Wave Life Sciences Ltd. (WVE) Earnings Call Transcript & Summary

[Audio Gap] Will be available in the investors’ action 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, Vice President, GSK's Novel Human Genetics Research Unit and will additionally hear from Tony Wood, 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, Li's Chief Development Officer, will be available for Q&A section. I would 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 sell-in 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 partners 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, the 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 amenable boys, clearly underscores the unique pharmacology that can be achieved. Here, we compare tissue concentrations and exon skipping data from N531 on the right with clinical data generated with our first-generation non-PN compound suvodirsen on the left. With only 3 biweekly doses of N531, we had more than 50x greater muscle tissue concentrations compared with our first-generation chemistry. Even though suvodirsen in 2 weekly doses. While exon skipping was undetectable with suvodirsen, we achieved the highest reported level of exon skipping ever achieved at 53% with N531. The half-life of N531 is approximately 25 days versus 18 hours with suvodirsen, supporting the potential for monthly dosing. An outstanding question for all 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 N531. The brown dots in the nuclear on the screen are stem cells, and you will see many of them stay in there, which is N531. This analysis then on in the stem cells, which may help 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 N531 from other therapeutics, including gene therapies. FORWARD 53, our Phase II potentially registrational study is an open-label trial assessed 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 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 -- last year, with just single doses of 003, we demonstrated a 35% reduction in mutant Huntington 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 Canada for AATD. Among the field, we continue to generate excitement for this not-ever RNA-editing compound to enter the clinic and is designed for restoration of both healthy hepatic and pulmonary function with a reversible and redoseable therapeutic. Importantly, 006 is not only the first of its plan, but it's also best-in-class in ATD as supported by our robust preclinical data package. We can achieve remarkable potency and durability in use dosing because of our unique fully chemically modified oligonucleotides. 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 more should be in this dosing. I'm excited to introduce you to the clinical development plan for WVE-006 called restoration. The clinical development plan is comprised of restoration 1 for healthy volunteers as well as restoration 2 for individuals with AATD who have the homozygous PiZZ mutation. These portions are interconnected and together will enable us to elevation of AAT protein in serum by the most expeditious path possible. Through the healthy volunteer cohorts, we can identify a dosing frequency that can most rapidly support a therapeutic effect in ZZ patients and rapidly completing volunteer cohorts will enable the initiation of patient cohorts at optimized dose levels. Because there are multiple assessments of Z world, it is possible to achieve proof of mechanism before the completion of the cohort or the whole study. Thus, the restoration program will provide a highly efficient path to proof of mechanism. Clinical start-up 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 ‘24, it’s 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 3 diseases indications represent approximately $12 billion in total addressable market value in the U.S., and we expect meaningful clinical data updates on all 3 programs in 2024 grams 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 infant enzymes such as ADAR to open new modalities like RNA editing, using all of our modalities, genetic insights and within 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 oligonucleotide called AIMer to harness the endogenous ADAR enzyme to make a single RNA base of it. We are the leaders in this stay 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 getting 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 increased 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 large our 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 patient 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 as we demonstrated today, we are benefiting from there to novel genetic insights. Building for the future, we also have access to the U.K. Biobank and are advancing our own internal data sets. Together, these resecting partnered and ally-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 VP of Data Science, will shed light on the [indiscernible], which is the editable gene disease universe and all its therapeutic possibilities. Today we can target with [indiscernible]. 