generated a number of conjugates using copper-free click chemistry and evaluated their binding affinity using a technique called Biolayer Interferometry.
Here, the cell surface receptor, the extracellular domain of the cell surface receptor, is immobilized on a biosensor, and we titrate in the conjugates composed of the peptide and the siRNA. In the sensogram and the right-hand portion of the slide, we can see very clear associations suggesting that the peptide still binds as a conjugate, and we can see the dissociation profile in the right-hand side of the sensogram. This data can be fit to a kinetic model, which is what I'm showing in the table on the bottom, and we calculated a KD of 1.6 nanomolar for the dimeric version as a conjugate to siRNA. Now, this is essentially exactly what we measured by SPR with the peptide alone and strongly suggests that conjugating the siRNA to the peptide does not perturb this interaction.
We followed this up with a couple of different cell-based techniques, again, to confirm that as a conjugate, we're seeing the same level of binding to the cells. To do this, we've looked at unconjugated siRNA or siRNA conjugated with the monomer or the dimer version of the peptide. All three constructs are labeled with an Alexa 647 modification, which allows us to track these conjugates. Starting on the right, we're looking at flow cytometry histograms on the top left of the unconjugated siRNA compared to unstained cells in light gray, and we see really no shift in the fluorescence signal. Now, on the right, we can see in light blue, the monomer results in a very nice shift, as well as the dimer in the bottom, and these are incubated at 400 picomolar concentrations, which is quite low.
Now we can see that this effect is titratable in the dose-response curve, and we're seeing the dimer saturate at lower concentrations compared to the monomer, which is most likely a consequence of the higher affinity of the dimeric version. We followed this up with a confocal microscopy assay, which is shown on the left-hand portion of the slide. For both the monomer and the dimer, we clearly show internalization of the red signal inside the cells, whereas the unconjugated version of the siRNA shows no cellular association. We've built this really nice body of in vitro characterization. We can show that these peptides bind, they internalize, but what we're really interested in is using these peptides to drive uptake into skeletal muscle and measure target gene knockdown. That's what I'm going to show you on the next slide here.
Now, in order to do this, we needed to rely on a humanized mouse model. So this particular peptide is not cross-reactive with the mouse version of the target receptor. So we've replaced the extracellular domain of the mouse receptor with the orthologous human sequence in the humanized mouse. We dosed both wild-type and humanized animals with the monomer and dimeric version of the peptide. And starting with the data on the left in the wild-type animals, we can see essentially no activity in the skeletal muscle after a single IV administration. Now, when we look in the humanized animal, which expresses the human receptor, we can see 50% to 60% knockdown for the monomer and dimer constructs in skeletal muscle.
Now, the lack of knockdown in the wild-type animals strongly suggests that the activity we're seeing in the humanized model is a consequence of the interaction between the targeting ligand and the receptor, and not driven by some other property of the peptide like hydrophobicity. The last point I wanted to make on this work is that all of the in vitro and in vivo data we've shared today were generated with the parent version of the peptide that came out of the phage display screen. Now, in parallel to this work, we've also put that peptide through a very rigorous SAR process to optimize for a number of different properties, one of which being binding affinity.
And the results of this SAR are shown in the kinetic map on the right-hand portion of the slide, where we're plotting the K off on the x-axis versus the K on on the y-axis of all of the different SAR peptide variants that we've made. Now, the diagonal lines cutting across the plot are lines of constant affinity, binding affinity, and we can show the improvement of the parent peptide, which was measured at about 230 nanomolar, to our optimized version in green, which is showing a single-digit nanomolar binding affinity for our receptor as a monomer. So we're very excited to now take these optimized peptides, functionalize them with siRNA, and put them through the same battery of in vitro and in vivo screens.
And there was one other quick bit of data I wanted to share with the team, and that's our evaluation of known binders for skeletal muscle as a way of sort of benchmarking these new binders that we are developing. And so we looked at the literature and found a couple of different peptides and small molecules which were shown to bind to a particular receptor that's enriched in skeletal muscle and compared the activity of those to a lipophilic conjugate. The lipophilic conjugates, we know, are broadly distributed. They elicit knockdown in skeletal muscle, but we also see knockdown in other tissues. And we're really interested to see the activity profile here of small molecule 1.
