Cyclica Therapeutics, Inc.

When Recursion Pharmaceuticals and Exscientia announced plans to merge in August, it marked the first major consolidation in the early AI-driven biotech field. The all-stock deal, touted as an alliance between biology and chemistry , caught many in the industry by surprise. But new documents filed Thursday with the SEC show the deal had been in the works for a while, with executives first discussing the idea almost two years earlier. Recursion CEO Chris Gibson first met with Exscientia founder and then-CEO Andrew Hopkins on Oct. 26, 2022. At that time, they discussed the idea of combining the companies, each of which had been trying to make the case that AI had a place in the future of drug R&D. Both companies had built clinical-stage pipelines and inked multiple pharma partnerships over the years. Hopkins expressed “preliminary interest in a ‘merger of equals’” during that meeting, according to paperwork outlining the background of the merger. A month later, Recursion submitted a non-binding proposal to merge with Exscientia, offering the UK-based startup 35% ownership of the combined entity. But Exscientia’s board argued the proposal undervalued the company, and Hopkins officially told Recursion they wouldn’t move forward with the proposal. Two months later, Recursion came back with another all-stock proposal, this time offering Exscientia shareholders 37% ownership of the combined business. The company declined the proposal again, with management saying the proposal “would not significantly accelerate Exscientia’s growth or create a strategic advantage for Exscientia relative to the continued execution of its standalone plan.” Recursion attempted to move on, considering other deals and partnerships through 2023 and early 2024. In May 2023, it bought AI drug discovery startups Cyclica and Valence , mostly in equity. Then, in February, Exscientia unexpectedly fired Hopkins , its longstanding CEO, after allegations of inappropriate relationships with employees, and its chair resigned. Three months later, the company laid off nearly one-quarter of its workforce amid plans to cut $40 million in costs. And, that same month, Recursion CFO Michael Secora called Exscientia CFO Ben Taylor and an undisclosed board member to reignite the combination talks after Recursion’s board told the company to “more aggressively” pursue strategic acquisitions. Secora and Recursion board member Dean Li, Recursion’s co-founder and president of Merck Research Laboratories, met with Taylor to talk about combining companies. By July, Recursion again offered an all-stock deal in which Exscientia shareholders would get up to 25% ownership of the combined company — far lower than the initial offer. The companies hashed out the details over the coming weeks and settled on 26% for Exscientia shareholders. A month later, Recursion shared its first Phase 2 data in a “ moment of truth ” for the buzzy AI-driven field. The stock prices of Exscientia $EXAI and Recursion $RXRX have cratered 83% and 79%, respectively, since the companies went public. The deal is expected to close early next year.

Entered into a collaboration with Tempus giving Recursion access to over 20 petabytes of multimodal oncology data for the purpose of training causal AI models together with Recursion’s proprietary data and for designing biomarker and patient stratification strategies for clinical programsExpansion of Recursion’s in-house supercomputer, BioHive-1, with NVIDIA H100 GPUs will likely make it the most powerful computer wholly owned or operated by any biopharma company and among the 50 most powerful supercomputers on the Top500 ListUpdated collaboration with Bayer to deliver against their thematic focus in precision oncology with higher per program milestones and broader use of Recursion’s OS SALT LAKE CITY, Nov. 09, 2023 (GLOBE NEWSWIRE) -- Recursion (Nasdaq: RXRX), a leading clinical stage TechBio company decoding biology to industrialize drug discovery, today reported business updates and financial results for its third quarter ending September 30, 2023. “Since our founding we have led TechBio with a belief that the next generation of biopharma leaders would operate at the intersection of scaled datasets and accelerated computing,” said Chris Gibson, Ph.D., Co-founder and CEO of Recursion. “Today, we are thrilled to share our plans to continue leading the field forward at this nexus in order to advance a growing pipeline of both wholly owned and partnered programs towards impact for patients. First, on the data front, we are excited to announce a collaboration with Tempus, giving us access to over 20 PB of proprietary data in precision oncology. I believe these forward-genetic data, combined with