It will be available in the investor section of our website at www.wavelifesciences.com. Before we begin, I would like to remind you that management may be making forward-looking statements during today's presentation. The statements are subject to a number of risks and uncertainties that could cause our actual results to differ materially from those described in these forward-looking statements. The factors that could cause actual results to differ are discussed in our SEC filings, including our ended December 31, 2022, and our quarterly report on Form 10-Q for the quarter ended June 30, 2023. We undertake no obligation to update or revise any forward-looking statements for any reason. Today's agenda features members of the Wave management team, including Paul Bolno, President and CEO, Chandra Vargeese, Chief Technology Officer, Ken Longo, Vice President of Data Science, and Ginnie Yang, Senior Vice President.
Also joining us today is Carolyn Buser-Doepner, Vice President of GSK's Novel Human Genetics Research Unit, and we'll additionally hear from Tony Wood, Chief Scientific Officer at GSK. We are delighted to have Tony and Carolyn with us to speak to our collaboration. Following the presentations, all presenters from Wave, as well as Anne-Marie Li-Kwai-Cheung , Wave's Chief Development Officer, will be available for Q&A session. I'd now like to turn the call over to Paul.
Thanks, Kate. Good morning, and thank you for joining us for our annual R&D Day. Wave has undergone a significant evolution over the past decade. We began as a company focused on pioneering oligonucleotide stereochemistry, and since then, we have rapidly expanded our capabilities such that we can now leverage our best-in-class chemistry to address new areas of cell and disease. Today, we are at a truly pivotal inflection point for the company. We have multiple high-value clinical RNA medicine programs in DMD, HD, and AATD. We are leaders in RNA editing, with emerging leadership in RNAi, and we have strategic collaborations ongoing with GSK and Takeda. Our GSK collaboration kicked off at the start of the year, and we're very excited our partner is joining us today.
And most importantly, you will hear during our presentation that we are building an innovative, sustainable, and wholly owned pipeline powered by human genetics to support our growth over the next 10 years. While much of today's focus will be on our novel target identification and emerging preclinical data in RNA editing and siRNA, I would like to start by reviewing our clinical programs. As we translate in the clinic to programs with differentiated best-in-class potential, and we see extraordinary potential for AATD, our first human proof of mechanism for RNA editing as it enters the clinic. For DMD, from Part A of our clinical trial of WVE-N531 in exon 53 and amenable boys, clearly underscores the unique pharmacology that can be achieved.
Here, we compare tissue concentrations and exon skipping data from WVE-N531 on the right with clinical data generated with our first- generation non-PN compound, Suvodirsen, on the left. With only three biweekly doses of WVE-N531, we had more than 50 times greater muscle tissue concentrations compared with our first- generation chemistry, even though Suvodirsen's two weekly doses. While exon skipping was undetectable with Suvodirsen, we achieved the highest reported level of exon skipping ever achieved at 53% with WVE-N531. The half-life of WVE-N531 is approximately 25 days versus 18 hours with Suvodirsen, supporting the potential for monthly dosing. An outstanding question for all need to access myogenic stem cells, also known as satellite cells, which are important for potential muscle regeneration.
On the right-hand side of the slide, we are showing a new analysis from the patient biopsies in Part A of the Phase I/II study of WVE-N531. The brown dots in the nuclei on the screen are stem cells, and you will see many of them stained red, which is WVE-N531. This analysis shows one in these stem cells, which may have profound implications for muscle regeneration. These are the first clinical data in DMD to demonstrate uptake in satellite cells at this early time point and further support the potential differentiation of WVE-N531 from other therapeutics, including gene therapies. FORWARD 53, our Phase II potentially registrational study, is an open-label trial that assesses muscle biopsies after 24 and 48 weeks of treatment. We remain on track to report clinical data, including muscle biopsies, in 2024. Importantly, our vision extends beyond exon 53.
We are planning a broad multi-exon strategy, which we would accelerate following positive dystrophin data for WVE-N531 to build a wholly owned DMD franchise, up to 40% of the DMD population. These follow-on exon skipping compounds are all designed with PN chemistry, and you can see the high levels of exon skipping and dystrophin protein restoration from in vitro studies for exon 51, 44, and 52 on the slide. Turning to HD, WVE-003 is the most advanced and most promising of these. Last year, with just single doses of WVE-003, we demonstrated a 35% reduction in mutant huntingtin compared with placebo and preservation of wild-type HTT. The multi-dose portion of our SELECT-HD trials is ongoing and has been enrolling with high demand. Additionally, with each update from competitor pan-silencing programs, our conviction in wild-type sparing...
We now expect to deliver the complete multi-dose data from the first cohort with extended follow-up in the second quarter of 2024 to enable decision making. In addition to the update on single-dose and available multi-dose data in the second half of this year. Turning to WVE-006, which is our GalNAc-conjugated RNA editing candidate for AATD. Among the field, we continue to generate excitement for this first-ever RNA editing compound to enter the clinic, and is designed for RestorAATion of both healthy hepatic and pulmonary function. With a reversible and redosable therapeutic. Importantly, WVE-006 is not only the first of its kind, but it's also best in class in AATD, as supported by our robust preclinical data package. We can achieve remarkable potency and durability in these dosing because of our unique, fully chemically modified oligonucleotides.
WVE-006 is also compatible with GalNAc conjugation, a highly specific and elegant delivery tool that is well-validated through multiple approved silencing therapeutics on the market. For AATD, it is a significant advantage to have a stable and optimized candidate that can leverage GalNAc, and thereby avoid intravenous dosing. I'm excited to introduce you to the clinical development plan for WVE-006, called RestorAATion. The clinical development plan is comprised of RestorAATion-1 for healthy volunteers, as well as RestorAATion-2 for individuals with AATD who have the homozygous PiZZ mutation. These portions are interconnected, and together will enable us to deliver vision of M-AAT protein in serum by the most expeditious path possible.