If we look in the quadriceps and the bar graph on the right, we see really nice similar knockdown using SM1 compared to the lipid, but we see much better specificity when we look at the liver. We essentially see no activity with SM1, whereas the lipid conjugate is still knocking down the target transcript here, suggesting that we can get better specificity using this small molecule. We followed this up with a dose-response using 1, 3, and 9 mg/kg doses, and after a single IV administration, again, we're seeing robust knockdown in the quadriceps and gastrocnemius and limited knockdown in the liver. Importantly, this activity is durable. We can see sustained 75% knockdown in the quadriceps and gastrocnemius, whereas at two months, the transcript levels in the liver have essentially bounced back to baseline.
So to wrap up, I hope I've highlighted that there are several systematic efforts ongoing to identify not only new ligands for targeted delivery, but also new receptors. We've established key collaborations with industry partners, including PeptiDream, to access industry-leading technologies like mRNA display, again, to evaluate a wide range of targeting ligands and receptors. And lastly, from the data that we've highlighted today, we've been able to identify a peptide-based ligand for targeted delivery to skeletal muscle, which we will continue to develop over the months to come. So with that, thank you for the attention, and I'll pass it over to my colleague Paul for the next section.
Great. Thanks, Kevin, and good morning, everyone. I'm Paul Nioi, Vice President of Discovery and Translational Research here at Alnylam. So we're going to change gears a little bit now and talk about our human genetics efforts within Alnylam. And I want to start with somewhat of a sobering view of the pharmaceutical and biotech industry over the years. And I'm sure you're all very familiar with this, but there's a very high rate of failure of drugs. And in fact, the probability of going from phase one to approval is about 6% or 7%. So it's clearly a very, very challenging thing to do. And as we dissect the reasons for that, what tends to rise to the top is a lack of efficacy of the drug.
So in other words, in your clinical trial, you're targeting a certain protein, RNA, whatever it might be, and it doesn't actually work. The target is actually not involved in the disease in humans. And that's really the crux of the issue. As an industry, there have been a lot of challenges, and what I would posit is that the reason for this, again, is that the whole premise of a given target being involved in a disease in humans is often wrong. We've relied very heavily on contrived animal models of human diseases and in vitro data, and clearly, those do not represent the human population as a whole. And so our efforts in human genetics are very much focused on understanding disease mechanisms in humans. What are the variations that exist within the genome that put someone at lesser or greater risk of a given disease?
One lens that we can look at that through is human genetics. Again, I think you're probably all familiar with this, but just to reiterate that if you have human genetic evidence for your drug target, in other words, if you have a mutation in the gene that encodes your target and that associates with a clinical phenotype that is consistent with the disease that you want to treat, then you are more likely to go from Phase 1 to approval. In fact, a study by GSK several years ago showed that you're about twice as likely to be successful, and more recent data from AbbVie have shown that that number may be as high as five times more likely. There's a big impact because you know that that gene is involved in the disease in humans.
So at Alnylam, we have invested in human genetics, and we've been part of some very exciting efforts that you're going to hear a little bit more about in a moment, most notably the UK Biobank, which we joined four years ago now as part of a group of companies that were sequencing the exomes of all 500,000 participants in UK Biobank, and so we have all of that data. We have all of the clinical data to go with it, and we can understand, again, relationships between variation in the genome and risk of disease. We've also very recently joined another large effort in the UK called Our Future Health, where the goal there is to recruit 5 million individuals and, similar to UK Biobank, also have access to clinical data from that cohort.
And that's really going to be unprecedented in terms of its size and its scale, and we are a founding industry member of that effort. I do want to make one final point, which is that using human genetics to validate targets is not a new thing for Alnylam. In fact, it runs through essentially our entire pipeline and has done for many years. And I think that is one of the reasons that you see this reflected in our probability of success. I mentioned in my opening slide that the industry overall has had major challenges, but when you look at Alnylam's pipeline and our likelihood of going from Phase 1 to approval, it's very notable that our numbers are much higher than the industry average.
So what I'd like to do now is pass over to my colleague, Aimee Deaton, who is in our human genetics group, and she's going to give you a case study of a very exciting new target that's come out of these efforts. Aimee?
