TGI-2 is under clinical development by Hefei TG ImmunoPharma and currently in Phase I for Metastatic Melanoma. According to GlobalData, Phase I drugs for Metastatic Melanoma have an 81% phase transition success rate (PTSR) indication benchmark for progressing into Phase II. GlobalData’s report assesses how TGI-2’s drug-specific PTSR and Likelihood of Approval (LoA) scores compare to the indication benchmarks. Buy the report here.

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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.

TGI-2 overview

TGI-2 (NM1F) is under development for the treatment of advanced, or metastatic solid tumors, including metastatic ovarian cancer, metastatic melanoma and metastatic colorectal cancer.  The drug candidate is administered through parenteral route. It acts by targeting poliovirus receptor-related immunoglobulin domain-containing (PVRIG) and is being developed based on ABC-NKer platform.

It was also under development for the treatment of triple negative breast cancer 

Hefei TG ImmunoPharma overview

Hefei TG ImmunoPharma formerly (Hefei Tiangang Immune Drug Co Ltd) is engaged in the field of tumor immunotherapy that intends to use the proceeds to advance the development and registration of multiple product pipelines for tumor immunotherapy. The company is headquartered in Hefei, China.

For a complete picture of TGI-2’s drug-specific PTSR and LoA scores, buy the report here.

This content was updated on 16 July 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.