This Annual Report on Form 10-K contains “forward-looking statements” about us and our industry within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. All statements other than statements of historical facts are forward-looking statements. In some cases, you can identify forward-looking statements by terms such as “may,” “will,” “should,” “would,” “expect,” “plan,” “anticipate,” “could,” “intend,” “target,” “project,” “contemplate,” “believe,” “estimate,” “predict,” “potential,” or “continue” or the negative of these terms or other similar expressions. Forward-looking statements contained in this report may include without limitation those regarding:

We have based these forward-looking statements largely on our current expectations and projections about our business, the industry in which we operate, and financial trends that we believe may affect our business, financial condition, results of operations, and prospects. These forward-looking statements are not guarantees of future performance or development. These statements speak only as of the date of this report and are subject to a number of risks, uncertainties and assumptions described in the section titled “Risk Factors” and elsewhere in this report. Because forward-looking statements are inherently subject to risks and uncertainties, some of which cannot be predicted or quantified, you should not rely on these forward-looking statements as predictions of future events. The events and circumstances reflected in our forward-looking statements may not be achieved or occur and actual results could differ materially from those projected in the forward-looking statements. Except as required by applicable law, we undertake no obligation to update or revise any forward-looking statements contained herein, whether as a result of any new information, future events, or otherwise.

potentially available relevant information. These statements are inherently uncertain and you are cautioned not to unduly rely upon them.

We leverage our Recursion OS to enableThere are three key value drivers:value-drivers at Recursion:

Human biology is a highlyan incredibly complex system for which human intelligence alone is insufficient to fully understand. While hundreds of thousands of incredible scientists around thecomprehend it. Our world dedicate themselves to expanding our understanding each day, the extraordinarily high failure rates of human-generated hypotheses in our industry suggest to us that we still understand just a small percentage of biology, chemistry and the interactions between the two.

However, a delay in the fruits of technology in our space. However, this means we are in a position to learnkey lesson from the lessons of the application to technology to manynumerous other fields. One of the primary lessons learned across numerous industries is that computational sophistication alone is rarely sufficient to create disruptive change. It is when computational sophistication is paired with the right data, typically in an iterative process of ongoing learning, prediction, and refinement, wherethat outsized change is created.

Recursion was founded in 2013 with a vision to capitalize on the convergence of advancements in computation and machine learning to address the decreasing efficiency of drug discovery and development. We believe that this opportunity represents one of the most positively impactful applications of ML and AI. Our vision is to leverage technology to map and navigate biology, chemistry, and chemistrypatient-centric outcomes in order to discover and develop more, betterincreasingly transition the process of developing medicines faster.from discovery to design. We believe that neither advanced computational approaches, massive datasets, nor human intelligence alone can fundamentally shift the efficiency curve of drug discovery and development; instead, we believe that those companies that augment their teams with sophisticated computational tools leveraging hard-to-replicate proprietaryand focus deeply on generating and aggregating the right datasets will have a significant advantage. We believe we are among the companies leading this burgeoning new sector of the biopharma industry that we call TechBio. Our success and the success of this newthe burgeoning TechBio sector generally, has the promise to drive more, new, and better new medicines to patients at higher scale and lower prices.prices in the coming decades. We are working hard to not only lead this space but define it.

Our culture at Recursion is intentionally designed with intention to fuel our mission. We believe culture drives delivery. Essential to decoding biology in our context is the Recursion Mindset, a mindset deeply committeddeep commitment to achieving impact at unprecedented scale through pioneering new industrialized approaches. We call it the Recursion Mindset. To embrace this mindset and our ambition, our people must deeply learn what will make them impactful in our context while questioning what made them successful in prior contexts. Sometimes this requires unlearning. Sometimes this requires a professional metamorphosis. For everyone it requires change. To decode biology, we intentionally source fortalent from an incredible breadth of fields from multiple industries and forindustries. For all of themour employees, Recursion is a new kind of company. The guideposts for teaching our people to successfully transition to TechBio and deliver our mission are our Founding Principles and Values. They are the essential shape of our culture. TheOur Founding Principles direct us in making scientific and technical decisions that further our mission. TheOur Values define the day-to-day behaviors and mindsets that further our mission. Together, along with the brilliance, humility and diversity of our people, our culture comes to life. Together, they are the compass that point our people towards decoding biology.