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 editing. Expect to advance 5 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, Inhibin 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 AATB program. Our collaboration with GSK puts us in a strong position to execute on bringing this novel therapeutic option to market given this and commercialization. As a quick reminder, under the terms of our collaboration, WAVE is eligible to receive substantial development, launch and sales milestone payments for 006, 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, 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 exons, 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 pro in broader biology. Take oligonucleotide, for example, these tiny molecules are mainly being used to target rare diseases, but including more prevalent ones. We're committed to leveraging genetics and genomics to support our pipeline, understanding the therapeutic targets with genetic evidence are more than twice as likely to become medicine. Currently, at GSK, over 70% of our pipeline is backed by human genetic evidence. We see, 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 is in is where Wave Life Science has come to. We've joined forces with Wave because they have a best-in-class platform called PRISM. It's truly impressive. First the platform is a leading multimodal platform with 3 different RNA targeting molecules. That includes RNA editing, splicing and silencing capabilities. Most platforms focus on just 1 or 2 of these. We're especially excited about the new modality of RNA editing that Wave has pioneered and the possibilities presented by GSK in way of working together on this area. And we see great potential in making allogenic tied in mainstream modality. Together, we're aiming to expand the use of all that goes into more prevalent diseases and scale them for larger patient populations, those one thought undruggable. Now I'll hand over to Carolyn Buser-Doepner, Vice President of GSK's novel human genetics research unit. She will 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 the research units in the identification, selection and progression of genetically associated with 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 to mostly inherited genetics, identify targets to be place acid therapeutics during a formal 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 exact one in 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 need. 5% of the global population has NASH a chronic 5% to 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, the retrospective review of trials in this time period of 2015 to 2019 shows that 90% of our candidates that enter Phase I fail. This high attrition rate is really bad news for patients, and a challenge to our industry. So we ask ourselves, why this high attrition rate, why this failure rates? Actually, the majority of drugs failed due to lack of efficacy. Basically, we have selected the wrong target to modify disease pathogenesis. So why the faulty selection? Again, a number of reasons can be cited include that we have used non-predictive animal models in our preclinical research. 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 business. Several paper team in 2015 have provided evidence that drugs acting on target for which there was genetic evidence associating the targets that we have received, but these targets were more likely to become medicine. And in fact, shown here is an updated figure from a seminal publication from GSK, showing that this strength of genetic hood of becoming a medicine. The strongest evidence comes from loss of function, peel off, and rare disease variants. And you can see this on the X axis showing the probability of success to become in medicine. In fact, when one can continentally assume the genetic evidence of a given gene, then targeting that drug for 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. And so you can quickly see, actually the greatest target opportunity are in genes where we think we have genetic evidence, but it's complicated resignment of the genetic variation to that specific gene. Let me give you an example. Shown on the top left-hand side is are the results from a genome-wide association study or GWAS, that was performed against coronary artery disease. And here, we're assuming in on chromosome 2. And we find 2 had to coronary artery disease. However, when we look at that position at that parent, 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 specific genes. It turns out in the minority of cases it is super clear. The variance in fact, it changes the immune-acid code of what is translated into a protein. Far more often, we find that the parent either sits in a gene bridge portion as is shown in this slide or in a gene 4 region or a desert region, and that's where we have to make a call to the next nearest gene. We have to do more experimental work. In order to address these complexities, but making these assignments, GSK has invested heavily into 3 approaches. The first is human genetics, where we have accessed the multiple and diverse databases, such as U.K. Biobank, and FinnGen, and Regeneron in Health. Of course, we've also worked with the second area of 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 of genomic research at UCSF growth, and AIML 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 protium about 20,000 genes and many more matte they’re regular. The region matte are things that include long noncoding RNA or Endogen. In terms of the Protium, 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 [indiscernible], we foresee that oligonucleotide will be the key models. 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 insights and feasibility. Now using the described target engine, we have significant lead with genetically associated targets and built a robust approach to perform parent gene function studies. Through our collaboration with Wave, we can now access more of that genetically associated target space with oligonucleotide as a modality. One subject you can share with us momentarily. We independently found inhibit through our genetic analysis, and we're particularly excited by it’s seemingly clean profile in phenome-wide association studies. 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 oligonucleotide. As Tony has already highlighted, we were attracted to Wave specifically because of their advanced chemistry and versatile Prism platform that supports the silence and precision editing of target. And 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 Wave 006, a first-in-class RNA editing therapeutic for the treatment of Alpha-1 and 2.

