Rakovina Therapeutics Presents New Preclinical Data at AACR 2026 Annual Meeting

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• Both programs leverage generative AI platforms to address longstanding limitations of existing cancer therapies, including poor CNS penetrance and the toxicity burden of drug combination regimens

  60% confidence

• No approved therapy directly addresses the dual ATR-mTOR vulnerability with effective CNS penetrance in PTEN-deficient tumors

  60% confidence

• kt-3283 clinical viability has been limited by bioavailability and metabolic stability challenges

  60% confidence

• For the ATR-mTOR program, candidate inhibitors are tracking closely with AI predictions for potency, selectivity, and CNS penetrance

  60% confidence

• The structured surface and organized phospholipid assembly observed in kt-3283 LNP are precisely the properties expected to enhance nanoparticle stability and cellular uptake

  60% confidence

• AI-powered discovery approach can evaluate billions of potential drug candidates at a pace not achievable through traditional methods

  60% confidence

• The in vivo efficacy data for our ATR-mTOR inhibitor are particularly encouraging. The compound demonstrated potency comparable to an established reference compound while exhibiting a meaningfully improved tolerability profile.

  60% confidence

• Our strategy of integrating AI-guided design with biological validation is working as intended

  60% confidence

• These results reinforce the potential of our AI-driven pipeline and support a path toward IND-enabling studies

  60% confidence

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