Arpeggio Biosciences, Inc.

PT-112's mechanism of action promotes immunogenic cancer cell death through ribosomal biogenesis inhibition and organelle stress NEW YORK, Oct. 14, 2023 /PRNewswire/ -- Promontory Therapeutics Inc., a clinical stage biotech company advancing immunogenic small molecule approaches in oncology, today presented data on its lead therapeutic candidate PT-112, detailing its early molecular effects that culminate in immunogenic cancer cell death (ICD). The presentation was made at the American Association for Cancer Research (AACR) – National Cancer Institute (NCI) – European Organisation for Research and Treatment of Cancer (EORTC) International Conference on Molecular Targets and Cancer Therapeutics, taking place October 11-15, 2023 in Boston. The study was designed to clarify early molecular effects of PT-112 that are known to culminate in ICD by assessing nucleolar protein phenotypes in human prostate cancer cells treated with PT-112 using antibody labeling, followed by confocal microscopy. Researchers also conducted nascent RNA sequencing in human cancer cells (non-small cell lung, prostate and renal cell carcinoma) following one and six hours of PT-112 treatment. "Our study shows that PT-112 inhibits ribosomal biogenesis in cancer cells. This was observed in all the cell lines assessed after short exposure durations, indicating it is central to PT-112's mechanism of action and its ability to induce selective immunogenic cancer cell death," said Tyler Ames, PhD, Promontory Therapeutics Senior Vice President of Research and Development. "While further validation of these findings is ongoing, this work deepens our understanding of PT-112's specific molecular effects. The present findings, together with prior validation of its ICD effects, and evidence of durable clinical activity of PT-112, position it as a promising small molecule cancer immunotherapy." Study findings include: PT-112 rapidly causes ribosomal biogenesis (RiBi) inhibition and nucleolar stress, likely driving previously observed PT-112-induced cancer organelle stresses and ICD. At early timepoints in prostate cancer cells, PT-112 caused evident nucleolar protein translocation, a hallmark of nucleolar stress and disruption of RiBi. Consistent with the above, PT-112 induced statistically significant repression of pathways related to RiBi, ribosomal RNA (rRNA) processing and translation, crossing prostate, renal and lung cancer cell lines. Notably, a pattern across these pathways was the reduced expression of many ribosomal genes, consistent with RiBi inhibition. Collaborating with the Baylor College of Medicine and with Arpeggio Biosciences in Boulder, Colorado, the Promontory research team used confocal microscopy and nascent RNA sequencing, and performed analysis using Enrichr across multiple databases to identify biological pathways linked to clusters of differentially expressed genes (p ≤ 0.001) in PT-112-treated cells. PT-112 is currently in Phase 2 clinical trials in a late-line setting of metastatic castration-resistant prostate cancer, and in thymic epithelial tumors. The latter study is led by the National Cancer Institute under a formal collaboration. For more information about Promontory Therapeutics and PT-112, visit . About PT-112 PT-112 is the first small-molecule conjugate of pyrophosphate in clinical development in oncology. PT-112 has numerous advantages — including its tolerability and inhibition of ribosomal biogenesis (RiBi) which leads to immunogenic cell death (ICD), through the release of damage associated molecular patterns (DAMPs) that bind to dendritic cells and lead to downstream immune effector cell recruitment in the tumor microenvironment. PT-112 represents a highly potent inducer of this immunological form of cancer cell death. Further, PT-112 harbors a property known as osteotropism, or the propensity of the drug to reach its highest concentrations in certain areas of the bone, making it a candidate for treatment of patients with cancers that originate in, or metastasize to, the bone. The first in-human study of PT-112 demonstrated an attractive safety profile and evidence of long-lasting responses among heavily pre-treated patients and data were published in eClinicalMedicine, part of The Lancet. The combination Phase 1b dose escalation study of PT-112 with PD-L1 checkpoint inhibitor avelumab in solid tumors was reported in a mini-oral presentation at the ESMO 2020 Virtual Congress and the Phase 2a dose confirmation cohort in non-small cell lung cancer (NSCLC) patients was reported at ESMO I-O 2022. The Phase 1 study in patients with relapsed or refractory multiple myeloma presented at ASH 2020 is the third completed Phase 1 study of PT-112. Monotherapy Phase 2 development is ongoing in mCRPC, and includes the Phase 2 proof of concept study in thymic epithelial tumors under the company's formal CRADA with the NCI. Preliminary data from the NCI study were published at ASCO 2023 and at the ITMIG 2023 annual meeting, and show evidence of anti-cancer immune activation by PT-112. About Promontory Therapeutics Promontory Therapeutics Inc. is a privately held, clinical stage drug development company focused on small molecule immunotherapy in cancer. Clinical data generated across three Phase 1 studies of lead molecule PT-112 have demonstrated single-agent and combination anti-cancer activity and an attractive tolerability profile. The Company's lead Phase 2 study in late-stage metastatic castration-resistant prostate cancer patients is underway in the United States and France. The company applies a borderless collaboration model for research and development work, which has been conducted in the United States, Europe and Asia. As part of its recent expansion into France, Promontory was selected as the first international company member of the Paris-Saclay Cancer Cluster, Europe's emerging biotech hub for oncology. The company also has an active Phase 2 trial underway with the National Cancer Institute utilizing PT-112 in thymic epithelial tumors (TETs), a rare disease with no FDA approved drug, for which PT-112 has received FDA Orphan Drug designation. To learn more about Promontory Therapeutics, visit the company's website here. CONTACTS: Promontory Therapeutics Brooke Raphael, MS VP, Strategy & Operations Tel: +1 (646) 974-6453 Email: [email protected] Investors: Stan Musial Tel: +1 (646) 222-6932 Email: [email protected] ICR Westwicke Media: Alexis Feinberg Tel: +1 (203) 939-2225 Email: [email protected] SOURCE Promontory Therapeutics Inc.