Thank you, Carolyn. I'll start by thanking our speakers in 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 communications, which cover all 4 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 talk 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 suits that are attributable to our extensive FAR and understanding of our best to how best to deploy PN chemistry in this modality. The result has been unprecedented increase in go-to loading. Our recent NAR paper highlighted our best-in-class capabilities using GalNAc siRNA targeting HSD. Again, conflicts are driven by increase [indiscernible], and not tissue exposure. Combining our RNAi capabilities and now genetic insights, access to our GSK collaboration I'm excited to announce our first GalNAc siRNA program targeting Inhibin 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 inhibin EG exhibiting several beneficial traits including reduced weight cap circumference, reduced risk for type 2 diabetes and coronary artery disease, and 50% or more with siRNA is expected to restore a healthy metabolic profile. Inhibin E, express primarily in liver, meaning we can rely on clinically proven GalNAc candidate for targeted delivery of siRNAs. Additionally, levels of [indiscernible] and other relevant clinical biomarkers to access target engagement and clinical efficacy in a relatively short period of time. Inhibin E siRNAs evolve the treatment landscape for metabolic diseases, including obesity. Approximately 47 million people in the U.S. and Europe have metabolic disorders. Metabolic syndrome is associated diabetes and cardiovascular diseases as well as increased risk of mortality. There is a high unmet need for therapeutic options beyond GLT1. These treatments lead to weight loss at the expense of muscle mass suffers the general reward system and are associated with poor tolerability profiles and discontinuation rates. So our therapeutic approach for obesity that improves metabolism, increase track loss, maintains muscle mass and does not affect the general reward system would be ideal. BS is the target engagement in vitro with a species cross reactor sequence using an early GalNAc siRNA design. We observed much with this siRNA with a maximal 90% I mean human system. This means that we would expect to see much higher potency in humans than that we observed in mice with the scans. These involve the proof-of-concept study in Vero in young diet-induced obesity or GAO mice, to evaluate if incipient is vital fact. After 5 weeks, we saw 52% inhibin E 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 PES after 5 weeks. A similar FX size was reported for semaglutide in a preclinical study. We look to see how body weight changes were reflected in different types of adipose tissues. We normally see removal of excess fat in the wide adipose tissue similar to levels in lymph shaped mice which is exactly what we saw. We saw substantial reduction in retinal cap issues mesenteric and epididyma. In a subsequent study, longer duration study, we followed 3 mice out to 8 weeks. Increasingly, the visual fat loss deepened over time in multiple tissues and all tissues were in line with the lymph shaped mice. To our knowledge, this is the first demonstration of siRNA treatment, restoring a healthy phenotype with a – we continue to improve our GalNAc siRNA designs. And our next assay generation family has best-in-class potential. On this slide, with our next-generation siRNA, shown in further improved the potency and duration of balancing over our best published design, showing in light blue and the benchmark or 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 commit, make support biannual or annual [indiscernible].We are currently applying this new chemistry format to inhibin E, and we expect to select a clinical candidate. As you heard from Carolyn, AI is also one of the multiple modalities that we are pursuing with GSK. As noted in the last slide, we aim to continuously improve our chemical design for presence. As we have shown through multiple publications across modalities, our PN modification profile modifications and the enhancement we observed can be tuned the variation of this modification, we call them as PN variance. We can take advantage of this versatility to introduce properties such as increased liver philosophy, which are advantageous for delivery, especially to cell-type beyond eukaryotes. And that we can improve silencing in tissues associated with metabolic disease, including liver and adipose tissue by changing from our standard PM to a PMV in siRNA. As a reminder, these are non- GalNAc conjugated siRNA, and they are single dose. With these siRNA-modified with PN variants, we are offering in multiple tissues, including heart and liver. We have played a similar approach to expand on AI mediated balancing in the CNS. We can achieve total and sustain silencing with a single dose with greater than 75% production in the APP transcript across all the regions of the brain through the end of the study of which is tremendous advancement to the data recently published by [indiscernible] for the same target and same route of administration. On this slide, we show evidence confirming that APP transcript silencing reached a striking reduction of APP protein across brain regions, 8 weeks following a single dose, which is highly visible after siRNA treatment in the bottom row. In summary, we have demonstrated best-in-class RNA-mediated talent. We are advancing our first Inhibin E GalNAc siRNA program with a clinical candidate expected by the fourth quarter of 2024. 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 capabilities in our nature biotechnology publication in early 2022. As with other modalities, the opportunities amenable to this emerging modality, we're continuously expanding our key editing capabilities. And this year, we shared how proprietary-based modifications have improved our next-generation AIMers. This slide provides an overview of how one of the base modifications, the M3 modification expands certificate for editing. The MCU modification consistently improves RNA editing levels across sequences, even those where our original designs were not affected. These modifications are incorporated in WB-006 and other editing compounds you will hear about in a moment. Earlier, Paul shared how product 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 operation of protein expression. We have publicly presented on our PPI capabilities toward the past year. Today, our team is to operate protein expression by editing RNA. The production, processing, stability and degradation of RNA is highly regulated, creating ample opportunity for us to intervene the AIMers to change the amount of protein a transcript can encoded. One mechanism for regulation relies on interactions between a transcript that promotes a degradation of the transcript. In this scenario, the dilaprotein expression with catalytic efficiency using AIMers. By developing AIMers that edit the sequence protein in the RNA that enables the protein RNA interactions, we can disrupt the interaction between the RNA and RNA binding protein, more RNA. 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 to the RNA editing, the resulting protein production should be dose dependent. Now here we demonstrate in deep proof of cancer for the application of AIMers moving from left to right, we demonstrate over 75% RNA editing of the target. And this leads to twofold upgrade, they can get twofold of propagation of their mRNA and ultimately, an increase in protein expression. With this, now I'll turn the call over to Ken Longo, Wave's VP’s of data science to discuss how we are leveraging.