Recursion’s multimodal reverse-genetic data, will continue strengthening our data-advantage. Second, on the compute front, we are pleased to announce a significant commitment to expand our BioHive-1 supercomputer to advance the exploration and construction of large AI models across our ever-growing proprietary biological data more rapidly and reliably. And finally, the evolution of our collaboration with Bayer and the recent exercise of our first oncology program option from Roche and Genentech highlights the growing appreciation of our Recursion OS by world-class teams in biopharma. We could not be more inspired about the future and are confident that the Recursion OS will continue to lead the transformation of BioTech into TechBio.” Recursion OS: Industrializing drug discovery to transform BioTech into TechBio Summary of Business Highlights Platform Tempus Collaboration Oncology-Focused, Precision Medicine Data: Tempus has built one of the world’s largest oncology-focused clinical and DNA/RNA molecular observational datasets. Our new collaboration with Tempus gives Recursion preferred access to these data. When combined with Recursion’s proprietary dataset of over 25 petabytes of interventional biological and chemical data, Recursion will now have approximately 50 petabytes of proprietary data fit for the purpose of machine learning at its disposal, enabling us to improve the training of causal AI/ML models of biology. When applied to our genome-wide reverse genetics platform, these data could facilitate the discovery of novel associations and mechanisms not otherwise identifiable in the clinical and forward genetics data from Tempus. Additionally, this patient-linked data will be used to support translating innovative therapeutics from Recursion’s platform directly to patients using novel biomarker and patient stratification strategies.Terms of the Tempus Collaboration: Recursion entered into an agreement with Tempus to access its patient-centric data as part of a 5-year licensing agreement. Recursion will make annual payments to Tempus in cash or equity ranging between $22M and $42M each year, up to $160M in aggregate, over the next 5 years in exchange for continued and updated data access and use rights for therapeutic development purposes. Supercomputer Expansion: We have committed to working with NVIDIA to expand BioHive-1, our on-premise supercomputer. After the expansion, which will be completed in the first half of 2024, BioHive-1 computational capacity will increase by over 4x (adding more than 500 NVIDIA H100 GPUs to the more than 300 NVIDIA A100s already in place). We project that upon completion and benchmarking, BioHive-1 will be in the top 50 most powerful supercomputers in the world across any industry (according to the Top500 list) and will be the most powerful supercomputer owned and operated by any biopharma company. These additional computational resources will continue to support the construction of the largest foundation models across biology and chemistry using Recursion’s vast datasets and data generation capabilities as well as tools based on interactive large language models and autonomous agents.Foundation Models: Our supercomputer expansion is meant to build on the deployment of our first Phenomics Foundation Model, PHENOM-1, which is a vision transformer utilizing hundreds of millions of parameters trained on billions of biological images from our proprietary phenomics library. PHENOM-1 demonstrated the scaling hypothesis within a biological context, namely that larger models trained on more diverse datasets lead to increased performance and emergent properties. With our recent acquisitions of digital chemistry company Cyclica and the deep-learning research team at Valence Discovery (now Valence Labs), the vast patient-centric data from Tempus and our own growing proprietary multi-omic datasets, we anticipate the construction and application of more foundation models and large language models across biology, chemistry and translation. Together, we believe these increasingly sophisticated models will enable us to drive new, better programs into clinical development both in our own pipeline and with our current and future partners at scale. Pipeline Cerebral Cavernous Malformation (CCM) (REC-994): Our Phase 2 SYCAMORE clinical trial is a double-blind, placebo-controlled safety, tolerability and exploratory efficacy study of this drug candidate in participants with CCM. This study was fully enrolled as of June 2023 with 62 participants and the vast majority of participants who have thus far finished their first year of treatment have enrolled in the long-term extension study. We expect to share Phase 2 proof-of-concept data in H2 2024.Neurofibromatosis Type 