Abstract LB434: Discovery and preclinical evaluation of ATM-5292: A potent and selective RNA targeting splice modulator for the treatment of non small cell lung cancer (NSCLC) and breast cancer, enabled by the PARSE™ machine learning platform
Authors: Minna Bui, Praveen Kumar, Timothy Sproul, Michael J. Luzzio, Suparna Gupta, Debarati DasGupta, Kevin Patel, Meredith Corley, Ramya Rangan, Connor Stephens, Brandon Anderson, Alexander Lin, Yuzu Ido, Matias Kaplan, Ryan Chow, Arthur Chase, Brent Townshend, Stephan Eismann, Raphael Townshend, Manjunath Ramarao
Journal: Cancer Research (2025)

Abstract PARSE™, our proprietary machine learning (ML) platform, addresses critical challenges in RNA targeted drug discovery by accurately predicting structural and functional properties of RNA. Furthermore, the expansion of the platform with data relevant to drug discovery, has enabled target identification and prioritization of small molecules. Leveraging our platform, we have rapidly discovered ATM-5292, a dual small molecule inhibitor targeting two key oncogenes—FoxM1 and Myb. ATM-5292 is a first-in-class, orally bioavailable mRNA degrader that selectively targets both FoxM1 and Myb with equipotency. ATM-5292 potently modulates RNA splicing at low nanomolar concentrations, inducing inclusion of a cryptic exon in these two oncogene transcripts. This results in a premature stop codon within the mRNA, leading to degradation via nonsense-mediated decay. Consequently, protein levels of both targets are significantly reduced, directly correlating with splicing modulation. In vitro studies demonstrate that ATM-5292 has a direct correlation between splicing modulation, reduction of protein, and cytotoxicity in cancer cell lines. ATM-5292 demonstrates impressive efficacy in multiple cancer cell lines and patient-derived cancer cells including NSCLC, breast cancer, colorectal cancer, and ovarian cancer, consistent with its proposed mechanism of action. Comprehensive ADMET profiling confirms favorable drug-like properties, including significant oral bioavailability, dose-dependent exposure in preclinical species (mouse, rat, and dog), and pharmacokinetic properties supporting once-daily dosing. Notably, ATM-5292 demonstrates clear anti-tumor activity in a leukemia CDX model in immunocompromised mice, with dose-dependent effects. Preclinical non-GLP toxicology studies to evaluate the maximum tolerated dose (MTD) and dose range finding (DRF) in rats showed that therapeutic doses are well tolerated. These studies identified both effective and non-effective dose ranges, providing valuable data for dose selection in a subsequent 28-day GLP toxicity and pharmacokinetic study. In summary, ATM-5292 is a promising, first-in-class dual mRNA degrader with potent anti-tumor activity in preclinical models. It is well-positioned for further GLP toxicology studies and potential clinical development. Citation Format: Minna Bui, Praveen Kumar, Timothy Sproul, Michael J. Luzzio, Suparna Gupta, Debarati DasGupta, Kevin Patel, Meredith Corley, Ramya Rangan, Connor Stephens, Brandon Anderson, Alexander Lin, Yuzu Ido, Matias Kaplan, Ryan Chow, Arthur Chase, Brent Townshend, Stephan Eismann, Raphael Townshend, Manjunath Ramarao. Discovery and preclinical evaluation of ATM-5292: A potent and selective RNA targeting splice modulator for the treatment of non small cell lung cancer (NSCLC) and breast cancer, enabled by the PARSE™ machine learning platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB434.