Through the healthy volunteer cohorts, we can identify a dose and frequency that can most rapidly support a therapeutic effect in PiZZ patients, and rapidly completing volunteer cohorts will enable the initiation of patient cohorts at optimized dose levels. Because there are multiple assessments of cohorts, it is possible to achieve proof of mechanism before the completion of the cohort or the whole study. Thus, the RestorAATion program will provide a highly efficient path to proof of mechanism. Clinical startup activities are well underway, and we expect to initiate dosing in healthy volunteers in the fourth quarter of 2023, and deliver proof-of-mechanism data in individuals with AATD in 2024. A compelling market opportunity. For DMD and HD, we are poised to unlock even bigger market value with the follow-on exons and/or additional SNP-targeting compounds.
Altogether, these three diseases indications represent approximately $12 billion in total addressable market value in the U.S., and we expect meaningful clinical data updates on all three programs in 2024. Programs have validated our proprietary best-in-class chemistry, and we are building the next generation of Wave programs by using this chemistry to unlock novel areas of disease biology, and advance first-in-class programs. We're doing this by accessing new endogenous enzymes, such as ADAR, to open new modalities like RNA editing, using all of our modalities, genetic insights, and putting prevalent diseases. I will walk through the opportunities of these capabilities, and our presenters today will follow with data that demonstrate how we are actively building our pipeline. With RNA editing, we are using short, single-stranded oligonucleotides called AIMers, to harness the endogenous ADAR enzyme to make a single RNA base edit.
We are the leaders in this space. Correct monogenic diseases by restoring or correcting protein functions such as AATD. We can take these same editing principles and apply them to larger diseases by adding RNA to upregulate or increase the stability of the mRNA transcript, thereby increasing endogenous protein production. Importantly, by using editing for upregulation, we are able to address disease and increase levels of endogenous protein with therapeutic potential. Chandra will discuss this more later on. While monogenic diseases carry high unmet need, they are often associated with smaller patient populations. Having unlocked the ability to upregulate endogenous proteins, we now have another tool to address prevalent diseases and offer treatment opportunities to larger oligonucleotide chemistry, investments have continued to flow into large genome-wide association studies.
These studies have unlocked new genetic insights that are being used to identify high-impact disease targets, creating an incredible opportunity for RNA medicines. Looking at this figure, we see a significant increase in identified genetic variants, particularly for patients of the UK Biobank project. GSK is at the forefront of investing in genetic discovery, and as you'll hear from Carolyn on this call, through our strategic collaboration, and we, as we demonstrate today, we are benefiting from their novel genetic insights. Building for the future, we also have access to the UK Biobank and are advancing our own internal data sets. Together, these research, partnered and fully owned opportunities. As one looks closer at these data sets, there is clear evidence that the majority of disease targets require more of something, meaning therapeutic approaches that will restore, upregulate, or fix proteins to improve patient outcomes.
These targets are not readily assessed with silencing approaches, but Wave is uniquely positioned to address them. RNA is highly regulated, and by understanding and mapping these different regulation drivers, we have potential to address a substantial number of diseases. Later in the presentation, Ken Longo, Wave's Vice President of Data Science, will shed light on the Editverse, which is the editable gene disease universe and all its therapeutic possibilities. Today, the things we can target with our AIMers. As we continue to evolve our data science models and generate new platform learnings, we expect this universe to continue to expand, and we believe we can target 50% of the transcriptome. Later in the presentation, our team will walk you through how we are uniquely defining, mapping, and identifying targets within the Editverse. Expect to advance five new clinical candidates by year-end 2025.
We are prioritizing high-value targets across all our modalities, including siRNA, and Chandra will speak to our first siRNA program, INHBE later in the presentation. The majority of these new candidates, however, are expected to be for protein upregulation or restoration, and Ginnie will cover in the RNA editing space later on. This strategy for growth also intends to add multiple pipeline programs for prevalent indications. Nowhere is the opportunity for novel biology and validated chemistry more clear than with our AATD program. Our collaboration with GSK puts us in a strong position to execute on bringing this novel therapeutic option to market, given development and commercialization. As a quick reminder, under the terms of our collaboration, Wave is eligible to receive substantial development, launch, and sales milestone payments per OO6, including meaningful near-term clinical milestones as well as significant royalties.
Now, it is my pleasure to introduce Tony Wood, Chief Scientific Officer of GSK, and Dr. Carolyn Buser-Doepner, Human Genetics Research Unit at GSK. They have joined us today to share their perspective on our strategic collaboration, which aims to discover and develop transformative oligonucleotide therapeutics. I'll now turn the presentation over to Tony.
Hi, everyone. We're living in an incredibly exciting time where science and technology are joining forces like never before. In my 30 years, I've never witnessed such remarkable advances in our understanding of human biology and the incredible possibilities they bring to unite science and tech together to get ahead of disease. Right now, we find ourselves in an inflection point with RNA and its role in broader biology. Take oligonucleotides, for example. These tiny molecules have mainly been used to target rare diseases, but including more prevalent ones. We're committed to leveraging genetics and genomics to support our pipeline, understanding that therapeutic targets with genetic evidence are more than twice as likely to become medicines. Currently, at GSK, over 70% of our pipeline is backed by human genetic evidence. However, traditional modalities to target disease are also facing challenges.
As much as 50% of new drug targets are considered undruggable with these traditional approaches. That's why we're focusing on alternative modalities like oligonucleotides, to tackle these previously undruggable areas. This, to me, is where Wave Life Sciences comes in. We've joined forces with Wave because they have a best-in-class platform called PRISM. It's truly impressive. First, their platform has enabled leading multimodal platform with three different RNA targeting molecules. That includes RNA editing, splicing, and silencing capabilities. Most platforms focus on just one or two of these. We're especially excited about the new modality of RNA editing that Wave has pioneered and the possibilities presented by GSK and Wave working together on this area. We see great potential in making oligonucleotides a mainstream modality. Together, we're aiming to expand the use of oligos into more prevalent diseases and scale them for larger patient populations, those once thought undruggable.