2 (NF2) (REC-2282): Our Phase 2/3 POPLAR clinical trial is a two part study of REC-2282 in participants with progressive NF2-mutated meningiomas due either to syndromic disease or initiating mutations in the meningiomas. Part 1 of the study is ongoing and is exploring two doses of REC-2282 in approximately 23 adults and 9 adolescents. We expect to share Phase 2 safety, tolerability, pharmacokinetics, and preliminary efficacy in H2 2024.Familial Adenomatous Polyposis (FAP) (REC-4881): Our Phase 2 TUPELO clinical trial is a two part study of REC-4881 in participants with FAP. Evaluation of three dose levels is ongoing, thereafter a dose expansion phase will commence evaluating the recommended Phase 2 dose in approximately 30 participants. We expect to share Phase 2 safety, tolerability, pharmacokinetics, and preliminary efficacy in H1 2025.AXIN1 or APC Mutant Cancers (REC-4881): Our Phase 2 LILAC clinical trial is a biomarker enriched two part study of REC-4881 in participants with unresectable, locally advanced or metastatic cancer with AXIN1 or APC mutations. The study will initiate in late Q4 2023 or early Q1 2024 and will explore the safety and efficacy of REC-4881 across three dose levels in 30-40 participants.Clostridioides difficile Infection (REC-3964): In early September 2023, we announced completion of our Phase 1 clinical trial and reported that REC-3964 had been well tolerated in healthy volunteers with no serious adverse events. We expect to initiate a Phase 2 proof-of-concept study in patients with recurrent Clostridioides difficile infection in 2024.RBM39 HR-Proficient Ovarian Cancer: RBM39 is a novel CDK12-adjacent target identified by the Recursion OS. We believe we can modulate this target to produce a therapeutic effect in HR-proficient ovarian cancer and potentially in other tumor types. This program is in the preclinical stage and IND-enabling studies are progressing. Partnerships Bayer: Bayer and Recursion have signed an update to their collaboration around a select set of oncology programs. This decision allows Bayer to leverage Recursion’s capabilities to identify novel targets and compounds applicable to traditionally undruggable oncology indications as well as Recursion’s access to expansive oncology-focused, patient-centric data from Tempus for their closely partnered programs. Under the amended and restated agreement, Bayer will pay Recursion increased per program milestones which may be up to $1.5B for up to 7 oncology programs as well as royalties on net sales. In this oncology-focused collaboration, Recursion will use many of the new tools it has developed since the collaboration was first signed to potentially identify and nominate programs rapidly.Roche-Genentech: In October 2023, Recursion announced that Roche-Genentech optioned its first partnership program in GI-oncology. This milestone represents a critical step in our joint efforts to initiate and advance new therapeutic programs using Recursion’s approach to map and navigate biology and chemistry. In the near-term, there is the potential for option exercises associated with map building or data sharing initiatives as well as option exercises associated with additional partnership programs. Third Quarter 2023 Financial Results Cash Position: Cash and cash equivalents were $387.3 million as of September 30, 2023. This cash position excludes the $3 million to be paid by Roche-Genentech for optioning its first partnership program in GI-oncology. Revenue: Total revenue was $10.5 million for the third quarter of 2023, compared to $13.2 million for the third quarter of 2022. The decrease was due to the timing of workflows from our strategic partnership with Roche-Genentech..Research and Development Expenses: Research and development expenses were $70.0 million for the third quarter of 2023, compared to $40.8 million for the third quarter of 2022. The increase in research and development expenses was due to increased platform costs as we have expanded and upgraded our capabilities.General and Administrative Expenses: General and administrative expenses were $29.2 million for the third quarter of 2023, compared to $19.5 million for the third quarter of 2022. The increase in general and administrative expenses was due to an increase in salaries and wages of $5.8 million and increases in software and depreciation expenses.Net Loss: Net loss was $93.0 million for the third quarter of 2023, compared to a net loss of $60.4 million for the third quarter of 2022.Net Cash: Net cash used in operating activities was $72.9 million for the third quarter of 2023, compared to net cash used in operating activities of $54.5 million for the third quarter of 2022. About RecursionRecursion is a clinical stage TechBio