The expanse of disease-causing proteins is fertile hunting ground for drug research. Many biotech startups are trying to find small molecules capable of binding to these targets. That’s great when it works, but many protein targets remain elusive, said Raphael Townsend, founder and CEO Atomic AI. Rather than pursue disease-causing proteins, Townsend’s startup aims for a different target: the RNA that carry instructions for making these proteins. Atomic AI uses artificial intelligence to find ways to drug RNA and it’s now out of stealth backed by $35 million. “By targeting the RNA instead, you’re giving yourself new ways of targeting these untreatable diseases,” Townsend said. The Series A round of financing announced Wednesday was led by Playground Global. In order to drug RNA, scientists need to first get a better understanding of it. Proteins are relatively well understood, with hundreds of thousands of known protein structures, Townsend said. By comparison, the human transcriptome, the complete set of all RNA, is poorly understood. The hundreds of known RNA structures are less well mapped out compared to proteins, Townsend said. That’s key because there’s growing recognition that RNA plays a major role in disease on its own, he added. Proteins fold and change shape, which can make them difficult to hit with a small molecule. But RNA is significantly more flexible making it more of a moving target, Townsend said. The technology of San Francisco-based Atomic AI maps out the transcriptome with an approach that combines wet lab experiments with computational analysis. Data generated by the wet lab are used to train the AI to discover novel targets on the three-dimensional structure of RNA, Townsend said. The AI makes predictions that inform additional wet lab experiments. Those results feed additional AI analysis, continuing a virtuous cycle. Atomic AI’s technology is based on research stemming from Townsend’s PhD work at Stanford University. That research was published in the journal Science in 2021, the same year Atomic AI formed. Since then, the company has made advances with its algorithms and its wet lab, Townsend said. The technology, now called Platform for AI-driven RNA Structure Exploration (PARSE) has also improved in speed and accuracy. The new capital enables Atomic AI to scale the platform, enabling the startup to become a drug discovery organization, Townsend said. The company will begin to narrow down the targets it will pursue. Townsend declined to identify specific diseases Atomic AI could pursue, but he said the technology could be used to discover small molecules for use in oncology, neurodegenerative disorders, cardiology, rare disease, and infectious disease. The startup’s initial research will focus on identifying the parts of the transcriptome that are even targetable, Townsend said. Atomic AI isn’t the first biotech aiming to drug RNA, and in addition to having an earlier start some of these startups already have partnerships with big pharma companies. The most advanced program of Arrakis Therapeutics is an oncology compound in lead optimization. The Waltham, Massachusetts-based company has a drug discovery alliance with Roche. Skyhawk Therapeutics is another Waltham-based company developing RNA-targeting small molecules. That company has alliances with Bristol Myers Squibb, Merck, and Takeda Pharmaceutical. Rather than directly target RNA, Remix Therapeutics is developing drugs that target parts of the cell that process it. Nearly a year ago, Cambridge, Massachusetts-based Remix inked a research alliance with a Johnson & Johnson subsidiary. More recently, Boulder, Colorado-based Arpeggio Biosciences unveiled a $17 million Series A round of funding. Townsend recognizes the other companies pursuing RNA-targeting small molecules, but he says what sets Atomic AI apart is the wet lab component of its platform. Companies that take a purely AI approach to RNA will have difficulty because there’s just not much RNA data out there for these technologies to analyze, he explained. Now that Atomic AI is out of stealth, Townsend said he’s looking for potential partnerships. While the startup’s internal research will focus on developing small molecule drugs, Townsend said partnerships will focus on using PARSE to develop new RNA-based medicines. The platform’s ability to predict how RNA folds and forms new structures can be used to design new RNA medicines, he explained. The technology also holds potential for improving certain aspects of RNA-based medicines, such as stability. For example, a more stable RNA molecule could avoid the ultra-cold storage required of the messenger RNA-based Covid-19 vaccines. Atomic AI initially raised $7 million in a 2021 seed financing led by 8VC. That firm also invested in the Series A round, which included the participation of Factory HQ; Greylock; NotBoring; AME Cloud Ventures; and angel investors including former GitHub CEO Nat Friedman; Doug Mohr; Curai CEO Neal Khosla; and Patrick Hsu, a University of California, Berkeley professor, and Arc Institute co-founder. Image by libre de droit, Getty Images