The traditional drug discovery and development process is characterized by substantial financial risks, with increasing and long-term capital outlays for development programs that often fail to reach patients as marketed products. Historically, it has taken over ten years and an average capitalized R&D cost of approximately $2 billion per approved medicine to move a drug discovery project from early discovery to an approved therapeutic. Such productivity outcomes have culminated in a rapidly declining internal rate of return for the biopharma industry.

Despite significant investment and brilliant scientists, these metrics point to the need to evolve a more efficient drug discovery process and explore new tools. Traditional drug discovery relies on basic research discoveries from the scientific community to elucidate disease-relevant pathways and targets to interrogate. Coupled with biology’s incredible complexity, this approach has forced the industry to rely on reductionist hypotheses of the critical drivers of complex diseases, which can create a ‘herd mentality’ as multiple parties chase a limited number of therapeutic targets. The situation has been exacerbated by human bias (e.g., confirmation bias and sunk-cost fallacy). Accentuating this problem, the sequential nature of current drug discovery activities and the challenges with aggregation and relatability of data across projects, teams and departments lead to frequent replication of work and long timelines to discharge the scientific risk of such hypotheses. Despite decades of accumulated knowledge, the result is that drug discovery has unintentionally created hurdles for innovation.

Simultaneously, exponential improvements in computational speed and reductions in data storage costs driven by the technology industry, coupled with modernthe rapid rise of large language models, generative AI and other ML tools, have transformed complex industries from media to transportation to e-commerce. TheHistorically, the biopharma sector however, has been slowerslow to embrace such innovations, exceptinnovations. Within the past 18 months, there have been remarkable shifts in narrow areas.perception among technology and biopharma companies as well as among regulators and policymakers, who highlight the utility of AI/ML for broad drug discovery and development from novel target discovery through next-generation manufacturing. We believe this rapid acceleration and adoption of these technologies demonstrates the growing consensus that AI/ML is a catalyst for substantial leaps in drug discovery.

We have used our approach to generate, aggregate, and integrate one of the largest biological, chemical, and chemicalpatient-centric datasets in the world (over 21at over 50 petabytes at the end of 2022) which2023. This dataset includes proprietary phenomics, transcriptomics, predicted protein-ligand binding interactions, InVivomics, ADME data, and more across a large number ofmany biological and chemical contexts.contexts as well as preferred access to over 20 petabytes of multimodal oncology patient data from Tempus. Additionally, we have built a proprietary suite of software applications within the Recursion OS which has identified over 35 trillion predicted biological and chemical relationships. The following table highlights how Recursion has scaled with respect to experiments, data and biological and chemical relationships. With our approach, we lookendeavor to turn drug discovery into a search problem where we map and navigate biology in an unbiased manner to discover new insights and translate them into potential new medicines at scale.

While most small to medium-sized biopharma companies are focused on a narrow slice of biology or a single therapeutic area, where they believe they have an advantage or insight based on the summed experience of their team, the Recursion OS allows us to discover and translate at scale across biology. However, we are cognizant that building disease-area expertise, especially in clinical development, is essential. And so, weWe have developed a multi-pronged, capital-efficient business model focused on key value-drivers that enable us to demonstrate our progress over time while continuing to invest in the development of the Recursion OS, which we believe is the engine of value creation in the long-term. While our mapping and navigating tools have the plasticity to be applied across therapeutic areas and modalities, our business model is tailored to maximize value and advance programs cost-effectively based on the nature of market and regulatory dynamics associated with our three value drivers (internal pipeline, transformational partnerships, and fit-for-purpose proprietary biological, chemical, and chemicalpatient-centric data).