company leading the space by decoding biology to industrialize drug discovery. Enabling its mission is the Recursion OS, a platform built across diverse technologies that continuously expands one of the world’s largest proprietary biological and chemical datasets. Recursion leverages sophisticated machine-learning algorithms to distill from its dataset a collection of trillions of searchable relationships across biology and chemistry unconstrained by human bias. By commanding massive experimental scale — up to millions of wet lab experiments weekly — and massive computational scale — owning and operating one of the most powerful supercomputers in the world, Recursion is uniting technology, biology and chemistry to advance the future of medicine. Recursion is headquartered in Salt Lake City, where it is a founding member of BioHive, the Utah life sciences industry collective. Recursion also has offices in Toronto, Montréal and the San Francisco Bay Area. Learn more at www.Recursion.com, or connect on Twitter and LinkedIn. Media ContactMedia@Recursion.com Investor ContactInvestor@Recursion.com Consolidated Statements of Operations Recursion Pharmaceuticals, Inc. Condensed Consolidated Statements of Operations (unaudited) (in thousands, except share and per share amounts) Three months ended Nine months ended September 30 September 30Revenue 2023 2022 2023 2022 Operating revenue$10,102 $13,053 $33,252 $26,005 Grant revenue 431 107 432 162 Total revenue 10,533 13,160 33,684 26,167 Operating costs and expenses Cost of revenue 10,877 15,409 32,706 37,435 Research and development 70,007 40,836 171,744 111,716 General and administrative 29,199 19,488 80,364 61,761 Total operating costs and expenses 110,083 75,733 284,814 210,912 Loss from operations (99,550) (62,573) (251,130) (184,745) Other income, net 6,533 2,128 16,060 2,761 Net loss$(93,017) $(60,445) $(235,070) $(181,984) Per share data Net loss per share of Class A, B and Exchangeable common stock, basic and diluted$(0.43) $(0.35) $(1.16) $(1.06)Weighted-average shares (Class A, B and Exchangeable) outstanding, basic and diluted 214,327,186 173,435,970 203,090,637 172,122,974 Consolidated Balance Sheets Recursion Pharmaceuticals, Inc. Condensed Consolidated Balance Sheets (unaudited) (in thousands) September 30,December 31, 2023 2022 Assets Current assets Cash and cash equivalents$387,322 $549,912 Restricted cash 2,256 1,280 Other receivables 3,164 2,753 Other current assets 17,780 15,869 Total current assets 410,522 569,814 Restricted cash, non-current 7,629 7,920 Property and equipment, net 86,248 88,192 Operating lease right-of-use assets 34,062 33,255 Intangible assets, net 39,459 1,306 Goodwill 52,750 801 Other assets, non-current 155 - Total assets$630,825 $701,288 Liabilities and stockholders’ equity Current liabilities Accounts payable$4,265 $4,586 Accrued expenses and other liabilities 39,806 32,904 Unearned revenue 43,997 56,726 Notes payable 695 97 Operating lease liabilities 5,355 5,952 Total current liabilities 94,118 100,265 Unearned revenue, non-current 51,383 70,261 Notes payable, non-current 1,126 536 Operating lease liabilities, non-current 44,300 44,420 Deferred tax liabilities 1,931 - Total liabilities 192,858 215,482 Commitments and contingencies Stockholders’ equity Common stock (Class A, B and Exchangeable) 2 2 Additional paid-in capital 1,312,591 1,125,360 Accumulated deficit (874,626) (639,556) Total stockholder's equity 437,967 485,806 Total liabilities and stockholders’ equity$630,825 $701,288 Forward-Looking StatementsThis document contains information that includes or is based upon "forward-looking statements" within the meaning of the Securities Litigation Reform Act of 1995, including, without limitation, those regarding the outcomes and benefits expected from access to the real-world multimodal data held at Tempus; outcomes and benefits of deriving therapeutic hypotheses by linking molecular data and outcomes data; outcomes and benefits of expanding our supercomputer; early and late stage discovery, preclinical, and clinical programs, including timelines for data readouts; licenses and collaborations, including option exercises by partners and additional partnerships; prospective products and their potential future indications and market opportunities; Recursion OS and other technologies; business and financial plans and performance, including cash runway; and all other statements that are not historical facts. Forward-looking statements may or may not include identifying words such as “plan,” “will,” “expect,” “anticipate,” “intend,” “believe,” “potential,” “could,” “continue,” and similar terms. These statements are subject to known or unknown risks and uncertainties that could cause actual results to