Anti-CEA-CAR-T Cells is under clinical development by Shanghai First Song Therapeutics and currently in Phase I for Metastatic Adenocarcinoma of The Pancreas. According to GlobalData, Phase I drugs for Metastatic Adenocarcinoma of The Pancreas have an 88% phase transition success rate (PTSR) indication benchmark for progressing into Phase II. GlobalData’s report assesses how Anti-CEA-CAR-T Cells’s drug-specific PTSR and Likelihood of Approval (LoA) scores compare to the indication benchmarks. Buy the report here.

GlobalData tracks drug-specific phase transition and likelihood of approval scores, in addition to indication benchmarks based off 18 years of historical drug development data. Attributes of the drug, company and its clinical trials play a fundamental role in drug-specific PTSR and likelihood of approval.

Anti-CEA-CAR-T Cells overview

The therapeutic candidate is under development for the treatment of advanced and metastatic solid tumors including gastric cancer, colorectal cancer, esophageal cancer, small bowel cancer, pancreas adenocarcinoma and other digestive system tumors. It is administered through intravenous route and comprises of T-cells genetically engineered to express chimeric antigen receptors (CAR) targeting carcinoembryonic antigen (CEA) expressing cells.

Shanghai First Song Therapeutics overview

Shanghai First Song Therapeutics is a innovative biotechnology company provide manufacturing, developing and commercializing cell immunotherapy products. The company is headquartered in China.

For a complete picture of Anti-CEA-CAR-T Cells’s drug-specific PTSR and LoA scores, buy the report here.

This content was updated on 10 June 2024

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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.

GlobalData’s Likelihood of Approval analytics tool dynamically assesses and predicts how likely a drug will move to the next stage in clinical development (PTSR), as well as how likely the drug will be approved (LoA). This is based on a combination of machine learning and a proprietary algorithm to process data points from various databases found on GlobalData’s Pharmaceutical Intelligence Center.