Now, I'll hand over to Carolyn Buser-Doepner, Vice President of GSK's Novel Human Genetics Research Unit. She'll shed more light on how we're going after the oligonucleotides and how Wave fits into and supports our strategy. Thanks very much.
Thank you, Tony. I'm excited to be participating today from the Wave Life Sciences office. Since 2018, I've been leading a research unit in the identification, selection, and progression of genetically associated targets of high unmet need. My background is actually in oncology, where the application of somatic or tumor-specific genetics has led to a number of successful targeted therapies. In my current position, we are applying a similar approach using germline genetics. They're mostly inherited genetics. To then identify targets with nucleic acid therapeutics. During a former role in oncology, when I was working closely with my colleague, now Chandra, on targeting hepatocellular cancer with LNP-encapsulated siRNA. The field of oligonucleotide therapeutics has advanced significantly since that time, and I will briefly share why we at GSK are so excited for moving into antisense, siRNA, and ADAR editing to modulate a large number of genetically associated targets.
So let's take a step back. Despite significant medical advances, there are still many diseases with very high unmet needs. 5% of the global population has NASH, a chronic and 5%-8% of the population over the age of 60 will have dementia. Both of these diseases are now seeing clinical trials using oligonucleotide therapies. This high unmet need is, of course, the driver behind the pharmaceutical and life sciences biotech industry. However, the sobering fact is that drug candidates that enter phase I trials fail to become medicine. In fact, a retrospective review of trials in the time period of 2015-2019 shows that 90% of drug candidates that enter phase I fail. This high attrition rate is really bad news for patients. It's a challenge to our industry. So we ask ourselves, why this high attrition rate? Why this failure rate?
Actually, the majority of drugs fail due to lack of efficacy. Basically, we've selected the wrong target to modify these pathogenesis. So why this faulty selection? Again, a number of reasons can be cited, including that we have used non-predictive animal models in our preclinical research, so we need to work on more human physiologically relevant systems, and/or the selection of the target is based on correlation to ongoing disease rather than causation for getting the disease. Several papers came in 2015 that provided evidence that drugs acting on targets for which there was genetic evidence associating the target to the given disease, that these targets were more likely to become medicine. In fact, shown here is an updated figure from a seminal publication from GSK, showing that the strength of genetic likelihood of becoming a medicine.
The strongest evidence comes from loss of function, pLoF , and rare disease variants. You can see this on the x-axis, showing the probability of success to become a medicine. In fact, when one can confidently assign the genetic evidence to a given gene, then targeting that drug or the product twofold more likely or higher to become a medicine. However, also shown on this plot, the size of the box in each one of these rows reflects the size of the target opportunity. So you can quickly see that the, actually, the greatest target opportunity are in genes where we think we have genetic evidence, but it's complicated, where of the genetic variation to that specific gene. Let me give you an example. Shown on the top left-hand side are the results from a Genome-Wide Association Study, or GWAS, that was performed against coronary artery disease.
Here we're zooming in on Chromosome 2, and we find two CAD, two coronary artery disease. However, when we look at that position of that variant, we quickly see that there are a number of genes that are expressed in this region. So it's not straightforward to assign the variant to a specific gene. It turns out, in the minority of cases, it is super clear. The variant, in fact, it changes the amino acid code of what is translated into a protein. Far more often, we find that the variant either sits in a gene-rich portion, as is shown in this slide, or in a gene-poor region or desert region, and that's where we call. We have to make a call to the next nearest gene. We have to do more experimental work.
In order to address these complexities of making these assignments, GSK has invented, invested heavily into three approaches. The first is human genetics, where we have access to multiple and diverse databases such as UK Biobank, FinnGen, and Genes & Health. Of course, we've also worked... The second area is functional genomics, where we're using editing technologies such as CRISPR to test hypotheses experimentally on human-derived cells, iPSC-type cells. And here we're leveraging both internal expertise as well as several external collaborations, such as the Laboratory for Genomics Research at UCSF, Broad, and others. AI/ML is taking all that data, the genetic data, the genomic data, and this is an exponentially growing data set, both through internal and, of course, external research.
And here the goal is to develop predictive algorithms to help us with that assignment of taking a variant, assigning it to a gene function, which we call Variant to Gene to Function. Now, these scaled approaches are identifying a large number of novel genetic signals, of which some map to the human proteome, about 20,000 genes, and many more map to the regulome. The regulome are things that include long non-coding RNA or endogenous. In terms of the proteome, oligonucleotides are really emerging as a third modality, and it allows us to go after targets that are difficult or perhaps impossible to target using our standard modalities of monoclonal antibodies and small molecules. In terms of targeting the larger regulome, we foresee that oligonucleotides will be the key modality. Okay, so far, I have mostly focused on genetics and genomics to identify target indication hypotheses.
Of course, target selection and progression is multifactorial and requires a deep understanding of biology, clinical insight, and feasibility. Now, using the described target engine, we have significantly paid with genetically associated targets and built a robust approach to perform Variant-to-Gene-to-Function studies. Through our collaboration with Wave, we can now access more of that genetically associated target space with oligonucleotide as a modality. One such example I'll share with us momentarily. We independently found INHB E through our genetic analyses, and we're particularly excited by its seemingly clean profile in PheWAS. And what I mean there is that it's not associated with detrimental phenotypes. So in summary, AI/ ML approaches are identifying a large number of genetically associated targets, of which a subset may be more readily modulated by oligonucleotides.