Thank you, Paul. Good morning, everyone. As Paul mentioned, I work in our human genetics group, where I'm really focused on finding new genetically validated targets that are suitable for our platform. And I'm going to take you through one really exciting example coming from our work in UK Biobank. So in UK Biobank, we have genetic information on nearly all participants, including exome sequencing data, which is a really rich source for looking at rare coding genetic variation with large impact on phenotypes. We also have health-related information on all the participants, including diseases, biomarkers, and other traits, many of which have causal relationships to disease. A very powerful method for looking at these data is to perform what we call gene-level tests, where we can aggregate rare loss of function and damaging variants in the same gene together.
This gives us statistical power to test association with kind of relevant health outcomes. Just one example coming out of this analysis is the association of INHBE loss of function and lower waist-to-hip ratio. This work was recently published in Nature Communications. Why look at Waist-to-Hip Ratio ? We know that abdominal obesity is the most prevalent manifestation of metabolic syndrome, which affects a huge segment of the adult population here in the U.S. We know that abdominal fat is a contributor to cardiovascular disease and metabolic risk beyond just what's conferred by BMI. We have, in Waist-to-Hip Ratio, corrected for BMI, a useful surrogate for this phenotype that correlates with direct imaging of abdominal fat.
In addition, there have been several studies using a technique called Mendelian Randomization, which show causal links between increased waist-to-hip ratio and increased risk of type 2 diabetes and coronary heart disease. This suggests that analyzing the genetics of Waist-to-Hip Ratio can identify new targets for cardiometabolic disease that, importantly, through changing abdominal fat, may have distinct mechanisms and be complementary to current therapies. I conducted gene-level tests for waist-to-hip ratio on predicted loss of function, damaging missense variants, and the two together across all genes in the genome in UK Biobank. The results are shown here in this Manhattan plot with all of the significant genes labeled. When we get these results, well, how do we decide what would be a good RNAi target? The first is to look for protective loss of function, so genes where loss of function associates with lower Waist-to-Hip Ratio.
All the genes meeting this criteria are highlighted here in red. The second thing, which is also very important, is to look for an absence of pathologic phenotypes, where we don't want some other unintended harmful consequence or association. When we looked at the results from this analysis, there were four genes that meet these criteria: INHBE, or Inhibin E, ACVR1C, PDE3B, and PLIN1. If we look at the expression of these genes, Inhibin E is the only one of these genes which is liver-enriched in terms of its expression, which is shown here on the right, and kind of stands out to the other genes which are all expressed in the adipose tissue. Obviously, we have a unique platform where we can deliver siRNA specifically to hepatocytes. We were really interested in this target as it may be ideal for our platform.
A key next step in terms of genetics is replicating this association of Activin E predictive loss of function with Waist-to-Hip Ratio in an independent cohort of people. We did this in collaboration with the AMP T2D-GENES Consortium . Then we further examined these carriers in UK Biobank. All these carriers are heterozygous loss-of-function carriers. We estimate that on average, they have a 50% decrease in Activin E. Strikingly, they have a favorable metabolic profile. In addition to this association with lower Waist-to-Hip Ratio, they have lower triglycerides and higher HDL cholesterol. They have lower alanine transaminase levels, which suggests better liver health, lower fasting glucose, and fewer metabolic syndrome traits. Importantly, we did not detect any associations suggesting adverse effects of Activin E predictive loss of function.
Consistent with this established relationship between Waist-to-Hip Ratio and cardiometabolic disease, if we look at our study, carriers of variants associating with lower Waist-to-Hip Ratio , such as Inhibin E, have fewer cases of heart disease and type 2 diabetes. That's what's shown on the plot below. And these effects on disease are proportional to these previous Mendelian Randomization studies. So what does Inhibin E encode for? It encodes for a TGF-beta superfamily member. The protein is called the Inhibin beta E subunit, which we think likely dimerizes to form a protein species called Activin E, which is secreted. The kind of processing of this is shown on the bottom of the slide. This protein is actually produced as a pro-protein, which dimerizes and then is processed into a mature protein, we think prior to receptor binding.