We believe that the primary currency of any biotechnology company today is clinical-stage assets. These programs can be valued using a variety of models by stakeholders in the biopharma ecosystem and most importantly, present the potential to meet critical patient needs. Further, forFor Recursion, these assets have a variety of additional benefits, including: (i) validation of key elements of the Recursion OS, (ii) growing our expertise in clinical development and (iii) building in-house processes to facilitate smooth interaction with regulatory agencies and advance medicines towards the market. If the Recursion OS evolves in the manner with which it has beenas designed, then it will continuously improve with more iterations such that future programs could be more novel and potentially more valuable than today’s programs. In this way, operatingOperating as a vertically-integrated biopharmavertically integrated TechBio company that leverages technology at every step from target discovery through clinical development (and even marketing and distribution) may be the long-term business model with the most upside for our stakeholders, including both investors and patients. Furthermore, weWe may be opportunistic about selling or licensing assets after they achieve key value-inflection milestones so that we can re-invest in our long-term strategy.

We believe that in its current form, our Recursion OS is already capable of delivering many more therapeutic insights than we would be able to responsibly shepherd alone today. As such, we have chosen to partner with experienced, top-tier biopharma companies like Bayer, Roche, and Genentech to explore intractable and resource-intensive areas of biology like fibrosis with Bayer and neuroscience with Roche and Genentech.biology. The key advantages of these partnerships are that: (i) we are able to deploy the Recursion OS to turn latent value into tangible value in areas of biology where it would be challenging for us to do so alone; (ii) the clinical development paths for these large therapeutic areas are often resource-intensive and highly complex; and (iii) we are able to learn from our colleagues at these top-tier companies such that it could give us a competitive advantage in the industry over the longer term. This strategy also embeds us in the discovery process of large pharmaceutical companies and gives rise to an alternative long-term business model whereby we become a valued partner of many such companies. Here, Recursion would focus on discovery efforts across a broad set of programs while relying on its partners to develop and market the medicines. Based on how value is ascribed across our industry today, this model alone is not yet feasible to maximize our business impact. However, we feel that due to shifts within the biopharma industry there is some potential for this portion of our business model to accrete notable value over the long-term.

As has been demonstrated in many other industries, a value drivedriver and competitive advantage can be generated from the creation of a proprietary dataset. At Recursion, we have generated what we believe to be one of the largest fit-for-purpose, relatable biological, chemical, and chemicalpatient-centric datasets on earth.Earth. Spanning multiple omics technologies and more than 175200 million unique experiments, the over 21 petabyte50 petabytes of data that Recursion Data Universe is ofgenerates, aggregates, and integrates has the same rough scale as those generated by some of the oldest and largest pharma companies,11 but is built from scratch in our laboratories with a fundamental purpose of being used to be used as training data fortrain machine learning models. Through intensive internal work, Recursion uses this data and our own models, algorithms, and software to generate and advance our own internal pipeline of medicines (Value Driver 1), as well as in partnership with our collaborators to advance additional discovery programs (Value Driver 2). Our most recent collaboration announcement with Roche and Genentech set an important precedent - there are up to or exceeding $500M in milestones unrelated to specific drug discovery programs, but instead based on successful creation and optioning of collaboration data generated by Recursion. As our field increasingly recognizes the potential for a comingtechnology-driven revolution in drug discovery, based on virtuous cycles of atoms and bits, these data are themselves becoming a direct value driver. While we will likely direct the generation of new data and exploit the latest data in our data Universehas increasing potential to maximize our pipeline and partnership value-drivers, wedrive value directly. We increasingly see the potential to license select models and subsets of our data to a growing universe of collaborators for which internal efforts would be minimal, but value could be significant.

There are threea few key factors that differentiate Recursion from the vast majority of other TechBiotechnology-enabled drug discovery companies.

In order to create large and relatable data sets,datasets, standardization and scale are two critical requirements that can be best achieved through automation. Standardization means that the experiment is executed consistently every time, day after day, year after year - and that any deviations can be detected, tracked, and quantified. It involves meticulous metadata collection, prospective/retrospective experiment execution analysis, standard results storage, quantitative quality control and more. At the same time, massive scale, with millions of experiments executed per week, requires execution of multi-step assays processed rapidly and in a tightly orchestrated manner. This combination of precise repetition, high speed and massive volumes favors relying on robots over highly trained scientists, whose time is better spent on context-specific problems. In addition, automation of high-dimensional experiment readouts at scale enables cost reductions in the large high-dimensional digital data setsdatasets that can underpin today’s cutting edgecutting-edge opportunities in machine learning (bits).learning.