As Tony has already highlighted, we were attracted to Wave specifically because of their advanced chemistry and versatile PRISM platform that supports the silencing and precision editing of targets. I'm really excited to be able to say that we now have examples in each one of those categories of the PRISM platform, with our most advanced collaboration, as Paul has already highlighted, around WVE-006, a first-in-class RNA editing therapeutic for the treatment of alpha-1 antitrypsin.
Thank you, Carolyn. I'll start by thanking our speakers and GSK for their remarks and echo their enthusiasm for all that we have achieved together in a short amount of time. The evolution of Wave's platform and capabilities is clearly visible in our publications, which cover all four of our modalities. Earlier this year, we added RNAi to this list. As you heard from GSK, this modality features prominently in our collaboration. I'm excited today to introduce our first RNAi program. But first, let's start with the highlights on why our siRNAs have the potential to be best in class. Our proprietary PN chemistry has a significant impact on potency and durability of RNA-mediated effects, are attributable to our extensive siRNAs and understanding of how best to deploy PN chemistry in this modality. The result has been unprecedented increase in Ago2 loading.
Our recent NAR paper highlighted our best-in-class capabilities using GalNAc siRNA targeting HSD. Again, efficacies are driven by increase in Ago2 loading and not tissue exposure. Combining our RNAi capabilities and novel genetic insights, access to our GSK collaboration, I'm excited to announce our first GalNAc siRNA program targeting INHB E for the treatment of metabolic disorders, including obesity. Wholly owned program to emerge from GSK collaboration. There is strong genetic evidence supporting this target as carriers of heterozygous loss-of-function variants of INHB E gene exhibit several beneficial traits, including reduced waist-to-hip circumference, reduced risk for type 2 diabetes and coronary artery disease, and INHB E by 50% or more with siRNA is expected to restore a healthy metabolic profile. INHB E is expressed primarily in liver, meaning we can rely on clinically proven GalNAc conjugates for targeted delivery of siRNA.
Additionally, levels of INHBE protein and other relevant clinical biomarkers test to assess target engagement and clinical efficacy in a relatively short period of time. INHBE siRNA is poised to evolve the treatment landscape for metabolic diseases, including obesity. Approximately 47 million people in the US and Europe have metabolic disorders. Metabolic syndrome is associated with diabetes and cardiovascular diseases, as well as increased risk of mortality. There is a high unmet need for therapeutic options beyond GLP-1's. These treatments lead to weight loss at the expense of muscle mass, suppress the general reward system, and are associated with poor tolerability profiles and discontinuation rates as... So a therapeutic approach for obesity that improves metabolism, increases fat loss, maintains muscle mass, and does not affect the general reward system would be ideal.
We assess the target engagement in vitro with a species cross-reactive sequence using an early GalNAc siRNA design. We observed much with this siRNA, with a maximal 90% non-targeting human system. This means that we would expect to see much higher potency in humans than that we observed in mice with this sequence. We then moved to proof of concept study in vivo in young diet-induced obesity or DIO mice, to evaluate if INHB E silence visceral fat. After five weeks, we saw 62% INHBE silencing, exceeding the therapeutic threshold. We then looked at body weight of these mice over the time and saw a 15% lower body weight as compared to PBS after five weeks. A similar effect size was reported for semaglutide in a preclinical study. We looked to see how body weight changes were reflected in different types of adipose tissues.
We wanted to see removal of excess fat in the white adipose tissue, similar to levels in lean chow-fed mice, which is exactly what we saw. We saw substantial reduction in visceral fat tissues like mesenteric and epididymal fat. In a subsequent study, longer duration study, we followed DIO mice out to 8 weeks. Encouragingly, the visceral fat loss deepened over time in multiple tissues, and all tissues were in line with the lean chow-fed mice. To our knowledge, this is the first demonstration of siRNA treatment restoring a healthy phenotype with in-... We continue to improve our GalNAc siRNA designs, and our next ASO generation firmly has best-in-class potential.
On this slide, with our next generation siRNA, shown in dark blue, we have further improved the potency and duration of silencing over our best published design, shown in light blue, and the benchmark on advanced ESC chemistry. As a reminder, translation from preclinical experiments to the clinic is well understood for RNAi, and we expect our next generation siRNA formats may support biannual or annual dose. We are currently applying this new chemistry format to INHBE, and we expect to select a clinical candidate. As you heard from Carolyn, RNAi is also one of the multiple modalities that we are pursuing with GSK. As noted in my last slide, we aim to continuously improve our chemical design through PRISM. As we have shown through multiple publications across modalities, our PN modification, our versatile modification, and the enhancement we observe can be tuned with variation of this modification.
We call them as PN variants. We can take advantage of this versatility to introduce properties such as increased lipophilicity, which are advantages for delivery, especially to cell types beyond hepatocytes. This, that we can improve silencing in tissues associated with metabolic disease, including liver and adipose tissue, by changing from a standard PN to a PN variant in an siRNA. As a reminder, these are non-GalNAc conjugated siRNA, and they are single dose. With these siRNA modified with PN variants, we have often in muscle tissues, including heart and diaphragm. We have applied a similar approach to expand RNAi-mediated silencing in the CNS.
We can achieve potent and sustained silencing with a single dose, with greater than 75% reduction in APP transcript across all the regions of the brain through the end of the study, of which a tremendous advancement to the data recently published by Alnylam of the same target and same route of administration. On this slide, we show evidence confirming that APP transcript silencing leads to a striking reduction of APP protein across brain regions eight weeks following a single dose, which is hardly visible after siRNA treatment in the bottom row. In summary, we have demonstrated best-in-class RNAi-mediated silencing. We are advancing our first in human E GalNAc siRNA program with a clinical candidate expected by the fourth quarter of 2024. We will continue to take advantage.