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 edit first, a term which we use to broadly define the editable gene disease possibility, all genes were an A to G edit is predicted to impact transcript levels; and 2, 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 aim target today will walk you through several examples of gene disease networks or Galaxy's with novel eukaryotes 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 so 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 adenosine likely to modulate the transcript with high confidence. We trained and validated a deep learning model using known variants. This model predicts the impact of editing on mRNA transcript levels. Our model achieves good accuracy at predicting no mutation sites but of equal importance, it also discovers novel eukaryotes. It competently predicts these changes for over 50% of the protium. As the figure on the right shows, there is a good correlation between the blue QTLs that the model never saw during training. The deep learning model allows us to rapidly and efficiently identify AIMers mediated upregulation opportunities. On this slide, you can see a typical eQTL prediction or trace or basis in an undisclosed gene. With potential AIMers targetable adenosine, higher scores identify locations where an ATG edit is predicted to impact transcript levels. For our first example, we'll zoom into a network for hyperlipidemia and energy editing. This knowledge graph connects concepts such as genes, diseases and pathways and is based on multi-evidentiary support, including G-WA studies. Zooming into this network, Gene nodes, which are each AIMers 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 SVP of Translational Medicine career at companies such as Bayer, Novo Nordisks, AstraZeneca and Eli Lilly. Applying traditional and novel modalities to bring medicine with disease-modifying potential to patients. With highly differentiated RNA therapeutic capabilities, especially in RNA editing [indiscernible]. After 4 months at waste, mices were 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 for safer medicines. Now I will share how we are experiencing our pipeline by target from our E learning model. As we zoom into the network, if we view target A, an undisclosed metabolic target that is uniquely suited for AIMers upregulation. We initially investigated this target to demonstrate the power of RNA editing to upregulate and obtain RNA 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 19 million with metabolic syndrome obesity. Upregulation of Target A, increases, the body is secreted in the blood. The ability to make this supporting serum level means that there is a biomarker of readily available for efficient clinical development. Our model correctly predicted an AIMer for editing in upregulation. We achieved over twofold mRNA upregulation and a similar degree increase in endogenous protein production in liver. Next, we look to assess the impact of this proving increase on the phenotype of a diet-induced obesity or CIO house model. You can see here that the AIMer have significantly lower body weight as compared to PBS-treated control model. Clearly, the data suggests that this degree of inductions folding of upregulation is sufficient to induce a healthy metabolic finer types, including body when it’s lowering. This result provides the first [indiscernible] phenotypes. We also look at fasting glucose and fasting income levels in this month. We showed that the AIMer treated DIO mice deployed suppression of fasting glucose and the profound reduction of fasting insulin, indicating substantially improved influent sensitivity of [indiscernible] shows that potential for this intermediate have regulation approach. Now I would say we've been the same disease network and move to target B. Upregulation of this target offers a first-in-class therapeutic approach for hyperlipidemia. As we target ATP indulgence protein will impact millions of patients with hyperlipidemia with a unique approach. There are also several biomarkers available to really assess the target engagement and efficacy. Our proprietary modeling that suggests that over 24 upregulation of target PMR they were delivered clinically meaningful [indiscernible]. We designed an AIMer to add target B in human primary [indiscernible]. As shown in the figure