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Antibody-Drug Conjugates (ADCs), as a part of precision medicine, target tumor cells by combining the specificity of antibodies with the potent effects of cytotoxins. The design and manufacturing of ADCs pose multifaceted challenges, involving the selection and integration of antigens, antibodies, linkers, and active payloads. It is projected that by 2028, the revenue generated from approved ADCs and those in Phase III clinical development will reach 26 billion USD. According to the Synapse database, currently, there are over 1700 ADC therapeutics in the global pipeline, with nearly 200 of them in clinical research stages, and several are in the process of applying for market approval. Recently, John Wu from the Boston Consulting Group and colleagues published a review article titled "The antibody–drug conjugate landscape" in Nature Reviews Drug Discovery. The authors analyzed data on 168 antibody-drug conjugates (ADCs) currently in clinical development and found that approximately 85% of these assets are aimed at solid tumor indications, with breast and lung cancers being the most common. Among the ADCs in phase III clinical trials, about 60% are considered second-generation assets that utilize established target/cargo mechanisms of action (MoA) combined with novel delivery components to achieve best-in-class characteristics. In early-stage development, higher biological risks are more evident; approximately 75% of the phase I/II ADCs are categorized as first-generation assets, featuring new targets and/or payload mechanisms, demonstrating potential as first-in-class drugs. In 2000, the United States Food and Drug Administration (FDA) approved the first ADC (Antibody Drug Conjugate) drug, Gemtuzumab ozogamicin (brand name Mylotarg), for the treatment of adult patients with Acute Myeloid Leukemia (AML). Gemtuzumab ozogamicin combines a humanized anti-CD33 monoclonal antibody with the potent cytotoxic drug calicheamicin, delivering the drug directly to the leukemia cells expressing CD33. However, due to the emergence of severe side effects, including abnormal liver function and the so-called Veno-Occlusive Disease (VOD), it was voluntarily withdrawn from the market by the manufacturer in 2010. Nevertheless, considering its efficacy in certain AML patients, following further research and risk assessment, Gemtuzumab ozogamicin was re-approved by the FDA in 2017 for the treatment of newly diagnosed CD33-positive AML in adult patients, as well as in pediatric patients aged two years and older. Since 2011, there has been a significant increase in the number of ADCs (antibody-drug conjugates) approved by the FDA. According to the latest data, the FDA has approved 14 ADCs for the treatment of cancer. These ADCs target a range of cancer types including breast cancer, lymphoma, leukemia, and multiple myeloma. Development of ADCs continues, with a considerable number of such drugs currently in clinical trials. The FDA is also reviewing several new ADCs. In reviewing ADCs, the FDA takes a stringent approach, assessing the safety and efficacy of these drugs, while also paying attention to potential off-target effects. The figure below evaluates the antibody-drug conjugates in clinical development phases, showcasing specific design innovations utilized in clinical assets. Linkers play a crucial role in ensuring the overall stability of Antibody-Drug Conjugates (ADCs) and the timely release of payloads, having been refined to enhance the therapeutic index. The payload determines the ADC's capability to eliminate cancer cells while minimizing adverse reactions. The figure below presents statistics on linkers and payloads for FDA-approved drugs. Cleavable linkers utilize differences between the tumor microenvironment and normal physiological conditions to release their drug load. This release can lead to the "bystander effect," where the released drug also affects neighboring cells not directly targeted by the ADC. While this can amplify therapeutic effects, it also poses risks: cleavable linkers are more prone to off-target toxicity. Many clinical trials favor the use of dipeptide, disulfide, and enzyme-cleavable linkers. Enzyme-cleavable linkers, such as those targeting tissue proteases, are designed to be cleaved by enzymes overexpressed in tumor tissues. This specificity adds an extra layer of targeting, further ensuring that the drug is released only at the intended site. When an ADC binds to its target through antigen-antibody interaction, non-cleavable linkers require the antibody component to degrade before the drug is released. This mechanism can reduce the bystander effect. However, advantages of non-cleavable linkers include greater stability and reduced risk of unexpected side effects. The key challenge in ADC design lies in the antibody's internalization and lysosomal degradation, which are crucial for payload activation. Additionally, payloads linked to polar amino acid linkers require special transport mechanisms to transition from the lysosome to the cytoplasm. Researchers have been exploring new methods to enhance the efficacy and safety of ADCs (Antibody-Drug Conjugates). By developing novel linkers and potent payloads, researchers aim to create ADCs that more effectively kill cancer cells while exhibiting lower toxicity to healthy cells. The ideal payload should display high cytotoxicity at low concentrations, enabling effective targeting of tumor cells. The selection of payloads depends on several parameters, including solubility, hydrophilicity, permeability, and modifiability. The design process also needs to consider potential challenges such as aggregation, bystander effects, and