While we do not consider ourselves to be hardware innovators, we have leveraged a significant team of automation scientists to assemble and synchronize advanced but widely-available robotic components, such as liquid dispensers, plate washers, incubation stations, automated HPLC, mass spectrometry and automated microscopy camera systems, to efficiently execute millions of experiments per week across a variety of data-rich outputs with only a small team overseeing the process at any given time. These robotic systems are modular by design and easily configurable to allow us to create complex and variable workflows. Furthermore, we have recently operationalized a fully integrated system that processes plates continuously through all steps in our primary experimental workflows. This fully integrated system is interoperable with the existing batch processing workcellswork cells but provides greater walk-away time for our operators and greater throughput in a smaller footprint.

We have built a state-of-the-art cell culture facility to consistently produce high-quality, primary mammalian cells, such as vein, kidney, lung, liver, skin and immune cell subsets, as well as stem cell-derived and cancer cell lines. In total, over 50 cell types have been onboarded to our high-throughput discovery systems. In 2022, we greatly expanded our cell culture facility footprint to perform work using human induced pluripotent stem cell (hiPSC) lines. Specifically, we have developed protocols using CRISPR genome editing technologies to generate knock-out or knock-in lines. We have developed protocols to differentiate hiPSCs into several distinct cell types using 3D and 2D differentiation methods. Furthermore, we have developed internal capabilities to characterize these cells using standardized and partly automated methods. Lastly, we have developed a scalable platform to produce 50-100 billion cells of the Recursion Data Universe isinterest per week and cryopreserve cells in assay-ready frozen format. Since 2022, our proprietary cellular image dataset generated by our automated phenomics platform. While investigatingteam produced over 1 trillion hiPSC-derived cells of interest to support various biological and chemical contexts, the readout remains constant: a fluorescent microscopy image that captures composite changes in cellular morphology; a cellular phenotype. We use our proprietary staining protocol to capture these changes in cellular morphology across our phenomic experiments. This protocol, consisting of six subcellular dyes imaged in six different channels, has been optimized to capture a wide array of biology across nearly any adherent human cell type that can be cultured and perturbed in laboratory conditions. As a result, we can capture the effects of a wide range of biological and pharmacologicalongoing projects.

Cellular morphology is a holistic measure of cellular state that integrates changes from underlying layers of cell biology, including gene expression, protein production and modification and cell signaling, into a single, powerful readout. Image-based -omics can be two to four orders of magnitude more data-dense per dollar than other -omics datasets that focus on these more proximal readouts, enabling us to generate far more data per dollar spent to inform our drug discovery efforts. We currently generate up to 13.2 million images or 110 terabytes of new data to the Recursion Data Universe per week across up to 2.2 million experiments. Lastly, ourOur phenomics approach builds on the recent explosion of powerful computer vision and ML approaches driven by the technology industry over the last decade. Modern ML tools can be trained to identify the most salient features of images without relying on any pre-selected, disease-specific subject matter expertise, even if these features are imperceptible to the human eye. Using these tools, we can capture the aggregate cellular response induced by a disease-causing perturbation or therapeutic and quantify these changes in an unbiased manner, freeing us from human bias. In contrast, traditional drug discovery relies on presumptive target hypotheses and bespoke biological signaling assays that only capture narrow, pre-determined biology and thus limit the scope of biological exploration.