DUBLIN, Jan. 5, 2023 /PRNewswire/ -- The "Investor Series: Opportunities in the Artificial Intelligence in Drug Discovery Market" report has been added to ResearchAndMarkets.com's offering. The discovery and identification of novel drug candidates is a time-intensive process, which is fraught with several challenges. One of the main concerns associated with the drug development process is the high attrition rate, which is often linked to the trial-and-error method adopted for lead identification. In this context, only a small percentage of pharmacological leads are eventually translated into potential candidates for clinical studies. Further, of these candidates, nearly 90% are unable to advance further in the development process. This, in turn, leads to a significant loss for drug developers, in terms of both resources and finances. Usually, a prescription drug requires at least 10 years to reach the market, and an average investment of over USD 2 billion. In addition, it is reported that the drug discovery phase accounts for about one-third of the aforementioned costs. In recent years, artificial intelligence (AI) has emerged as prominent tool, demonstrated to have the potential to address a number of existing challenges. As a result, players engaged in the pharmaceutical domain have started implementing AI based tools to better inform their drug discovery and development operations, using available chemical and biological data. Currently, a number of AI-based techniques, including machine learning, deep learning, supervised learning, unsupervised learning and natural language processing are being used across various stages of the drug development process. Specifically, AI-based solutions are being extensively used in combination with deep learning algorithms to produce actionable insights for target identification, hit generation, as well as lead optimization. Such solutions are anticipated to increase the overall R&D productivity and reduce clinical failure of product candidates. Moreover, estimates suggest that, in 2022, the adoption of AI-based solutions for drug discovery are likely to enable savings worth USD 8.57 billion, with market projections suggesting cost savings of USD more than 28 billion by 2035. Despite the fact that niche startups are spearheading the innovation in this domain, several big pharma players are also actively acquiring capabilities for these technologies. Numerous tech giants, such as Google, IBM and Microsoft, have either developed their proprietary products or are offering solutions through collaborations with other industry stakeholders; for instance Google's DeepMind and IBM Watson. Even though only a few of such AI-based platforms have gone public, developers have experienced considerable growth in share value as their respective platforms / product candidates progressed through the various stages of development. Taking into consideration both the historical and contemporary scenarios, we believe that the AI-based Drug Discovery Market presents lucrative investment opportunities for both short- and long-term investors. Scope of the Report The "Investor Series: Opportunities in the Artificial Intelligence in Drug Discovery Market (Focus on Need for AI-based Drug Discovery, Market Landscape of Key Players, Analysis of Product Offerings and Affiliated Value Propositions, Insights from Historical Funding Activity, Startup Health Indexing, Potential Venture Opportunities, Financial Analysis of Key Public Ventures, Market Forecast and Opportunity Analysis, Insights from Publicly Disclosed Investor Exits and Key Acquisition Targets)" report provides detailed information on the AI-based Drug Discovery Market, along with a focus on drug discovery platforms, service and technology providers. It offers a technical and financial perspective on how the opportunity in this domain is likely to evolve, in terms of future business success, over the coming decade. The information in this report has been presented across multiple deliverables, featuring MS Excel sheets (some of which include interactive elements) and an MS PowerPoint deck, which summarizes the key takeaways from the project, and insights drawn from the curated data. The report features the following details: A qualitative and quantitative (wherever information was available) perspective on the current need for AI in the drug discovery domain. It presents details on the key applications of AI in drug discovery, along with information on the benefits of using such methodologies over conventional discovery approaches. Further, it highlights various challenges faced during various stages of drug discovery, and the opinions of representatives from key stakeholder companies involved in this domain. A detailed analysis of AI-based drug discovery focused companies that were established post 2005, featuring inputs on observed trends related to basic input parameters, such as year of establishment, location of headquarters, company size, and type of venture. A quantitative perspective on the relative health (based