Repares’ RP-6306 transition score to next stage in solid tumours
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LoA Update: Repare Therapeutics’ Phase I RP-6306 transition score to next development stage in solid tumours moves ahead by seven points

By Manasi Vaidya 09 Jun 2021 (Last Updated June 9th, 2021 14:27)

Repare Therapeutics’ RP-6306 Phase Transition Success Rate (PTSR) score rose by seven points, as of 1 June, after the company enrolled the first patient in a Phase I study focusing on certain genetic alterations in solid tumours.

LoA Update: Repare Therapeutics’ Phase I RP-6306 transition score to next development stage in solid tumours moves ahead by seven points
The Phase I trial is recruiting 60 patients with genomic alterations that are sensitive to RP-6306. Credits: shad_foxy/Shutterstock.com.

Repare Therapeutics’ RP-6306 Phase Transition Success Rate (PTSR) score rose by seven points, as of 1 June, after the company enrolled the first patient in a Phase I study focusing on certain genetic alterations in solid tumours.

The Phase I trial is recruiting 60 patients with genomic alterations that are sensitive to RP-6306. More specifically, the program is focused on STEP2 genomic alterations which include CCNE1 amplification and FBXW7 loss, as per the company’s 14 May corporate presentation. Preliminary data from this study is expected in 2022 and Repare has planned further monotherapy and combination proof-of-concept (POC) studies in the same year. Once RP-6306 POC data is available, the company plans to consult with the regulatory authorities on further development. RP-6306 targets PKMYT1 which, as per the company, has a synthetic lethality for CCNE1 amplification, FBXW7 loss, and other alterations.

Repare Therapeutics went public in June 2020 and currently has a market cap of $1.2bn. While RP-6306’s PTSR score increased to 39%, the Phase I patient enrolment and biomarker focus did not impact the drug’s Likelihood of Approval (LoA) score. The PTSR score represents the probability of a drug progressing successfully from one development stage to the next. Both scores are calculated by GlobalData’s analysis which uses a combination of machine learning and a proprietary algorithm.

Manasi Vaidya is a Associate Editor for Pharmaceutical Technology parent company GlobalData’s investigative journalism team. A version of this article originally appeared on the Insights module of GlobalData’s Pharmaceutical Intelligence Center. To access more articles like this, visit GlobalData.