here with over 70% editing, we are demonstrating more than 24 Upregulation of having the mRNA, which leads to a significant increase in porting production apatite. Now we move to a renal disease up network but continue with targets that can be access to AIMer delivery to the processing. We call this target X which produced a security protein endeavor to treat kidney disease. This condition is associated with early mortality, significant economic burden, and we estimation in the U.S. and zero could be addressed with our approach. There is a strong therapeutic rationale for target X, as supported by genetic insights few and observational data indicating targets of the upregulation can stop kidney functional decline. Plasma biomarker segment and our modeling indicated approximately 24 upregulation in security working will be clinically meaningful. Based on our existing data set, we view this level of upregulation is readily achievable, and we are working toward proof of content data. Building on the success we have seen as another opportunity to correct disease of causing mutations with RNA editing. The medical need, the nature of pathology and the mechanism driving the [indiscernible] are well understood, and there is a fully translatable serum biomarker to support clinical development. R&A correction of 15% to 30% is illustrated by the figure, we have achieved over 60% correction of target E from [indiscernible]. As Chandra discussed with RNAi, our chemistry enables us to distribute RNAi to a broad array of tissues. I will now share an up diseases that have heart, lung and Kidney disease. In kidney with a single dose, we demonstrated approximately 40% editing of acting in nonhuman primates and over 60% editing of the EGP target with our next-generation ambition 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 [indiscernible] or the tubule. We now return to the renal insufficiency subnetwork but look at target F for rare genetic kidney disease. Abra demonstrated we store a kidney function, the unmet medical need in end-stage renal disease is well understood. In the patient population is well defined. Available clinical and molecular data demonstrate that 24 upregulation of the target was delivered clinically meaningful benefit. You can see on the left side of the slide, we have achieved over twofold of upregulation of target F and in human kidney tubule epithelial cells following an editing of 2 different AIMers. Turning to [indiscernible], we have tested our ability to exit mRNA with our early AIMer design with a single dose, we achieved approximately 18% editing of [indiscernible] in the line of nonhuman primate. Ecology images demonstrated accumulation of our targeting oligos in frontal epithelial cells. As Chandra shared earlier we can ration AIMERs, we have achieved over 35% editing of UGT2 in the month of March. Again, we turn to an opportunity to explain profound our work with AATD to attend an RNA correction approach for genetic lung disease with targets E. This is the right very little benefit from the valuable therapy and molecular mechanism is well understood. The available data from human genetics suggest a correction of approximately 20% of the new take in mRNA, we deliver clinically meaningful benefits, and we have achieved approximately 70% model. Let me conclude with a few points. The adverse accessible to Wave the AIMer is very substantial and still expanding. We are advancing our work for a diverse set of target addressing areas of high medical needs, including [indiscernible]. 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 group of concept. This includes our first siRNA program, inhibit 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 U.K. Biobank. We are well poised to sustainably deliver transformational – we expect 5 new clinical candidates by year-end 2025. The first of these programs is expected to be our GalNAc siRNA inhibit program. We're also advancing multiple high-value RNA editing targets, which will follow on the success of our WBE006 editing program and further extend our leadership in this space. Any combination of these targets may support our 525. 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. And 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. [Operator Instructions]. So we'll start with first question. Operator, can you hear us.

