On April 22, 2026 Rakovina Therapeutics Inc. (TSX-V: RKV; FSE: 7JO0), a biopharmaceutical company advancing innovative cancer therapies through artificial intelligence (AI)-powered drug discovery, reported the presentation of new preclinical data from two of its lead programs at the 2026 American Association for Cancer Research (AACR) (Free AACR Whitepaper) Annual Meeting, held April 17–22 in San Diego, California.
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The data, presented across two poster sessions at the world’s premier cancer research forum, advance Rakovina’s AI-driven pipeline targeting DNA damage response (DDR) vulnerabilities in hard-to-treat solid tumors. Both programs leverage generative AI platforms to address longstanding limitations of existing cancer therapies, including poor central nervous system (CNS) penetrance and the toxicity burden of drug combination regimens.
Novel Brain-Penetrant Dual ATR-mTOR Inhibitor Demonstrates In Vivo Efficacy in PTEN-Deficient Cancers
The first poster, titled A Novel Brain-Penetrant Dual ATR-mTOR Inhibitor for PTEN-Deficient Cancers (Presentation #1743, DNA Damage and Repair 2 session, April 20), presented preclinical data from Rakovina’s program to develop first-in-class CNS-penetrating molecules that simultaneously inhibit ATR and mTOR, two key drivers of survival in PTEN-deficient cancer cells. The program was developed in collaboration with Variational AI (Vancouver, BC) using the Enki generative AI platform.
PTEN deficiency is found in up to 40% of gliomas and 63% of breast cancers, which frequently metastasize to the brain. Simultaneous inhibition of ATR and mTOR is a rational therapeutic strategy in PTEN-deficient tumors, as PTEN loss activates both ATR-dependent DNA damage signaling and mTOR-driven cell survival pathways. However, no approved therapy directly addresses this dual vulnerability with effective CNS penetrance.
Using the Enki latent diffusion model to simultaneously optimize potency, selectivity, CNS penetrance, and ADMET properties, Rakovina generated and synthesized a curated set of novel small-molecule dual ATR-mTOR inhibitor candidates. Key findings presented at AACR (Free AACR Whitepaper) 2026 include:
Enzymatic potency: Candidate compounds demonstrated equal or greater inhibition of recombinant ATR and mTOR enzymes compared to reference compounds ceralasertib and tuvusertib.
Selectivity: Candidates are equally or more selective against PIKK family enzymes than the reference compounds ceralasertib and tuvusertib.
Cell viability inhibition: Candidates inhibit cell viability of D283 medulloblastoma cells equally or more than reference compounds. A prototype lead candidate inhibited cell viability of both PTEN wild-type and PTEN-deficient cancer cell lines.
Metabolic stability: After 45 minutes of incubation with human liver microsomes, candidate compounds demonstrated strong metabolic stability.
CNS penetrance: Pharmacokinetic profiling following intraperitoneal administration in mice confirmed varying but measurable levels of CNS penetrance across candidates, with brain-to-plasma ratios broadly consistent with Enki AI predictions.
In vivo efficacy: In a subcutaneous LNCaP prostate tumor model, a prototype lead candidate significantly prolonged tumor doubling time compared to vehicle control, with equal potency to reference compound ceralasertib. Critically, the Rakovina candidate was better tolerated than ceralasertib, demonstrating less weight loss with daily dosing and no signs of hematological toxicity at terminal complete blood count analysis.
Optimization of candidate inhibitors is ongoing.
Novel AI-Designed Lipid Nanoparticle Formulation of kt-3283 Successfully Characterized
The second poster, titled Development of a Lipid Nanoparticle Formulation of the Bifunctional PARP and HDAC Inhibitor Kt-3283 (Presentation #6373, Drug Delivery session, April 21), presented preclinical formulation data on pLNP/kt-3283, developed in collaboration with NanoPalm (Riyadh, Saudi Arabia) using the EnsaliX AI platform.
kt-3283 integrates PARP inhibition and HDAC-mediated chromatin remodeling into a single compound, thereby improving the PARP efficacy, and eliminating the need for combination drug regimens and their associated toxicity risks. While kt-3283 has demonstrated potent anti-tumor activity across multiple tumor types in prior in vitro studies, its clinical viability has been limited by bioavailability and metabolic stability challenges. The pLNP formulation has been specifically designed to address these limitations.
Data presented confirm the successful assembly of the EnsaliX-designed patterned lipid nanoparticles. Physicochemical characterization confirmed uniform particle size, stable colloidal behavior, and a structured surface texture predicted to enhance cellular uptake. The pLNP/kt-3283 formulation demonstrated structure and particle size consistency supporting further biological evaluation.
Next steps include in vitro and in vivo characterization to confirm activity against PARP and HDAC enzymes, determine ADME properties, and evaluate efficacy in tumor models.
"Presenting at AACR (Free AACR Whitepaper) is a meaningful milestone for our team, and these results represent a genuine step forward for both programs," said Kim Oishi, Chief Executive Officer of Rakovina Therapeutics. "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. That is exactly the differentiation we are building toward. Combined with the initial characterization of our LNP formulation for kt-3283, we believe these results reinforce the potential of our AI-driven pipeline and support a path toward IND-enabling studies."
Rakovina’s AI-powered discovery approach leverages generative AI platforms to evaluate billions of potential drug candidates at a pace not achievable through traditional methods. These capabilities are supported by the company’s access to the University of British Columbia’s lab infrastructure, enabling rapid in-house testing of lead compounds.
"These results demonstrate that our strategy of integrating AI-guided design with biological validation, is working as intended," said Dr. Mads Daugaard, President and Chief Scientific Officer of Rakovina Therapeutics. "For the ATR-mTOR program, our candidate inhibitors are tracking closely with the AI predictions for potency, selectivity, and CNS penetrance and our in vivo results give confidence in the direction of this program. For kt-3283, we have demonstrated that the EnsaliX-designed LNP formulation produces a well-characterized nanoparticle. The structured surface and organized phospholipid assembly we observed are precisely the properties expected to enhance nanoparticle stability and cellular uptake of kt-3283. Both programs have clear next steps, and we are moving forward with purpose."
The data presented at AACR (Free AACR Whitepaper) 2026 reinforce the progress of Rakovina’s AI-enabled DDR inhibitor pipeline and inform the next phase of preclinical development for both programs. For the ATR-mTOR program, further optimization of candidate inhibitors is ongoing. For the kt-3283 LNP program, the company will advance in vitro and in vivo studies to further characterize biological activity prior to evaluating efficacy in tumor models.
Rakovina intends to use these findings to advance best-in-class lead candidates toward IND-enabling studies in collaboration with pharmaceutical partners.
(Press release, Rakovina Therapeutics, APR 22, 2026, View Source [SID1234664716])