And we will continue to expand tissues and targets amenable to siRNA to support our partnership with GSK. Now, turning to RNA editing. We first unveiled proof of concept experiments supporting our RNA editing capabilities in our Nature Biotechnology publication in early 2022. As with our other modalities, the opportunity is amenable to this emerging modality. We continuously expanding our editing capabilities, and this year we shared how proprietary base modifications have improved our next generation AIMers. This slide provides an overview of how one of the base modifications, the N3U modification, expands the budget for editing. The N3U modification consistently improves RNA editing levels across sequences, even those where our original designs were not effective. This makes base modifications are incorporated in WVE-006 and other editing compounds you will hear about in a moment.
Earlier, Paul shared how correct protein functions have enabled the AATD program. We have also invested in expanding the application for AIMers through modulation of protein-protein interactions, or PPI, and upregulation of protein expression. We have publicly presented on our PPI capabilities throughout the past year. Today, our team looks to up-regulate protein expression by editing RNA. The production, processing, stability, and degradation of RNA is highly regulated, creating ample opportunity for us to intervene with the AIMers to change the amount of protein a transcript can encode. One mechanism for regulation relies on interaction between a transcript that promotes the degradation of the transcript. In this scenario, we dial up protein expression with catalytic efficiency using AIMers. By developing AIMers that edit the sequence motif in the mRNA, that enables the protein RNA interactions, we can disrupt the interaction between the RNA and the RNA-binding protein, mRNA.
They can decrease protein binding and durably stabilize the mRNA. The stable mRNA will produce more protein simply because it's around for longer. As with the RNA editing, the resulting protein production should be dose dependent. Now, here we demonstrate in vivo proof of concept for the application of AIMer. Moving from left to right, we demonstrate over 75% RNA editing of the target. This leads to greater than twofold upregulation of the mRNA, and ultimately an increase in protein expression. With this, now I'll turn the call over to Ken Longo, Wave's VP of Data Science, to discuss how we are leveraging.
Chandra, and good morning to everyone listening in. I lead Wave's data science team in designing and building our machine learning capabilities. That includes our own proprietary knowledge models, which provide important insights into the genetic drivers of human diseases. Today, I'll cover the Editverse, a term which we use to broadly define the editable gene disease possibility. All genes where an A- to- G edit is predicted to impact transcript levels, and two, all genes for which there is a therapeutic rationale for transcriptional upregulation. Notably, upregulation offers the potential to address multiple pathogenic mutations in a gene with a single therapy. As you will see, the AIMer target... And today, we'll walk you through several examples of gene disease networks or galaxies within the Editverse that have the potential to drive new therapeutic programs for Wave.
As Paul discussed earlier, recent computational work from Princeton and the Flatiron Institute has shown that about 80% of pathogenic variants are associated with a clear opportunity space in regards to RNA stabilization and upregulation as a therapeutic rationale for many diseases. To efficiently design our AIMers, we built the model to accelerate identification of adenosines likely to modulate the transcript with high confidence. We trained and validated a deep learning model using known variant examples. This model predicts the impact of editing on mRNA transcript levels. Our model achieves good accuracy at predicting known mutation sites, but of equal importance, it also discovers novel edit sites. It confidently predicts these changes for over 50% of the proteome. As the figure on the right shows, there is a good correlation between the blue eQTLs that the model never saw during training.
The deep learning model allows us to rapidly and efficiently identify AIMer-mediated upregulation opportunities. On this slide, you can see a typical eQTL prediction score trace for bases in an undisclosed gene. With potential AIMer targetable adenosines, higher scores identify locations where an A-to- G edit is predicted to impact transcript levels. For our first example, we'll zoom into a network for hyperlipidemia and energy intake. This knowledge graph connects concepts such as genes, diseases, and pathways, and is based on multi-evidentiary support, including GWAS studies. Zooming into this network, gene nodes, which are each AIMer editable targets. Now I'll turn the presentation over to Ginnie, who will speak to how we are using these proprietary models to rapidly identify drug targets, as well as design AIMers for upregulating those targets.
Thanks, Ken. I joined Wave earlier this year as the SVP of Translational Medicine. Career at companies such as Bayer, Novo Nordisk, AstraZeneca, and Eli Lilly, applying traditional and novel modalities to bring medicines with disease-modifying potential to patients. With highly differentiated RNA therapeutic capabilities, especially in RNA editing field. After four months at Wave, my team has grown even stronger. I'm excited to be a part of today's presentation, to share a broader view of the potential of RNA editing therapeutics to benefit millions of patients who are in need of better medicines. Now, I will share how we are expanding our pipeline by another target from our deep learning model. As we zoom into the network, it reveals Target A, an undisclosed metabolic target that is uniquely suited for AIMer upregulation.
We initially investigated this target to demonstrate the power of RNA editing, to upregulate our mRNA and increase and ultimately improve impact functional outcomes in a preclinical model. This will provide us with confidence to expand to other disease targets. Target A also represents a large patient population of 90 million with metabolic syndrome and obesity. Up-regulation of Target A increases protein is secreted in the blood. The ability to measure this protein serum levels means that there is a biomarker readily available for efficient clinical development. Our model correctly predicted an AIMer for editing and upregulation. We achieve over two-fold mRNA upregulation and a similar degree increase in endogenous protein production in liver. Next, we look to assess the impact of this protein increase on the phenotype of a diet-induced obesity or DIO mouse model.
You can see here that the AIMer treated has significantly lower body weight as compared to PBS-treated control mice. Clearly, the data suggests that this degree of endogenous protein upregulation is sufficient to induce a healthier metabolic phenotype, including body weight lowering. This result provides the first in vivo caloric phenotype. We also look at fasting glucose and fasting insulin levels in this mouse. We showed that the AIMer-treated DIO mice displayed suppression of fasting glucose and a profound reduction of fasting insulin, indicating substantially improved insulin sensitivity in obese mice. Clearly shows that potential for this ADAR-mediated upregulate, upregulation approach. Now I will stay within the same disease subnetwork and move to target B. Upregulation of this target offers a first-in-class therapeutic approach for hyperlipidemia. As with target A, the B endogenous protein will impact millions of patients with hyperlipidemia with a unique approach.