Kind of another important follow-up from this genetic result was to specifically examine these predicted loss-of-function variants that we see in UK Biobank and other cohorts. Very important for us that we believe loss-of-function is really protective, so that silencing via RNAi would also have beneficial effects. We did some in vitro experiments to characterize the three most common Inhibin E loss-of-function variants in our studies. To do this, we used HEK293 T cells, which don't endogenously express Inhibin E, and we overexpressed FLAG tag versions of the wild type or variant proteins. When we did this, we examined the protein that secreted into the media. Strikingly, all of these variants resulted in a kind of drastic reduction in Inhibin E protein by 90% or more. This really gave us confidence that these are true loss-of-function variants and that silencing Inhibin E would be beneficial.
So what do we think Inhibin E, Activin E, is doing? There's not a lot known about this particular protein. But based on other family members, we think that Activin E is secreted from the liver and then travels in circulation and likely binds to receptors on adipose tissue. The exact receptors and signaling pathways for Activin E are not understood. But interestingly, one candidate for a receptor is the type 1 Activin receptor ALK7. This is encoded by a gene called ACVR1C. And we think this is a likely candidate, partly because it's genetics phenocopy Inhibin E loss-of-function. So rare variants associate with decreased waist-to-hip ratio and protection from type 2 diabetes. This was seen in our rare variant study and has also been shown in published studies.
This association of Inhibin E loss-of-function with lower waist-to-hip ratio, favorable metabolic profile, really supports the potential of it to be evaluated as a novel therapeutic target for treating cardiometabolic disease. We know that abdominal obesity and metabolic syndrome impact more than 20% of adults worldwide. We are currently pursuing a development candidate for an RNAi therapeutic targeting Inhibin E using our IKARIA platform, which is going to allow long-term silencing of genes in the liver. Thank you very much for your attention.
Very well. Thank you, Aimee. Now we will move into the Q and A session. So please feel free to ask your questions in the chat or here. I'll read them, and we'll go with the panel here. So I see one question here. Is there an upper limit of number of conjugates like trimer or more? I'm assuming you're asking about the ligand itself. So as you know, with our GalNAc siRNA conjugates, we have a trivalent nature of this ligand. So there are three GalNAcs. Now, the nature of the ligand and the valency needed really depends on the binding affinity. So if you have a single GalNAc, a mono GalNAc, that doesn't have enough binding affinity to engage the ASGPR. And that's why you need multiples of those ligands.
In the cases that we are looking at with peptides and small molecules and other things, that would be in our mind, obviously, to look for the optimal binding affinity. It could happen with the monovalent ligand, but if needed, we can go to two or three of the valency. Again, it's really about the binding affinity. But based on the data that we are seeing and our experience with GalNAc-like, two to three should be sufficient. And the binding affinity is in the range that is needed to engage the target receptor. But beyond that, the higher affinity is not necessarily giving you the benefit. Again, really looking at the conjugates and their binding affinity to engage the receptor. All right. Let me look into the other question here.
So the question here is, will you be using IKARIA platform, and when do you expect to enter clinic? I think this might be related to Inhibin E. So Paul will get to you on this one. But before that, I would also just add that our IKARIA platform that we have announced earlier is with ALN-TTRsc04, and that's the first one. So moving forward, this is the kind of platform that we're using on the IKARIA design, which has a potential for annual dosing.
Yeah, absolutely, Vasant. And so to expand on the question a little bit, vis-à-vis Inhibin E, our preclinical work on the targets is ongoing. And DC is very likely next year in 2023. So that's our plan.
Perfect. And I think just to add, I mentioned about ALN-TTRsc04, which obviously is our kind of a follow-on molecule targeting TTR that is entering in clinic this year. So we're really looking forward to that with the potential for annual dosing. I think there's another question, which is about how is the PeptiDream collaboration going? When do we expect to have a development candidate for a drug targeting tissue other than liver and CNS? Great question. As Kevin sort of walked you through on the targeted delivery update, we are taking a very, very systematic approach for this. And we're not limited to just one type of ligand or one type of receptor. We have established multiple collaborations to go after different technologies that we can utilize. And one of them is obviously the PeptiDream.