stability in the bloodstream. To assess the potential impact of ADC innovations, the diagram below categorizes next-generation technologies and compares them to the characteristics of ADCs that are already on the market. Given their novelty and/or the encouraging preclinical and clinical data, certain designs may be particularly noteworthy. Next-generation payloads: Small molecule degraders as a payload category exhibit significant potential, as they possess high specificity, sub-nanomolar efficacy, and the ability to target an extensive range of intracellular proteins associated with cancer. For instance, Orum Therapeutics' ORM-5029 delivers a selective degrader payload targeting GSPT1, a GTPase overexpressed in multiple cancers including gastric, colorectal, and breast cancer. Next-generation carriers: Engineering antibodies to have variable antigen-binding affinity can reduce toxicity to non-target tissues and increase tumor-specific exposure. Strategies include masking the Fab domains with a peptide that is cleavable by proteases overexpressed in tumors, and engineering antibodies to optimize pH-sensitive binding characteristics. For example, Mythic Therapeutics’ MYTX-011 is designed to have lower antigen affinity at the pH of endosomes, enhancing payload escape from endosomes. Next-generation linkers: Emerging linker technologies focus on controlled payload release independent of endogenous enzyme-mediated cleavage. For example, TagWorks’ preclinical ADC TGW101 uses an exogenously administered chemical activator to induce payload release. Next-generation conjugation: Incorporating non-natural amino acids into antibody carriers facilitates site-specific conjugation through oxime linkages. For example, Ambrx's ARX788 is a HER2-targeted ADC where a non-cleavable PEG linker is attached site-specifically to a non-natural amino acid. Globally, the trend of regulating ADCs (Antibody-Drug Conjugates) as biologics is growing, yet there are still some differences in regulatory requirements across different countries. For instance, in the United States, ADCs are regulated by the FDA as combination products. This means that both the small molecule drug component and the monoclonal antibody must receive FDA approval before the ADC can be marketed. In the European Union, ADCs are regulated by the European Medicines Agency (EMA) as biologics. In China, ADCs are regulated by the NMPA (National Medical Products Administration) as biologics, and draft guidelines on ADC CMC (Chemistry, Manufacturing, and Controls), clinical, and non-clinical aspects have already been issued. How to obtain the latest research advancements in the field of biopharmaceuticals? In the Synapse database, you can keep abreast of the latest research and development advances in drugs, targets, indications, organizations, etc., anywhere and anytime, on a daily or weekly basis. Click on the image below to embark on a brand new journey of drug discovery!

Fierce competition. Thin pipelines. Patent cliffs. The stakes are sky-high for pharmaceutical companies and investors alike. With biotech firms often going public before generating revenue, valuations hinge on the potential for FDA approval of their assets. Herein lies the power of artificial intelligence (AI): by predicting the likelihood of success for these assets, we can uncover undervalued companies, offering a strategic edge not only to hedge funds and VCs but also to big pharma companies considering acquisitions. In this dynamic landscape, projected to see M&A activities reaching as high as $275B in 2024, the ability to discern and invest in promising biotech assets early is crucial. At Intelligencia AI, our methodology offers a clear edge by delivering superior, real-time insights into the probability of success (PoS) for Phase 1 and Phase 2 assets gaining FDA approval. We used our predictions to create a virtual portfolio that generated 60% returns in one year and 3 out of the top 10 companies we highlighted were acquired. This advanced predictive capability is pivotal for stakeholders to identify promising acquisition and in-licensing opportunities with a higher degree of accuracy and confidence. Our methodology has proven to be highly effective as a standalone strategy and can also complement established approaches. Achieving Results: A Glimpse Behind the Curtain At Intelligencia AI, we used our proprietary, patented probability of success (PoS) prediction technology to create our own virtual portfolio* of publicly traded biotech companies. Our portfolio was constructed with early-stage, pre-proof-of-concept biotech companies with oncology pipelines and a market capitalization between $100M and $1B. Our investment strategy focused on ranking companies based on predicted overperformance against historic benchmarks and selecting the top percentile of ranked companies. The ranked list discussed here was produced algorithmically. Overall, results can be further qualified and prioritized by our team of expert analysts when further dimensions may be relevant to our partners. Biotech companies ranked by their expected overperformance. Intelligencia AI’s approach has been prospectively validated with time-stamped predictions from April 21, 2023. By March 21, 2024, Intelligencia AI’s portfolio of top 40 ranked biotechs achieved a 60% return and a Sharpe Ratio of 1.83. In comparison, similar market cap companies had a 25% return, while the biotech market benchmark index (XBI-ETF) saw a 17% return, underscoring the strategy's effectiveness. This approach has also been back-tested over a 5-year period. Performance of Intelligencia AI portfolio of top 40 ranked biotechs compared to the rest of the biotech market. The same