We have developed an in-house transcriptomics laboratory platform, complete with walk up automation and push button digital data processing, capable of profiling up to 15,00025,000 samples per week covering expression of nearly 20,000 genes from samples drawn from any of our biological modules. At the end of 2022,2023, we had leveraged our transcriptomics platform to sequence over 250,000700,000 individual transcriptome samples to improve our biological understanding of many of our programs.programs and to begin to create another layer of orthogonal biological mapping data to complement our phenomics mapping data. In 2023,2024, we intend to further scale and automate this capacity further to enable more hits identified from our phenomics platform to be confirmed using an orthogonal, transcriptomic readout as part of our Industrialized Program Generation workflows.industrialized program generation workflows and we expect to be able to approach whole-genome scale mapping in this data-layer as well. This approach of combining high dimensional, large scale data layers from the Recursion OS, across phenomics and transcriptomics allows us to increase our confidence around which insights to prioritize for scientist follow-up, while at the same time minimizing cost and human effort. Similar to how we scaled transcriptomics to complement phenomics, we expect to scale additional data types like proteomics, metabolomics, lipidomics, and others.

In 2023, our Digital Vivarium consisted of over one-thousand digital mouse cage units. These support digital tolerability studies, which allow us to identify phenotypic responses unique to different modes of toxicity and holistic assessment of the animal’s health state across the entirety of the study. This data can subsequentlyprioritize which compounds and doses should be used in efficacy studies. We also conduct InVivomic efficacy studies to create more abstract representationsevaluate treatment effects early based on whole-animal digital observations. We have also initiated the expansion of animal behavior, potentially allowing usour InVivomics tolerability studies into rats to rapidly phenotype new animalleverage the advantages of whole-animal digital observations for exploratory non-GLP toxicology studies.

Our in-house chemistry tools include physical compound collections, state-of-the-art compound storage and handling infrastructure and high-precision analytical equipment. Our experienced teamThe physical capabilities paired with our cutting-edge computational and digital chemistry platforms combine to accelerate hit identification and progression through virtuous cycles of chemists use this equipmentpotency and a network of reputable CROsproperty optimization to advance discovery efforts and deliver differentiated drug candidates.

In December 2023, we entered into a collaboration with Enamine to generate and design enriched compound libraries for the global drug discovery industry. By leveraging Recursion’s MatchMaker AI model, a product added to Recursion after the acquisition of Cyclica in 2023, to identify compounds in the Enamine REAL Space predicted to bind to high-value targets, we believe we can generate more powerful compound libraries for drug discovery purposes. Recursion is expanding our chemical libraires by leveraging MatchMaker and other Recursion-developed predictive ML models to select compounds that represent tractable starting points and have an increased probability of exhibiting biological activity on our phenomics platform. We believe that the scale of our total in-house chemical library is comparable to the scale of chemical libraries curated by some large pharmaceutical companies. We plan to substantially increase the size and diversity of our NCE library over the coming years through a combination of partnerships and investments in automated chemical microsynthesis in order to more fully understand novel biological and chemical relationships. Furthermore, we envision that an automated chemical microsynthesis system would integrate with existing sample management, synthesis and purification capabilities. With the completion of our recent wet-laboratory expansion, we now have the potential capability to store up to more than 60 million compounds (in plated formats) onsite.

The Recursion OS is built on top of a core technology stack that is highly scalable and flexible. We have adopted a hybrid-cloud strategy, leveraging the benefits of both public and private cloud infrastructure depending on the context and our needs:needs.

Modern approaches to drug discovery are built on significant datasets, in both structured and unstructured forms, as well as from various sources, proprietary, licensed, and public. Recursion enables access to the processData Universe through a combination of modern Data Lake / Warehouse tools for our structured data, and notifyproprietary tools for managing our large volume of unstructured data. Recursion combines these data sources as part of The Recursion Data Universe, which compromises the appropriate Recursionaut, providing them the tooling needed for manual intervention. Elements of our Enabling Software Tool suite include:following data sets, among others:

The Recursion Data Universe spans many petabytes of biological, chemical, and navigating tools arepatient-centric data, relatable across years of experiment execution and data types. We have also built a rapidly growing suite of in-house software applications designed to process and translate this data frominto rapidly actionable insights. Increasingly, these tools and data are joined in automated workflows to rapidly prosecute drug discovery programs.

