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World’s First Drug Candidate developed by Organoid and AI Doses First Patient in Phase 1 Study for first Diffusive Gastric Cancer Targeted Therapy

Signet Therapeutics (“Signet”), a clinical-stage biotech company leveraging organoids and AI to develop first-in-class cancer therapies, today announced that the first patient has been dosed in th...

Business Wire

NEW YORK: Signet Therapeutics (“Signet”), a clinical-stage biotech company leveraging organoids and AI to develop first-in-class cancer therapies, today announced that the first patient has been dosed in the Phase I clinical trial in China for its lead candidate, SIGX1094, for the treatment of diffuse gastric cancer (DGC) and other advanced solid tumors. This marks a significant milestone as SIGX1094 is the first pipeline drug discovered using AI and organoid disease model to enter clinical trials, and the first targeted therapy candidate to address the unmet clinical needs in DGC.

The Phase I study, currently underway at Beijing Cancer Hospital in China, aims to evaluate the safety, tolerability, and optimal dosing of SIGX1094 in patients with DGC, a cancer with limited treatment options. In preclinical studies, SIGX1094 has demonstrated strong promise in treating various other cancers, including ovarian, triple-negative breast, and pancreatic cancers, and has shown potential in combination therapies for KRAS and EGFR mutations.

“Our preclinical studies utilizing organoid models and AI have been encouraging, demonstrating SIGX1094’s potential to inhibit tumor growth and metastasis,” said Dr. Haisheng Zhang, founder and CEO of Signet Therapeutics. “This milestone validates the transformative power of integrating organoid and AI technologies to propel the entire drug discovery process—from drug target discovery to efficacy evaluation—accelerating the development of groundbreaking therapies for cancer patients.”

First Clinical-stage Candidate of the Organoid + AI Drug Discovery Paradigm

SIGX1094 is the first of a series of AI-Organoid drug discovery collaborations between Signet and XtalPi (2228.HK), a leading drug discovery platform company integrating quantum physics, AI, and robotics. Signet’s organoid models, derived from real-world cancer genomics data, can closely mimic human biology in 3D tissues to identify novel targets and evaluate drug efficacy with greater clinical relevance. By combining the strengths of organoids and AI, Signet identified the FAK as a potential target for treating DGC. XtalPi identified and delivered promising pre-clinical candidates in half a year, which are validated by Signet using its organoid platform. As a result, Signet advanced SIGX1094 from concept to clinical trials in less than four years. On November 18th, 2024, SIGX1094 received Orphan Drug Designation from the FDA, underscoring its potential as a treatment for rare and underserved cancer populations.

Founded on December 4th, 2020, Signet has successfully constructed a range of proprietary normal tissue and tumor organoids, including those of the stomach, liver, heart, and various cancers, which feature high-frequency mutation genes from real-world patients. On December 3rd, 2024, Signet secured approximately $10 million in a new round of pre-A financing, which will support SIGX1094’s clinical trials and advance the company’s second pipeline project toward IND approval.

About Signet Therapeutics

Signet Therapeutics is dedicated to developing innovative targeted cancer drugs using novel organoid models and AI. The company aims to meet the unmet medical needs of cancer patients, particularly those unresponsive to conventional therapies. Signet Therapeutics has successfully secured nearly $31 million in funding and has been recognized in Forbes Asia 100 to Watch list. Signet's first drug pipeline has entered Phase I clinical trial, with two additional pipelines progressing toward clinical stages. By combining the power of novel organoid disease models and AI, Signet is reshaping drug discovery and paving the way for more effective cancer treatments.

Fonte: Business Wire

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