We believe the PeptiDream technology is unique in finding the ligand that will be beneficial for the siRNA delivery. And so far, we are almost in our second year of collaboration. We're very pleased with the collaboration that has worked with the PeptiDream. A number of things that are coming out of that, and we'll obviously share more about it at the appropriate time. But at this moment, we can say that we can be more excited with the progress that we have done. In terms of the question on the development candidate, so our goal is to identify, optimize, and essentially finalize an optimal ligand receptor pair within next year or so, and hoping for the DC likely in 2024. All right. Let me take a look here. Another question that are coming up. And so Paul, this might be for you.
With this very exciting target, the Activin E, within the space of the indication of this, what are the potential targeted indications for Activin E, and which ones you are most interested in?
Yeah. Thanks for the question. It's a very exciting finding because it is the first genetic finding that really ties together multiple aspects of metabolic syndrome, and with that comes a lot of opportunity for us to really think about, as the question is alluding to, where would we go with this, so we are considering multiple indications at the moment in cardiometabolic diseases, so in general, things that are characterized by insulin resistance, hypertension, and greater excess abdominal fat would really make the most sense for an Inhibin E-targeted therapeutic, so that's where our thinking is at the moment.
Thank you, Paul. Let's look into another question that have come in. Does RNAi have any activity in the nucleus, and how does that inform how you are thinking about indication selection for your muscle platform? Very important question. Whether RNAi works in nucleus or not has been debated for a very long time. We obviously know that it works very well for the cytosolic target. But what is important to us is, rather than being dogmatic about whether it works in nucleus or not, it's really to take the target and design the siRNAs against it and see whether we are seeing the knockdown or not. And that's the approach that we will take in selecting the targets. And that's one part, obviously, the target needs to have the therapeutic opportunities that are suitable with our platform. All right. I will go with one more question here.
Other peptide approaches for the muscle, both linear and cyclic, that have now entered clinic seem to have some kidney tox that is charge-based. How are these peptides different? How do you think about potential target tox like this in kidney that may be based on the charge of peptides? Very important question. I think the charge of the peptide, specifically the positive charge of the peptide, is something to keep in mind. And I'm seeing that more from the experience on the CPPs, the self-penetrating peptides that people have tried to use before, and these cationic or positively charged peptides don't really keep their properties when attached to siRNA, the negatively charged siRNA. So this topic is definitely on our mind.
As we do with all our other things, we'll be doing thorough investigation of the potential safety of any of our ligand siRNA conjugates in the preclinical species before they can move forward. Paul, another question, just continuing on the Inhibin E. Which endpoints are you likely to measure to demonstrate efficacy for Inhibin in DC?
Thanks, Vasant, and thanks for the question. Yeah, this sort of follows on from the last question. So for us, what would make the most sense for Inhibin E would be looking at body fat distribution by MRI, with a particular focus on abdominal fat, and also insulin sensitivity indices. So those are some of the major endpoints that we would expect to characterize the pharmacological effects when we silence Inhibin E, consistent with what we're seeing from the genetic association. Registrational efficacy endpoints, on the other hand, will vary depending on the indication that we ultimately decide to pursue.
Perfect. So another question here. Can you please comment on the toxicity issue from Avidity's TFR siRNA conjugation? Any consideration on receptor selection on that side? Now, we can't really comment on Avidity's data here because we really haven't seen the whole thing there. But what we can say from our approach is we will be looking very, very carefully at this. We do think the receptors like TFR are important receptors worth investigating because they do have the ability to get into the tissues like muscle. And there is also potential to go into CNS with these kinds of receptors. That's obviously overcoming blood-brain barrier. That's a humongous barrier. But these kinds of receptors could help in that space as well. So we don't think that this news in a way links the receptor per se. And we will be looking at these and other receptors as such.
Question, Paul. So recently, there was a paper from Regeneron on Cardiovascular Disease Genetic Association and PNPLA3 being one of the factors. Could you please comment on that?
Yeah, thanks for the question. Regeneron have published several genetic associations over the last little while. I think the one that you might be referring to was a paper on HSD17B13, where they showed that carriers of loss of function in HSD17B13 were protected from NASH. And that effect was even greater in carriers of a PNPLA3 risk variant. So we actually are partnered with Regeneron on that target. And we've recently completed Phase 1. And we've highlighted some of that data in a recent press release. And so it's a good example of something that's gone from discovery from human genetics all the way now into the clinic, and obviously something we're hoping to do with Inhibin E.