There are also serum biomarkers available to regularly assess the target engagement and efficacy. Our proprietary modeling data suggests that over two-fold upregulation of target B mRNA will deliver clinically meaningful benefits or model. We designed an AIMer to edit target B in human primary hepatocytes. As shown in the figure here, with over 70% editing, we are demonstrating more than 2-fold upregulation of target B mRNA, which leads to a significant increase in protein production in hepatocyte. Now we move to a renal disease subnetwork, but continue with targets that can be accessed through GalNAc delivery to hepatocyte. We call this target X, which produces a secreted protein in liver to treat kidney disease. This condition is associated with early mortality, significant economic burden, and we estimate patients in the US and Europe who could be addressed with our approach.
There is a strong therapeutic rationale for target X, as supported by genetic insights, PheWAS, and observational data, indicating target upregulation can stop kidney functional decline. Plasma biomarker assessment and our modeling indicated approximately two-fold upregulation in secreted protein will be clinically meaningful. Based on our existing data set, we view this level of upregulation as readily achievable, and we are working toward proof of concept data. Building on the success we have seen, as another opportunity to correct disease-causing mutations with RNA editing. The medical need, the nature of pathology, and the mechanism driving the pathology are well understood, and there is a fully translatable serum biomarker to support clinical development. RNA, RNA correction of 15%-30%. As illustrated by the figure, we have achieved over 60% correction of target E in vitro.
As Chandra discussed with RNAi, our chemistry enables us to distribute RNA oligos to a broad array of tissues. I will now share in heart diseases that are part of lung and kidney disease zones. In clinic, with a single dose, we demonstrated approximately 40% editing of ACTB in non-human primates and over 60% editing of the UGP2 target with our next generation AIMers in mice. Solution to kidney is supported by the histology image on the right side of the slide, demonstrating substantial presence of our oligos in the proximal and distal convoluted tubule. We now return to the renal insufficiency subnetwork, but look at target F for a rare genetic kidney disease. Our approach demonstrated to restore kidney function. The unmet medical need in end-stage renal disease is well understood, and the patient population is well defined.
Available clinical and molecular data demonstrate that two-fold upregulation of the target will deliver clinically meaningful benefits. You can see on the left side of the slide, we have achieved over two-fold upregulation of target F in human kidney tubular epithelial cells following RNA editing with two different AIMers. Turning to lung, we have tested our ability to edit mRNA levels. With our early AIMer design, but with a single dose, we've achieved approximately 10% editing of ACTB in the lung of non-human primates. Histology images demonstrated accumulation of our targeting oligos in bronchial epithelial cells. As Chandra shared earlier, with generation AIMers, we have achieved over 35% editing of UGP2 in the lungs of mice. Again, we turn to an opportunity to expand upon our work with AATD to advance an RNA correction approach for genetic lung disease with target Indication
disease derive very little benefit from available therapies, and molecular mechanism is well understood. Available data from human genetics suggests that correction of approximately 20% of the mutated mRNA would deliver clinically meaningful benefits, and we have achieved approximately 70% editing. Let me conclude with a few points. The Editverse accessible to Wave AIMer is very substantial and still expanding. We are advancing our work for a diverse set of targets, addressing areas of high unmet medical needs, including AATD. I will now turn the call back to Paul.
Thanks, Ginnie. Stepping back, we are in an exciting position with clinical and preclinical programs spanning multiple modalities. Going forward, our strategy for driving pipeline growth is to prioritize high-value targets backed by novel genetic insights that can leverage predictive clinical proof of concepts. This includes our first siRNA program, INHBE, that emerged through our GSK collaboration. We will continue to expand the pipeline beyond AATD by unlocking new RNA editing targets using our proprietary deep learning model, as well as through access to unique genetic insights, including through the UK Biobank. We are well poised to sustainably deliver transformational. We expect five new clinical candidates by year-end 2025. The first of these programs is expected to be our GalNAc siRNA INHBE program.
We're also advancing multiple high-value RNA editing targets, which will follow on the success of our WVE-006 editing program and further extend our leadership in this space. Any combination of these targets may support our 5x 25. With these additional 5 programs, Wave is poised to unlock significant value for patients and for shareholders, with fivefold growth in our total addressable market opportunity. Looking ahead to 2024, we expect to deliver several key data sets, including DMD, HD, and proof of mechanism data for AATD, as well as continued progress on our GSK collaboration. We also anticipate providing additional updates on this and our other novel targets next year. With that, I'd now like to begin the Q&A portion of our program.
Thank you, Paul. All Wave speakers will be participating in this portion of the event, and I'll also remind you that Anne-Marie is available for questions as well. I will be moderating the Q&A session and asking the questions to the Wave team. As a reminder, if you'd like to ask you several questions, so I'll get started. So we'll start with a question to Anne-Marie. Operator, can you hear us?
Yes, we've got you now. Thanks, Alicia.
No, Operator, can you hear us?
So now it's me. No.
No, nothing. Yep, they can.
We can hear?
I think we can.
Yeah. I'm gonna start just 'cause I'm not sure where the audio cut off. I'll reread the question, and we'll start again. On our AATD trial design on slide 12, at which dosage levels do you start to hit the therapeutic window? At which dosage levels of WVE-006 should we expect to get data in 2024?
