Gene Therapy to Target SLAMF7 for Multiple Myeloma is under clinical development by T-CURX and currently in Phase I for Multiple Myeloma (Kahler Disease). According to GlobalData, Phase I drugs for Multiple Myeloma (Kahler Disease) have a 75% phase transition success rate (PTSR) indication benchmark for progressing into Phase II. GlobalData’s report assesses how Gene Therapy to Target SLAMF7 for Multiple Myeloma’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.

Gene Therapy to Target SLAMF7 for Multiple Myeloma overview

Gene therapy is under investigation for the treatment of multiple myeloma. The drug candidate comprises of autologous T cells encoding an inducible caspase 9 (IC9) and genetically engineered with chimeric antigen receptor specifically target signaling lymphocyte activation molecule F7 (SLAMF7). The therapeutic candidate acts by targeting cells expressing signaling SLAMF7. The therapeutic candidate is administered as infusion.

T-CURX overview

T-CURX is a healthcare provider that offers develops T-cells for personalized cancer immunotherapy. T-CURX is headquartered in Wuerzburg, Bayern, Germany.

For a complete picture of Gene Therapy to Target SLAMF7 for Multiple Myeloma’s drug-specific PTSR and LoA scores, buy the report here.

This content was updated on 4 December 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.