The growing number of AI tools and intuitive user interfacesdatasets at Recursion – comprised of many wet-lab and dry-lab modules that can be connected – increases the complexity of our phenomapsearly drug discovery workflows. Moreover, each module often requires specific expertise to operate and is only accessible to highly trained data scientists or machine learning engineers. LOWE is an LLM agent designed to orchestrate complex drug discovery workflows using a natural language interface. These workflows chain together a variety of steps and tools, from finding significant relationships within Recursion’s maps of biology and chemistry to generating novel compounds and scheduling them for synthesis and experimentation. Through its natural language interface and interactive graphics, LOWE puts state-of-the-art AI tools into the hands of every drug discovery scientist at Recursion in a simple and scalable way.

Because LOWE deploys via a web-based application, the most up to date versions of data and modules are automatically propagated for usage so that version control is not an issue. At Recursion, we are working on an autonomous agent that interfaces with a set of drug discovery modules, whereby a human scientist would review a set of potential programs for further advancement. Whether running via human prompts or autonomously, LOWE maintains a precise record of the commands being made, versions of the data and modules being called, and results being relayed so that workflows could be replicated in the future and computational and experimental results can be collected as evidence for the progression of programs for our scientistsinternal pipeline, external partners, or regulatory groups.

The Digital Chemistry Platformacquisitions of Cyclica and Valence in May 2023 added industry-leading capabilities in digital chemistry, machine learning, and artificial intelligence to Recursion’s existing small molecule drug discovery capabilities. We are leveraging these capabilities to advance our internal and partnership drug discovery programs.

Late-stage clinical failures are the primary driver of costs in today’s pharmaceutical R&D model, due in part to inherent uncertainty in the clinical development and regulatory process.model. Reducing the rate of costly, late-stage failures and accelerating the timeline from hit to a clinical candidate would create a more sustainable R&D model. To achieve this more sustainable model, we believe that in its ideal state, a drug discovery funnel would morph from the being shaped like the letter ‘V’ to being shaped like the letter ‘T,’ where a broad set of possible therapeutics could be narrowed rapidly to the best candidate, which would advance through subsequent steps of the process quickly and with no attrition. Recursion’s goal is to leverage technology to reshape the typical drug discovery funnel towards its ideal state by moving failure as early as possible to rapidly narrowing the funnel into programs with the highest probability of success.

We believe we have made progress in reshaping the traditional drug discovery funnel in the following ways:

We have leveragedBy leveraging our Recursion OS to explore more than 170 diseaseand advance our programs, to a depth sufficient to quantify improvements in the time, cost and anticipated likelihoodswe have shown leading indicators of program success by stageimprovement when compared to the traditional drug discovery paradigm. Weprocess, particularly with respect to cost and time. Across all Recursion programs from late 2017 through 2023, the average amount of time to reach the validated lead stage is approximately 11 months. By the end of 2024, we believe that Recursion programs could reach the validated lead stage in about half that time or less. Ultimately, we believe that future iterations of the Recursion OS will enable even greater improvements. Ultimately, we look to minimizeimprovements minimizing the total dollar-weighted failure whileand maximizing the likelihood of success.

Over time, we believe continued successesThe Recursion OS has not only improved speed and improvements in any or all of the dimensions highlighted above will improve overall R&D productivity, allowingcost, but also led us to address targeted patient populations that may otherwise not be commercially viable using traditional drug discovery approaches. Furthermore, we have seen our unbiased approach lead us toexplore novel targets which we believe could enablegive us to outperform others in highlya competitive disease areasadvantage where multiple parties often simultaneously pursue a limited number of similar target hypotheses. These advantages potentially significantly expand the total addressable marketBelow one can see quantitative measures for our technology. However, the process of clinical development is inherently uncertain, and there can be no guarantee thathow we will achieve shorter development timelines with future product candidates.

All of the programs in our internal pipeline are built on unique biological insights surfaced through the Recursion OS where: (i) the disease-causing biologyetiology of the disease is well defined but the downstream effectssubsequent impacts of the disease-causedisease are typically poorly understood,generally obscure, the primary targets are typically considered undruggable, or the primary targets are not well knownextensively recognized in the context ofassociation with a particular disease and (ii) there is a high unmet medical need, no approved therapies, or significant limitations to existing treatments. Several of our internal pipeline programs could have potential market opportunities in excess of $1.0more than $1 billion in annual sales. We currently have fourfive programs in activeor planning to initiate Phase 2 clinical studies and we are preparing to submit an IND for a fifthsixth program to enter a Phase 1b/2 clinical study in earlyH2 2024. In addition to our clinical stage programs, we are actively developing dozens of preclinical and discovery programs.