Great. Thanks, Paul. So there's another question here. This might have been the follow-up of the discussion that we were doing on the ligand itself. I was talking about the binding affinity and the avidity. I want to make sure that the avidity I mentioned was having the ligands two or three within the construct. And it's just like in antibodies, having the two domains there obviously improves the avidity. So that's where the binding affinity comes from. I hope I answered your question. If not, please add back in the chat. Another question. From your small molecule conjugate experiment in skeletal muscle, it is clear that some of the siRNA is taken up by other tissues, that is, heart and liver. Is there any tox concern associated with drug accumulation in nonspecific tissues? Does that increase the potential for off-target effects? Again, very important question.
As you know, we have really done a pioneering work in the space of improving the specificity of the siRNAs. And that's the basis of our ESC+ platform to minimize the RISC-mediated off-target effect. And all of that is part of our IKARIA design platform. Now, the data that Kevin showed earlier with very good activity in muscle and some activity in heart and liver with these conjugates. So we think the toxicity issues the short answer is we don't think there are toxicity issues associated with getting into the heart and liver with our siRNAs. Obviously, these will have very high specificity to its intended target.
And then on-target activity, and that's where we'll be very careful about when we do the target selection, whether the target is playing any role within heart or liver in terms of any undesired effects if we really want to just have activity in muscle versus other tissues. I think there's another one, Paul, for you. Is Inhibin E partnered with Regeneron, or is this the wholly-owned Alnylam program?
Yeah. So Inhibin E is an Alnylam proprietary target. We are not partnered with Regeneron or anyone else on Inhibin E. As you probably know, we are partnered with Regeneron primarily in the CNS and the eye with a limited number of liver targets. But for certain liver targets, we retain the option to pursue and develop them independently, depending on our own internal strategic priorities.
Right. Another question here. And this relates to our ALN-APP program. Just as a reminder, we have another roundtable coming up on CNS delivery. But since the question is there, I'll just read it out and address it. If your initial knockdown data with ALN-APP do not prove as robust as you had hoped, would you pivot to a new molecule using a new targeted delivery approach other than C16? Now, we are very excited with our entry in clinical studies with the C16 conjugate as ALN-APP. It is too early to comment on this, on the PD effect of this molecule. We obviously are very pleased with the ALN-APP performance in non-human primates. We have shared that data. We have published that data.
And with a single dose and the duration of effect that we are seeing and our experience in liver, the correlation that we see for GalNAc siRNA conjugates from NHP to the humans is excellent. And we hope that something similar happens with our C16 platform as well in other tissues. So fingers crossed. We are also very eager to see the ALN-APP data. All right. I think there are a couple of questions here that might be more suited for the upcoming roundtables or the R&D Day that is in December. So please wait for that one because it obviously also needs the folks who are more associated with those programs and areas of expertise. Paul, with the Inhibin E, great biology and the progress so far. But ultimately, when do you expect to have a DC for Inhibin E?
Yeah. Thanks, Vasant. As I mentioned earlier, we are in the midst of all of our preclinical work, and we are hoping to have our DC next year. 2023 is the goal.
Great. I think we have addressed most of the questions that have come through in the chat, so it's time to wrap up the Q and A session, and I'll hand it over to Josh.
Great. Thanks so much, Vasant. Thanks to all of our speakers. Thank you all for joining us. This concludes today's roundtable. As always, you can access the replay of the webinar and download the slides on the Calendar section of Alnylam's website, and I also want to remind you that we do have one more upcoming roundtable in the series that will take place on Tuesday, November 1st, and we'll discuss our efforts in CNS delivery and, in particular, ALN-APP in development for the treatment of Alzheimer's disease and cerebral amyloid angiopathy. I'd also like to remind you that we'll be hosting a virtual R&D Day on December 15th. At this event, we'll do a deeper dive into other pipeline programs and platform activities, which will include advancements in our TTR franchise. Please save the date for December 15th, and we hope that you can join us.
All right. Thank you all for joining today. This concludes the event. Hope you all have a great day.