on basic company details, product details, financing activity, and estimated revenues and profits) of companies that have been described in detail in this report. This analysis is based on a proprietary scoring criterion, which was informed via secondary research. An assessment of the various products and affiliated services, offered by the companies mentioned above, featuring analysis based on number and types of services / platforms, and an informed perspective on the value of the aforementioned offerings based on multiple relevant aspects, namely intellectual capital related value, value to end users, developer value, and others. A company competitiveness analysis, which offers a quantitative basis for comparing the strengths / contributions of various industry stakeholders that are involved in providing AI-based services and platforms for drug discovery, captured in this report. It is worth mentioning that this analysis is based on the insights generated from the abovementioned relative health indexing and value proposition analyses. A detailed analysis of the funding and investment activity that has taken place in this domain, since 2011. It also includes financing category-wise trends, describing the relative maturity (in terms of number of funding instances and total capital raised) of the key companies discussed in the report. Further, it features a list of the leading investors in AI in drug discovery market, based on their participation in financing activity in this industry segment. An elaborate review of the overall AI-based Drug Discovery Market from a financial perspective, including detailed fundamental (insights from the balance sheet, and key financial ratios) and technical analyses (insights from historical and recent stock price variations, and analysis using popular stock performance indicators) of financial data of the publicly listed companies within the market landscape dataset. A business risk analysis, focused on some of the major categories of risk that are usually discussed in the industry, namely operations-related risks, overall business-related risks, financial risks, product / technology associated risks, and social, economic, environmental and political risks. Case studies of instances where investors have exited various AI-based drug discovery-related ventures, offering insights on returns on investment made (based on availability of data). Leveraging the abovementioned details, the report offers an informed opinion on the future outlook for investors in the AI-based Drug Discovery Market. A key acquisition targets analysis, based on the insights generated during the course of this study, highlighting some of the promising early-to-mid stage business ventures around which there is likely to be interest for future acquisitions / mergers. Key Topics Covered: Excel Deliverables 1. KEY PLAYERS AND PRODUCTS DATASET 2. FUNDING AND INVESTMENT ANALYSIS 3. POTENTIAL INVESTMENT OPPORTUNITIES 4. FUNDAMENTAL AND TECHNICAL FINANCIAL ANALYSIS 5. BUSINESS RISK ANALYSIS 6. MARKET FORECAST AND OPPORTUNITY ANALYSIS 7. RETURNS ON INVESTMENT PowerPoint Deliverables 1. Context 2. Project Approach 3. Project Objectives 4. Executive Summary Section I: Need for AI-based Drug Discovery Market and Market Landscape 5. AI-BASED DRUG DISCOVERY MARKET 6. Market Landscape 7. Product Landscape and Company Health Indexing 8. Value Proposition Analysis 9. Company Competitiveness Analysis Section II Analysis of Investments 10. Funding and Investments Analysis Section III Financial Analysis and Assessment of Business Risks 11. Financial Analysis of Public Ventures 12. Business Risk Analysis Section IV Market Forecast and Opportunity Analysis 13. Market Forecast and Opportunity Analysis Section V Analysis of Returns on Investment, Key Acquisition Targets and Promising Investment Opportunities 14. Analysis of Returns on Investment 15. Key Acquisition Targets 16. Promising Investments Analysis 17. Conclusion 18. Appendices Selection of Companies Mentioned 3BIGS 99andBeyond A2A Pharmaceuticals A2i Therapeutics AbCellera Absci Accutar Biotech Acellera AcuraStem Adagene Aganitha AI Therapeutics AI VIVO Ai-biopharma Aiforia Aigenpulse Ainnocence Alphanosos Anagenex Anima Biotech Antiverse ApexQubit Aqemia Arctoris Ardigen Aria Pharmaceuticals Arpeggio Biosciences Arzeda Atavistik Bio Atomwise Auransa BenevolentAI BERG BigHat Biosciences BioAge Labs Biolojic Design Biorelate BioSymetrics bioSyntagma biotx.ai CardiaTec Biosciences Causaly Cellarity Celsius Therapeutics CHARM Therapeutics ChemAlive ChemPass Cloud Pharmaceuticals Collaborations Pharmaceuticals Collective Scientific Cortex Discovery Cyclica CytoReason Data2Discovery Data4Cure Deargen Deep Genomics DeepMatter DeepCure Deepflare DeepTrait Denovicon Therapeutics DevsHealth DEXSTR Differentiated Therapeutics Dyno Therapeutics And More For more information about this report visit About ResearchAndMarkets.com ResearchAndMarkets.com is the world's leading source for international market research reports and market data. 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