We believe that the number of potential programs we can generate with our Recursion OS is key to the future of our company, as a greater volume of validated programs has a higher likelihood of creating value. Additionally, we believe that our large number of potential programs makes us an attractive partner for larger pharmaceutical companies. The static or declining level of R&D output at manyseveral large pharmaceutical companies means that they haveindicates an ongoing needrequirement for new projects to fillsustain their product pipelines.

REC-994 is an orally bioavailable, superoxide scavenger small molecule being developedcurrently under development for the treatment of symptomatic CCM. In Phase 1 SAD and MAD trials in healthy volunteers directed and executed by us, REC-994 demonstrated excellent tolerability and suitability for chronic dosing. CCM is among the largest rare disease opportunities and has no approved therapies to date. AREC-994 demonstrated excellent tolerability and suitability for chronic dosing in Phase 1 SAD and MAD trials in healthy volunteers directed and executed by Recursion. SYCAMORE, a Phase 2 randomized, double-blind, placebo-controlled, safety, tolerability and exploratory efficacy study is underway.underway, and Orphan Drug Designation has been granted in the US and EU. We expect to share top-linePhase 2 data in 2HQ3 2024. This trial was fully enrolled in June 2023, with the vast majority of participants who completed 12 months of treatment electing to enter the long-term extension portion of the study.

invasive and risky nature of these interventions, these options are reserved for a subset of patients with significant symptomatology and/or easily accessible lesions. Rebleeds and other negative sequelae of treatment further limit the effectiveness of these interventions. There is no approved pharmacological treatment that affects the rate of growth of CCM lesions or their propensity to bleed or otherwise induce symptoms. Hence, CCM can beremains a severe diseaseserious health condition resulting in progressive neurologic impairment and a high risk of death due to hemorrhagic stroke.

REC-994 is a small molecule therapeutic designed to alleviate neurological symptoms associated with CCM and potentially reduce the accumulation of new lesions. REC-994 is an orally bioavailable superoxide scavenger with pharmacokinetics supporting once-daily dosing in humans. The putative mechanism of action of REC-994 is through reduction of reactive oxygen species and decreased oxidative stress that leads to stabilization of endothelial barrier function. In addition, REC-994 exhibits anti-inflammatory properties which could be beneficial in reducing disease-associated pathology.

A subsequent Phase 1 Multiple Ascending Dose (MAD) study was conducted in 52 healthy human volunteers and was designed to investigate the safety, tolerability, and PK of multiple oral doses of REC-994, to bridge from the PIB

ARecursion initiated the SYCAMORE study, a two-part Phase 2 study is underway.trial in CCM patients in Q1 2022. Part 1 is a randomized, double-blind, placebo-controlled trial to investigate the safety, efficacy, and PK of daily doses of REC-994 (200 mg and 400 mg) compared to placebo in participants with symptomatic CCM over a treatment period of 12 months. Part 2 is an optional, double-blind, long-term extension (LTE) study of daily doses of REC-994 (200 mg and 400 mg) for participants completing Part 1 of the study. Currently, there is no development or regulatory precedent or registrational pathway for CCM drug development. Results from the ongoing Phase 2 study are expected to inform a pivotal trial design with guidance from the FDA.

To our knowledge, the REC-994 program is the only industry-sponsoredfirst industry sponsored therapeutic program in clinical trials for CCM. If approved, REC-994 would be the first pharmacologic disease-modifying treatment for CCM, one of the largest areas of unmet need in the rare disease space. Other ongoing research includes an investigator-initiated study of a marketed therapeutic and a preclinical industry-sponsored program.There are currently three other active programs in clinical development for CCM.