Thanks, Kate. So our RestorAATion-1 and RestorAATion-2 studies have been really thoughtfully designed so that we can most efficiently generate M-AAT, the wild type protein. Through the volunteer study, we can rapidly move to a therapeutically active dose in patients. And then what's really neat is we have designed the study such that we can measure and detect M-AAT pro-protein on an ongoing basis. Therefore, the proof of mechanism can be detected at any time during the-
AATD? Will initial proof of concept data, including assessment of neutrophil elastase or other indicators that WVE-006 could differentiate from RNAi by impacting lung phenotypes be included?
Yes. So in addition to measuring M-AAT, the wild-type protein, which will demonstrate that we have successfully edited and restored an M-like phenotype, we have this wild-type protein using the neutrophil elastase assay.
Okay. Next question, we're going to go to INHBE. Is your INHBE clinical candidate going to leverage GalNAc?
I think the very, very short answer to that is absolutely. I think, you know, one of the advantages we're seeing, as you saw today with Chandra's data, not just on the power of GalNAc delivery, but also gives us the potential for much more infrequent dosing. You know, we believe those two ingredients, Wave chemistry coupled with GalNAc, opens up the opportunity for a best-in-class INHBE siRNA silencing approach.
As you think outside the liver, how are you thinking about purely chemistry modifications versus leveraging different conjugates, more or less accessible via chemistry alone?
Which you learned a little bit about today, I think chemistry, and I think we use chemistry in a very broad context of chemical modifications, as Chandra was talking about, with new variants of PN are giving us the ability to select various cell types. And so you saw that in the distribution at low doses. I don't think this forgoes, and we have a lot of discussions about it, around, you know, one of the advantage, driving delivery to specific cell types using active receptors. And again, it's one of the advantages we have in collaborating with GSK, is we think broadly about the genetic universe of targets and tissues with which we want to explore.
Excellent. So going back to Anne-Marie, what kind of translation for exon skipping to dystrophin expression would you expect to see in Exon 53 patients?
Well, skipping produces more dystrophin, and it's also clear there's a delay between exon skipping and dystrophin. Golodirsen showed 35% skipping at 12 weeks, and this resulted in 1% dystrophin. Our study is powered to show more than 5% dystrophin at 24 weeks, and we'll continue the study through 1 year to further enable an estimate of the longer term dystrophin production. We would expect a model. We saw an impact both on survival and muscle and respiratory function, and higher concentrations of WVE-N531 in non-human primate heart and diaphragm, compared with skeletal muscle.
So I think we could step back, too, and just think more broadly about treatments for DMD. So I think that's kind of the question, as I, as I'm interpreting it, and really referencing it, is existing oligonucleotides and how they've been approaching the disease about treating DMD. It is a multi-muscular disease, skeletal, muscle, heart, and diaphragm. And the approach we took from the very beginning of this was broad distribution across multiple muscle tissues, broad exposure to the nuclei of multiple muscle cells, including, as we shared today for the first time, satellite muscle cells. So we think about the regenerative potential of how to actually bring the myofiber, being able to not just get into skeletal myoblasts, but being able to get into the satellite cells themselves, help drive the regenerative potential of muscle.
Which over time, as we think about dystrophin as a function of time, should yield not just higher levels of protein, but I know we're always focused on this, you know, what is the percentage? But if we're really focused on what is the benefit of dystrophin replacement therapy, how do we think about. We demonstrated return to wild-type and respiratory function with tidal volume, so the rescuing of skeletal muscle in our preclinical models. So we do have to think back about the treatment of this disease. As Anne-Marie said, we do expect, so this is not saying we don't expect to be greater than 5%, and, you know, hopefully a lot more. We also have to think about those kinetics.
So in the question, you know, I recognize saying numbers from Golodirsen, where it's about 35% at that 12-week time point, and looking at dystrophin as a cellular machinery that needs to start over time. At 6 weeks, we were already 53%, so that machinery started earlier, and it's continuing over the longer course, given the substantial amount of muscle distribution of muscle. So again, a lot of this yields our confidence and conviction based on our preclinical data in models where they don't have dystrophin. You know, we anticipate seeing substantial amount of dystrophin, but most importantly for our DMD community, is that we expect this to translate to hopefully benefit in the function.
Excellent. So I'm gonna go to the INHBE again. Can you walk us through the key differences of Wave's INHBE program and competitors?
You know, I look at my colleagues, and I'm sure Chandra and Ginnie will have a lot to jump in around this. But, you know, I think about when we talk about competitors in INHBE. I think we think about first and foremost is our partnership. So if we think about our partnership with GSK, that began very much at the beginning of this year, as we said, when we announced the collaboration only this year, at the beginning of this year, that GSK was coming to the unique genetic insights on targets. So not just accessing databases, but really bringing deep genetic insights into understanding genetic targets, to, as you heard Carolyn say earlier, increase the probability of success in translating therapeutic outcomes.
So based on those genetic insights, we're excited about kind of the starting point with where we're beginning the program to say we're starting on a really firm foundation of biology. I think when one couples this, and we've seen several publications over the last year identifying this as a potential target that should be explored. I think what's exciting about the data that Chandra presented today is these are the first data demonstrating the translation of human clinical genetics into animal models. I mean, I do think it's a bit remarkable when we think about, we talk oftentimes about these clinical genetics, that in this case, how that's gonna ultimately translate, which is very important as we look about the human genetics here. So ultimately clinical benefit to these patients.
We forget that obesity is a public health epidemic with multiple complications in cardiovascular disease, stroke, cancer, and other diseases like type 2 diabetes. So when we think about the importance of actually doing something, like changing, not just losing body weight, but actually changing, so for instance, we see this in the clinical genetics, but what was remarkable about the early experiment is we demonstrate that it's achievable, we can engage target, and we can recapitulate human genetics in an animal model. This continues to give us confidence both on the chemistry engine, so how we design our siRNA constructs to do this, how infrequent administration in this population is gonna be important. And I think what we can ultimately do go forward-...
