Promise Bio founders (company pic)
Promise Bio is an Israeli medtech startup transforming immune-mediated disease treatment with advanced precision medicine solutions. The firm emerged from stealth with an $8.3 million seed round of funding led by Awz Ventures with funding via AION Lab’s venture seeding track from AstraZeneca and Pfizer.
Promise Bio is poised to revolutionize precision medicine. With the recent influx of funding, including a grant from the Israel Innovation Authority and strategic partnerships with industry leaders, the company is well-positioned to scale its cutting-edge technology, expand its research capabilities, and build the world’s largest PTM database. These partnerships offer more than just financial support; they provide access to critical industry expertise and global networks, accelerating the development of innovative treatments for immune-mediated diseases.
Promise Bio’s innovative platform is built on years of research by Dr. Assaf Kacen and Prof. Yifat Merbl at the Weizmann Institute of Science. Their groundbreaking work, published in Nature Biotechnology in 2023, focused on protein modifications and immune response. Building on this foundation, Dr. Kacen developed a system that can comprehensively profile dozens of post-translational modifications (PTMs) directly from mass-spectrometry data, eliminating the need for time-consuming and complex lab procedures.
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Post-translational modifications (PTMs) are chemical modifications of proteins that occur after protein synthesis (translation). These modifications can significantly alter a protein’s function, stability, localization, and interactions with other molecules.
“Determining the change in the protein level or relying on just blood count caused by a complex immune response is like looking at a black-and-white TV screen with poor resolution,” said Dr. Assaf Kacen, Co-Founder & CTO of Promise Bio. “Our platform extracts modifications that occur to the proteins resulting from the disease; those specific changes inform us about aberrations in protein function or regulation. Interpreting it with machine learning approaches is like watching a colored TV with an unparalleled high-resolution view of the underlying biology.”