Yes, we've got you now. Thanks, Alicia.

Operator can hear us.

I think we can.

Yes, I'm going to start just to --I'm not sure where the audio cut off, so if we read the question and we'll start again. On our AHG trial design on Slide 12 at which dosage levels you start to hit the therapeutic window. At which dose levels of WVE-006 should we expect to get data in 2024?

Thanks, Kate. So restoration 1 and 2 studies have been really thoughtfully designed so that we can most efficiently generated and AAT, the N-type protein. Through the volunteer study, we can rapidly move to a therapeutically active dosing patients, and then what's really it mean is we have designed the study such that we can measure and detect AAT protein on an ongoing basis. Therefore, the proof of mechanism can be detected at any time during the…

APD. -- with initial proof-of-concept data, including assessment of neutrophil elastase or other indicators that WVE-006 could differentiate from RNAi by impacting lung phenotypes included.

Yes. So in addition to measuring AAT, the wild-type protein, which will demonstrate that we have successfully edited and restored MD phenotype, this wild-type protein using the niche from the last stage assay.

Okay. Next question we on go to inhibit, the inhibiting clinical candidate going to leverage GalNAc?

I think the very, very short answer to that is, absolutely. I think one of the advantages that we're seeing is, as you saw today with Chandra data, not just on the power GalNAc delivery, but it also gives us the potential for much more frequent dosing. We believe those 2 ingredients, Wave chemistry, coupled with GalNAc opens up the opportunity for a best-in-class inhibiting siRNA approach.

And as you think outside the liver, how are you thinking about purely chemistry modification. Modifications versus leveraging different core or less accessible via chemistry alone.

Because you learned a little bit about today, I think chemistry, and I think we use chemistry in a very broad context, the chemical modifications as Chandra was talking about with new variants of PN are giving us the ability to elect various phenotypes. And so we saw that in the distribution and [indiscernible]. I don't think this for goes, and we have a lot of discussions about it around one of them that is driving delivery to specific phenotypes using active receptors. And again, it's one of the advantages we have in collaborating with GSK, we think broadly about the genetic universe of target and tissues with which we want to explore.

And going back to Anne-Marie, what kind of translation for exon skipping to dystrophin expression, would you expect to see an exon 53 patients?

Of course, these things produce more dystrophin. And it's also clear there's a delay between Exon [indiscernible] and dystrophin. [indiscernible] showed 35% skipping at 12 weeks, and this resulted in 1% dystrophin. Our study is powered to show more than 5% dystrophin 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 N531 in nonhuman primate core and diaphragm compared with skeletal muscle.

So I think to step back too and just think more broadly about treatments for DMD. So I think the kind of the question is I'm interpreting and really referencing it is existing oligonucleotides and how they're approaching the disease about treating DMD. It is a multi-muscular disease, skeletal muscle, heart and diaphragm. And the approach we took in the very beginning of this was broad distribution across multiple muscle tissues, brought exposure to the nuclei of multiple muscle cells, including as we shared today, for the first time, satellite muscle cells. So when we think about the regenerative potential of how to actually bring the myofiber, being able to not just get in a scale myoblast 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. I know we're always focused on what is the percentage but we're really focused on what is the benefit of dystrophin replacement there, how do we think about. We demonstrated return to wild-type and respiratory function with title volume, right? So the rescuing of skeletal muscle in our preclinical model. So we do have to think back about the treatment of the disease. As Anne-Marie, we do expect that this is not saying we don't expect to be greater than 5% and hopefully a lot more. We also have to think about those kinetics. So in the question, I recognize citing numbers from [indiscernible] about 35% in that 12-week time point. And looking at dystrophin is a cellular machinery that needs to start over time. At 6 weeks, we're already at 53%. So that machinery started earlier and it's continuing over the longer course given the substantial amount of muscle on distribution of muscle. So again, a lot of these yields, our competency and conviction based on our preclinical data in models where they don't have dystrophin. 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.

So I am going to go to inhibit again. Can you walk us through the key differences of inhibit program and competitors?

And I look at my colleagues, and I'm sure Chandra and Ginnie will have a lot to jump in around this. But I think about when we talk about competitors in inhibit, 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 yielding, as we said, we announced the collaboration only initiated at the beginning of this year and that GSK was coming to the unique genetic insights on target. So not just accessing databases, but really bringing deep genetic insights into understanding genetic targets 2, as you heard Carolyn say earlier, increase the probability of success in translating therapeutic outcomes. And so based on those genetic insights, we're excited about 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 years, 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 model. I mean I do think it's a bit remarkable when we think about, we talk oftentimes about these clinical genetics as in this case, how that's going to ultimately translate which is very important as we look about the human genetics here. So ultimately, clinical benefit to these patients. We forget the obesity of the public health epidemic with multiple complications in cardiovascular disease, stroke, cancer and other diseases like type 2 diabetes. And so when we think about the importance of actually doing something like changing, not just losing body weight, but actually changing to -- we see this in the clinical genetic, but what was remarkable about the early experiment as 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 that, how infrequent administration in this population is going to be important. And I think what we can also as we go forward. I think the benefit of clinical genetics 0 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 an M&A. So I think it really does leverage the best of what the team is built around GalNAc conjugation, chemical modifications and distinguish us, I think, from others in this space.