differ materially from those expressed or implied in such statements, including but not limited to: challenges inherent in pharmaceutical research and development, including the timing and results of preclinical and clinical programs, where the risk of failure is high and failure can occur at any stage prior to or after regulatory approval due to lack of sufficient efficacy, safety considerations, or other factors; our ability to leverage and enhance our drug discovery platform; our ability to obtain financing for development activities and other corporate purposes; the success of our collaboration activities; our ability to obtain regulatory approval of, and ultimately commercialize, drug candidates; our ability to obtain, maintain, and enforce intellectual property protections; cyberattacks or other disruptions to our technology systems; our ability to attract, motivate, and retain key employees and manage our growth; inflation and other macroeconomic issues; and other risks and uncertainties such as those described under the heading “Risk Factors” in our filings with the U.S. Securities and Exchange Commission, including our most recent Quarterly Report on Form 10-Q and our Annual Report on Form 10-K. All forward-looking statements are based on management’s current estimates, projections, and assumptions, and Recursion undertakes no obligation to correct or update any such statements, whether as a result of new information, future developments, or otherwise, except to the extent required by applicable law. Photos accompanying this announcement are available at:https://www.globenewswire.com/NewsRoom/AttachmentNg/816be0b8-7538-44d3-ae77-526a0f0a4ae0https://www.globenewswire.com/NewsRoom/AttachmentNg/f6a98a74-af5f-4d00-84fd-99f6738146f3

Pictured: Pills organized in a digital pattern/iStock, EvilWata When Alex Zhavoronkov launched Insilico Medicine in 2014, the company’s main focus was on using artificial intelligence as an alternative to animal testing for pharmaceutical research and development. Using deep learning, an advanced form of machine learning, the company aimed to predict how drug compounds would affect cells and what the side effects might be. Fast forward to 2023, and Insilico has evolved into an end-to-end AI-driven biotech company, deploying AI from lead and target discovery through clinical trials. Insilico’s Pharma.AI platform, backed by $2 trillion in machine-readable research data, contains over 40 generative algorithms. The firm uncovered the intracellular “Target X” for idiopathic pulmonary fibrosis (IPF) using AI analysis, and subsequently developed a novel small molecule inhibitor, INS018_055, which has shown potential in preclinical studies for improved fibrotic disease treatment with fewer side effects. In August, it became the first drug fully generated by AI to enter a Phase II trial. Making up the Pharma.AI platform, Insilico’s PandaOmics for target discovery, Chemistry42 for drug design and InClinico for clinical trial prediction “work together in an end-to-end feedback loop,” Zhavoronkov explained. “However, safety of the target is accounted for at two different stages,” said Alex Aliper, president of Insilico. First, PandaOmics uses relevant existing information to find the safest and most promising therapeutic targets, while Chemsitry42 enables multi-parametric optimization of the molecules. The latter includes ADMET predictors, a machine learning (ML) software tool, to assess how the body processes the generated molecule, covering aspects like absorption, distribution, metabolism, elimination and toxicity, as well as enzyme inhibition like cytochrome P450, which is involved in drug side effects. “Chemistry42 also has around 500 medicinal chemistry filters that filter out compounds with structural motifs known to be toxic,” Aliper told BioSpace. “Our initial studies have indicated that INS018_055 has the potential to address some of the limitations of current IPF therapies—including unpleasant side effects—and we are hopeful that the data from our current and future clinical trials will confirm this belief,” Zhavoronkov said. Schrödinger’s Selectivity New York- and Germany–based Schrödinger focuses on the safety of small-molecule drugs. Its platform blends AI/ML efficiency with physics-based accuracy, enabling its specialists to design and synthesize desired molecules, thus reducing time and costs. “In some cases, the clinical candidate has been selected after synthesizing just 100 molecules instead of the thousands made using traditional design approaches,” said Robert Abel, the company’s chief computational scientist. In order to minimize drug side effects, Schrödinger uses its platform to design molecules that bind potently and selectively to