Rakovina Therapeutics to Present New Data Highlighting CNS-Penetrant ATR and PARP1 Inhibitors at the 2025 Society for Neuro-Oncology Annual Meeting

VANCOUVER, British Columbia, Nov. 10, 2025 (GLOBE NEWSWIRE) -- Rakovina Therapeutics Inc. (“Rakovina” or the “Company”) (TSX-V: RKV)(FSE: 7JO0) a biopharmaceutical company advancing cancer therapies through AI-enabled drug discovery, today announced that new data will be presented in two posters at the Society for Neuro-Oncology (SNO) Annual Meeting, taking place November 19–23, 2025, in Honolulu, Hawaii.

The presentations will highlight Rakovina’s AI-driven programs to discover and develop next-generation DNA-damage response (DDR) inhibitors engineered for central nervous system (CNS) penetration, supporting the development of potential new treatment options for primary and metastatic brain cancers.

Presentation Details:

Title: Discovery and development of a novel CNS-penetrating ATR inhibitor
Session: Poster Session – DNA Repair (DNAR-05)
Date/Time: November 21, 2025 | 11:30 a.m.–12:45 p.m. (HST)

Title: Discovery and development of novel CNS-penetrating PARP1-selective inhibitors
Session: Poster Session – DNA Repair (DNAR-06)
Date/Time: November 21, 2025 | 11:30 a.m.–12:45 p.m. (HST)

The first poster presents results from Rakovina Therapeutics’ kt-5000AI ATR inhibitor program, developed in collaboration with Variational AI. The program leverages the ENKI generative AI platform to design and optimize CNS-penetrant ATR inhibitors with favorable pharmacologic properties for the potential treatment of brain cancers. The data highlight the discovery of potent, selective ATR inhibitors that demonstrate activity against treatment-resistant tumor phenotypes.

The second poster presents progress from the Company’s kt-2000AI PARP inhibitor program, developed using the Deep Docking AI-accelerated drug discovery platform. The program applies ultra-large-scale virtual screening to evaluate billions of compounds in silico, rapidly identifying and optimizing PARP1-selective, CNS-penetrant inhibitors with desirable pharmacologic properties. The data illustrate how this approach enables deep exploration of chemical space and efficient discovery of next-generation candidates designed to address the limitations of first-generation PARP inhibitors.