I think the benefit of clinical genetics, serum biomarkers, the ability to assess outcomes, also gives us confidence to be able to rapidly assess in a phase I/II study, no differently than how we approach other genetic diseases, to be able to look at de-risk the proof of mechanism for INHB E. So I think it really does leverage the best of what the team's built around GalNAc conjugation, chemical modifications, and distinguish us, I think, from others in the space.
A follow-on to that question, for the INHBE program, is there a specific disease you are targeting?
I think our initial approach right now is obesity, in the public health context of obesity. We use terms like metabolic syndrome and others, but, you know, I think at the crux of it, obesity is a disease. It has a whole host of downstream complications in terms of how glucose is processed, how lipids are processed, and hyperlipidemia ultimately impacting outcomes. And so I think if we think about our approach, particularly in a phase I/II study, we're gonna be able to assess multiple biomarkers that we're having an impact on this disease, including being able to look at redistribution of fat.
Around these topics, so I'll kind of include them all together. What's the correlation of INHB E levels with obesity and the prevalence of obese patients with elevated INHB E? And what happens to animals who have complete knockout of INHB E?
So what we're seeing is, you know, this is one of the advantages of population genetic studies: patients who are obese have elevated levels of INHB E. So this is not a genetic stratification of patients, a feature of how the target came about; it is a population genetic study. So if a driver is involved in lipid metabolism, so it's be able to measure it and trigger some activity E. So it, it's at the, at the crux of understanding the beginning of this metabolic syndrome, so the target's identifiable as being a potential driver. I think what's also interesting is how with the target selected, it removal of INHBE , they looked at patients, so heterozygous, looking about 50% reduction in the population genetics.
This target is selected because those patients, so where you are removing it, have a benefit in terms of outcome, more lean muscle. They have, you know, better hip to waist ratios and proportion of where fat is distributed. They have a healthy phenotype. And what's also compelling is, because this ostensibly is that knockout of the target has not been lethal. And as people study in the genetics, you can look at . . . There's no increase in mortality that's been studied with the reduction of the target. So as one thinks forward to an ideal target to pursue for this indication, that could open up maximum opportunity for patients, we see a target that causes redistribution to healthy fat, fat ratio, so improvement in body function, improvement in lipid profile.
Doing all of that without suppressing the general reward system, without the tolerability complications that come from existing standards of care, without reducing lean muscle mass and essentially inducing a starvation. We think that being able to reset the body metabolism and restore function offers an ideal opportunity for a drug to address this.
So do we now have a question on RNAi more broadly? With siRNA APP program in the CNS, is C16 being used as a ligand similar to Alnylam?
Chandra, do you want to take that question?
So our constructs are very different, and we are using our PN, proprietary PN chemistry and PN variants, actually, to build tissues. And that's exactly what we have shown here. This is very different modality.
I'll stay in chemistry. Can you expand on what was involved in the N3-uridine base modification? Is chirality important to siRNA drugs?
So N3U base modification, and this is everything involves the chirality aspect, but then when you look at ADAR, there are multiple modifications that we have to use to make our ADAR, our AIMers, to be very efficient with the catalytic turnover. So the N3U is one of the modifications that we use to modify, and there are other variants that we apply.
I think if we step back holistically, because the question on RNAi, but the question on the chemistry that's driving our distribution in CNS. And I will say, right now, the siRNA, to avoid confusion, we're not posing this now that the APP program is a program. I think it's the best application we have, as many of you are doing on the call right now, which is benchmarking where are we in CNS RNAi versus the standard publications from... We think it gives the best point of comparison of what our chemistry can do in comparison with others. I think as Chandra just pointed out, in the case of our AIMer chemistry, it's very similar. We continue to evolve and generate best-in-class chemistry that opens up the opportunity to do better editing.
I think what's really important, too, is we said over the last decade at Wave, while Wave initially started and it's going to continue to add chemistry, I do think it's very important that people understand that our coverage of the universe of chemical modifications extends beyond stereochemistry. So when we talk about things like N3U , PN modifications, PN variants, we're not referring to them in the context of stereochemistry. Referring them to their broadest context, which is that composition of matter. And so as we think the build, really is grounded in a firm understanding of the chemistry, but as you heard today, is really now focused on launching high-value medicines, and we're excited to see that continued translation.
Awesome. So we're going to turn back to AATD. Got a couple questions here, so I'll summarize together. What gives you confidence in healthy volunteers post Anne-Marie? Any risks you're concerned about dosing at the safety of in humans?
What gives us confidence in healthy volunteers? Well, these are studies that are very easy to re-recruit and execute, which is why we have combined registration 1 and 2. We have a well-characterized safety profile from our preclinical studies. We've not seen things that we would consider worse, and that would not enable us to continue data that's going to be generated. Dosing starts this year, and we will see data next year.
I think just to add on that, and I do, you know, I think, again, feature, not a bug, is the fact that we can start in healthy volunteers is a really important feature. It speaks to that question and concerns about safety as one looks broadly at the field of purely DNA editing. And I think the fact that we can go into healthy volunteers and edit, we don't have to be concerned about permanent off-target edits and mutating DNA, that we can be like a... As one would imagine, as we spoke a lot about today, RNA medicines in general, whether it's siRNA, it's splicing, we can look at editing of a similar modality. I think, again, gives us a lot of conviction in moving forward in the space.
And importantly, as you heard from Ginnie today, as we think about diseases where this is an implication, not just for small, rare diseases, the tolerance for that is gonna be really important. So we think about this as a capability set, as really being able to open up for broad prevalent diseases.
At this time, we've covered all the questions in the chat. I'll turn the call back to Paul.
Thank you, everyone, for joining the webcast, and thank you to everyone at Wave for their hard work and dedication to patients.