methodology can be applied continuously incorporating the latest information to construct the most up-to-date portfolios. We continued to construct such time-stamped pro Below we highlight one constructed in late November 2023 displaying top ten picks from that portfolio. Highlights include multiple recently acquired early-stage biotechs. Highlights from Intelligencia AI’s portfolio of top 10 ranked biotechs on November 28, 2023. Harpoon Therapeutics (HARP), which has developed a portfolio of novel T-cell engagers, entered into a definitive agreement with Merck (MSD) on January 8, 2024. We singled out Harpoon Therapeutics’ assets in clinical development as standing out from competitive products. Both the lead candidate, HPN328 targeting T-cell engager, and HPN217 targeting B-cell maturation antigen (BCMA), were included in the acquisition. PoS distribution of ongoing assets similar to Harpoon’s candidate HPN217 targets B-cell maturation antigen (BCMA). Harpoon’s candidate had been outperforming most development programs within the same indication and stage of development, belonging to the top quartile. Ambrx (AMAM), an early-stage biotech with proprietary Antibody Drug Conjugate (ADC) technology, entered into a definitive agreement with Johnson & Johnson on January 8, 2024. We singled out two of the programs currently in early-stage clinical development as highly promising. Both novel ADC candidates, ARX788 and ARX517, were part of the acquisition. Kinnate Biopharma Inc. (KNTE), a clinical-stage precision oncology company, entered into a definitive agreement with XOMA Corporation on February 16, 2024. We highlighted two of their programs, Exarafenib and KIN-3248 treating melanoma and intrahepatic cholangiocarcinoma respectively, which had probabilities of success that far exceeded their expected historical benchmarks. Better Predictions, Better Investment and Clinical Development Decisions With Validated PoS Assessments The ability to create the above-described composite biotech scores on the basis of PoS is driven by highly accurate, prospectively validated PoS predictions for specific programs. Built on a unique, proprietary database, Intelligencia AI’s platform brings together expertly curated and harmonized clinical development, biological and regulatory data, which feeds our AI algorithms to assess drug approval and phase transition probabilities. Our algorithms show an 83%** accuracy in predicting FDA approvals for Phase 2 oncology programs, reaching 90% retrospectively. We achieve this performance by using standard calibration techniques and continuously validating our predictions against real FDA outcomes. In conducting a 4-year, ongoing prospective study, we demonstrate the high accuracy of our algorithms for early-stage development programs. In 2019, we collaborated with a leading pharmaceutical company to assess PoS for all FDA-track, industry-led, interventional Phase 1 and Phase 2 non-small cell lung cancer (NSCLC) and melanoma programs. We have been monitoring these programs since then, with 140 failures and 6 approvals. Our platform accurately predicted approval for four drugs, including novel ones and three receiving accelerated approvals: Amivantamab, Tepotinib, Tebentafusp, and Selpercatinib. Our PoS predictions for these programs were significantly higher than for those of similar-stage peers, even with relatively limited information at the time. Also, by time stamping the PoS assessments, we show that the accuracy of the PoS predictions has further improved in later releases of our algorithms. The graphs show the distribution of approved and failed programs, based on the PoS assigned by Intelligencia AI. Our algorithms have consistently assigned a significantly higher PoS to programs that moved on to approval and lower probability to programs that eventually failed. Importantly, our accuracy has improved over time, as the comparison between 2019 (left panel) and 2022 (right panel) shows. Conclusion and Future Outlook Our AI-driven approach in assessing biotech companies based on the risk of their development portfolio constitutes an innovative approach to identify assets for acquisition and in-licensing. The returns realized with our approach highlight its accuracy and demonstrate its capability to guide timely, informed and impactful decisions. These capabilities allow us to help our partners quickly pinpoint overlooked opportunities and realize superior returns. As we continue optimizing the use of AI for risk assessment in drug development, we are committed to supporting life science companies in bringing groundbreaking therapies to the market and transforming patients’ lives. Visit intelligencia.ai to discover more and request a demo. Authors: Dimitrios Skaltsas, CEO and Co-founder, Intelligencia AI Bara Badwan, Data Scientist, Intelligencia AI Panos Karelis, Director of Customer Experience & Insights, Intelligencia AI *This material is provided for informational purposes only, and it is not, and may not be relied on in any manner as investment advice or as an offer to sell or a solicitation of an offer to buy an interest in any fund or investment vehicle managed by Intelligencia AI or any other Intelligencia entity. The information and data are as of the publication date unless otherwise noted, and Intelligencia has no obligation to update such information or data. Certain information contained herein has been obtained from third-party sources. Although such content is believed to be reliable, it has not been independently verified as to its accuracy or completeness and cannot be guaranteed. **Based on Intelligencia AI predictions as of May 2022 for oncology programs with definite announcements in 2023. ####