the intended protein target versus related proteins, explained Karen Akinsanya, president of R&D, therapeutics at Schrödinger. “This approach allows us to find molecules that might provide a therapeutic benefit without binding to hERG, CYPs, PXR and other well-known off-target proteins that are well known to cause side effects,” she told BioSpace. The hERG gene is crucial for heart rhythm regulation; CYPs, or cytochromes, are liver enzymes that metabolize drugs, potentially causing interactions; PXR, a cellular protein, helps process drugs and foreign substances by influencing enzyme production. These factors are vital in drug development and medication safety, Akinsanya said. “Protein structures, including binding sites, are crucial for our computational platform, but starting a drug discovery project often lacks enough experimental data for naive AI approaches due to the vast chemical space, which exceeds available data,” she explained. “Schrödinger addresses this by using advanced physics-based calculations to efficiently generate the needed binding affinity datasets.” Both Insilico and Schrödinger use AI to discover drugs that they claim would be safer than those hunted down using non-AI methods. Big pharma players also entered this space a few years ago, either directly or by partnering with tech companies or incubating smaller AI-based startups. AstraZeneca collaborated with BenevolentAI to discover new targets and predict potential drug side effects by analyzing scientific literature, clinical trial data and biomedical databases. Novartis employs AI for intelligent molecular design to develop safe molecules for patients. By analyzing various data sources, including chemical properties and biological interactions, the company aims to identify compounds with lower risk pro Meanwhile, Merck KGaA has been busy inking AI deals aimed at making safer medicines. In 2018, Merck struck an alliance with Canadian company Cyclica that uses the latter’s cloud-based technology to uncover new drug targets and identify off-target adverse effects. Then, in February 2022, the company announced a partnership to leverage Quris’ BioAI safety prediction platform, which uses miniaturized human tissues on a chip, nano-sensing and machine learning to predict the safety of drug candidates in humans. Confidence and Caution AI’s potential in drug development is often recognized with respect to saving time and money. However, its increasing deployment in drug safety studies elicits both confidence and caution from industry and researchers, according to experts such as Andreas Bender, a chemoinformatics researcher at the University of Cambridge, U.K. Bender, who has been working with AstraZeneca on computational drug safety studies, said there is often a “tech push and a science pull” about AI. Although data collection becomes easier, understanding the data’s signal is more critical, he told BioSpace. “We have quite a lot of data, but we don’t understand much of that pile, like how certain biological mechanisms relate to a phenotype,” he said. Using AI to identify potential side effects is “like [finding] a needle in a haystack.” Drug safety lies not just in how the molecule has been designed but also in other factors. For example, Bender explained, the use of excipients—the inert or slightly active substances used in formulating a drug compound into a drug product—and other additives can affect a drug’s safety profile, as can patient biology and environmental stimuli. “There are mechanistic side effects that you know where they come from and what to do about, but there are also idiosyncratic side effects that you don’t know where they come from, so you cannot predict such adverse reactions using AI,” he said. Hence, along with machine learning, his group’s research also integrates chemical biology and human physiology to predict drug toxicity. There are multiple other challenges, Bender continued, such as how different countries define specific side effects. “How do we translate, then, the [computational predictions] into decision-making?” he asked. “That integration is often where the real problems come from, and that’s why you always need to integrate computers with experiments—[to] follow up on predictions.” Still, he said, AI-based predictions of drug safety could improve the drug development process. “It is, rather, about making better decisions. I’m not saying nothing works, but also not that everything works; just . . . that finding meaningful signals in drug safety prediction is statistically more difficult and depends on several [kinds of] data, so promises must be cautious and realistic.” Vijay Shankar Balakrishnan is a freelance journalist based in Ludwigshafen, Germany. Reach him at LinkedIn.