And a follow-on to that question for the inhibiting 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 interim and others, but I think at the crux of it, obesity is a disease. It has a whole host of downstream complications in terms of how glucoses process, how lipids process, hyperlipidemia ultimately impacting outcome. And so I think if we think about our approach, particularly in a Phase I/II study, we're going to be able to assess multiple biomarkers that we're having the impact on this disease, including being able to look at redistribution of that.

It kind of include them all together, it's the correlation of inhibiting levels with obesity and the prevalence of obese patients with elevated antimony. And what happens to animals to a complete knockout of inhibiting?

So what we're seeing is this is one of the advantages of population genetic studies. These patients who have elevated levels of inhibiting. So this is not a genetic stratification of patients’ measure of how the target came about a population genetic study. So if a driver is involved in metabolism, so to be able to measure and it triggers active in E. At the crux of understanding the beginning of this metabolic syndrome. So the target identifiable as being a potential driver. I think what's also interesting is how with the target selected a move of inhibiting that looked at patients. So heterosis within about 50% reduction in the population genetics. This target selected because those patients where you are removing them have a benefit in sort of outcome more in muscle. They have better get to waste ratio proportion of where it’s first distributed. They have a healthy phenotypes. And what's also compelling is because that sensibly is that knockout of the target has not been levelled. And as people have studied in the genetics, you can look at -- there's no increase in mortality that's been studied with the reduction of the target. So it's one thing 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 [indiscernible] so improvement in body function, improvement of lipid profile and doing all of that without suppressing the general reward system, without the tolerability complications that come from existing metrics care without reducing lean muscle mass and essentially reducing starvation. We think that being able to reset the body metabolism and restore function offers an ideal opportunity for tractors this.

And so 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.

So our contracts are very different, and we are using our proprietary PN chemistry and PN variance actually into these tissues, and that's exactly what we have shown here. This is very different to that.

And then I'll stay in chemistry. Can you expand on what was involved in the [indiscernible] based modification? Is chirality important to siRNA?

[indiscernible] base modifications, and this is everything involves, there is chirality aspect. But then when you look at it, there are multiple modifications that we have been used to make or emerge to be very efficient with the [indiscernible]. So the entry will be one of the modifications that we use to market, and there are other gains that we apply.

I think if we step back holistically with the question on RNAi, with the question on the chemistry that's timing our distribution of CMS. And I will say, right now, the siRNA to avoid confusion, we're not posing us now that the ATT 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 gets 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 [indiscernible] chemistry, it's very similar. We continue to evolve and generate best-in-class chemistry that opens up the opportunity to do better edit. And I think what's really important too, as we said over the last decade, we initially started to continue with chemistry. I do think it's very important that people understand that our coverage of the universe of chemical modification extends beyond stereochemistry. So we talk about things like [indiscernible], TM modifications, [indiscernible]. We're not referring to them in the context of stereochemistry, referring to them in the broadest context, which is that opposition model. 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.

I’m going to come back to AATD. A couple of questions here, so I'll summarize together. What gives you confidence in healthy volunteers? Goes to Anne-Marie. Any risks you're concerned about dosing at the safety of humans.

What gives us confidence in healthy volunteers? Well, these are studies that are very easy to recruit and execute which is why we have combined castration 1, and 2. We have a well-characterized safe profile from our preclinical studies. We've not seen things that we would consider worsen that would not enable us to continue, [indiscernible] that’s going to be generated, floating starts this year, and we will see data next year.

I think just to add on that, I think, again, a feature, not a bug. 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 in the field in early DNA kind of thing. And I think the fact that we can go into health [indiscernible], we don't have to be concerned about permanent off-target and mutating DNA that we can be like as one would imagine, as we spoke a lot about today, RNA medicines in general, whether it's as by RNA, in splicing, we can look at editing as a similar modality. I think, again, it 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 going to be really important. So we think about this capability set as really being able to open up for broad prevalent diseases.

At this time, [indiscernible], all the questions. With that, I'll turn the call back to Paul.

Thank you, everyone, for joining the webcast, and thank you to everyone and Wave for their hard work and dedication to patients.