Scipher Medicine, a pioneer in network-based and AI-driven precision immunology,  announced a strategic collaboration with Maxymune Therapeutics, a biotechnology company focused on research and development of novel therapeutics for immunological disorders. Through this partnership, Maxymune will leverage Scipher’s platform to capitalize on Scipher’s target ranking capabilities across research and development programs.

Under the terms of the agreement, Scipher will provide Maxymune with advanced target ranking and engagement optimization services through its hosted platform. This includes a customized target-ranking report that aligns Maxymune’s protein target of interest with Scipher’s multi-omics disease models. Scipher’s ability to rank targets on predicted efficacy has been validated on over 25 disease areas and provides critical insight on whether to pursue or stop development. The collaboration aims to accelerate the development of high-efficiency targets for autoimmune and inflammatory disease indications based on in-silico validation.

“Our platform is designed to de-risk therapeutic development by identifying the most promising targets through deep molecular network analysis,” said Dr. Reg Seeto, Chief Executive Officer of Scipher Medicine. “We are excited to support Maxymune’s innovative R&D programs with actionable data that aligns with our shared commitment to transforming the treatment landscape for immune-mediated diseases.”

“Our collaboration with Scipher combines Maxymune’s biological expertise with the power of Scipher’s AI platform to uncover unique insights into our target’s role in autoimmune diseases. By integrating these AI-driven discoveries with our rigorous in vitro and in vivo studies, we can triangulate findings, sharpen our discovery strategy, and move more rapidly toward transformative immunotherapies,” said Tatjana Naranda, President and Chief Operating Officer of Maxymune Therapeutics.

This collaboration reflects the growing demand for data-driven insights to inform early-stage target identification and improve the probability of clinical success via indication prioritization.