Neurofibromatosis type 2 (NF2) is an autosomal dominant, inherited, rare, tumor syndrome that predisposes affected individuals to multiple nervous system tumors, the most common of which are bilateral vestibular schwannomas, intracranial meningiomas, spinal meningiomas and other spine tumors such as ependymomas.

Although most meningiomas are benign, their location often makes complete resection untenable, and subsequently patients with NF2 experience loss of hearing, facial paralysis, poor balance, and visual difficulty. Spinal tumors can result in weakness and disability and some patients become wheelchair bound. Many patients with multi-tumor disease die in early adulthood. Due to the catastrophic nature of the disease and lack of non-surgical options for management, new approaches to treatment are needed, particularly those directed toward shrinking tumor burden.

Four Investigator-Sponsored Trials (ISTs) of REC-2282 (previously referred to as AR-42) have been completed. In study AR-42-001, REC-2282 was administered as monotherapy. In the other 3 trials, REC-2282 was administered in combination with anti-neoplastic agents: decitabine (AR-42-002), pazopanib (AR-42-003) and pomalidomide (AR 42 004), respectively. In these studies, REC-2282 was given to 77 patients with solid or hematological malignancies in doses ranging from 20 mg to 80 mg three times a week for three weeks followed by one week off-treatment in four-week cycles. Multiple patients were treated for multiple years using this dosing regimen at the 60 mg dose and the longest recorded treatment duration is 4.4 years at the 40 mg dose. The majority of adverse events were transient cytopenia that did not result in dose reduction or stoppage. The MTD in patients with solid tumors was determined to be 60 mg. The REC-2282 plasma exposure in patients with hematological malignancies and solid tumors generally increased with increasing doses. There were no consistent signs of plasma REC-2282 accumulation across a 19-day administration period nor obvious differences in PK between hematologic and solid tumor patients.

In another early Phase 1 pharmacodynamic IST conducted by Ohio State University, it appeared that REC-2282 suppressed aberrant activation of ERK, AKT and S6 pathways in vestibular schwannomas from adult patients undergoing tumor resection. These results may be difficult to achieve with single pathway inhibitors of ALK or MEK.

REC-4881 is an orally bioavailable, non-ATP-competitive, allosteric small molecule inhibitor of MEK1 and MEK2 being developedcurrently under development to reduce polyp burden and progression to adenocarcinoma in FAP patients. REC-4881 has been

FAP is characterized by progressive development of hundreds to thousands of adenomatous polyps in the lower gastrointestinal tract, mainly in the colon and rectum, and is associated with up to a 100% lifetime risk of colorectal cancer before age 40 if left untreated. StandardThe standard of care for patients with FAP is colectomy in late teenage years. Without surgical intervention, affected patients will progress to colorectal cancer by early adulthood. Post-colectomy, patients receive endoscopic surveillance every 6-12 months to monitor disease progression given the ongoing risk of malignant transformation.

Despite removing the main at-risk organ, approximately 50% of patients will develop adenomatous lesions in the neo-rectum. Once endoscopic management is no longer sufficient, additional surgical procedures are required. Similarly, these patients also develop duodenal (particularly ampullary) adenomas which also require endoscopic management. In the presence of larger adenomas and evidence of carcinoma, patients require additional localized surgery, including radical Whipple procedures. There are currently no approved therapies for FAP.

REC-4881 is an orally bioavailable, non-ATP-competitive, allosteric small molecule inhibitor of MEK1 and MEK2 (IC50 2-3 nM and 3-5 nM, respectively) that is being developed to reduce polyp burden and progression to adenocarcinoma in FAP patients. We obtained a global license for REC-4881 from Takeda Pharmaceuticals

Lending further support for the use of MEK inhibitors in FAP, studies have shown that ERK signaling is activated in adenoma epithelial cells and tumor stromal cells, including fibroblasts and vascular endothelial cells. In addition, genomic events resulting in alteration of mitogen-activated protein kinase signaling, such as activating mutations in KRAS, are frequent somatic events that promote the growth of adenomas in FAP. Overall, suppression of aberrant MAPK signaling in adenomas of FAP with REC-4881 has the potential to regress or slow the growth of these tumors by acting on core pathways driving their growth.