VivoSim’s NAMKind Intestine Wins Blue Ribbon Award at Digestive Disease Week Conference

SAN DIEGO, May 13, 2025 (GLOBE NEWSWIRE) — VivoSim Labs, Inc. (Nasdaq: VIVS) (the “Company” or “VivoSim”) announced today that its NAMkind platform for intestinal efficacy prediction was recognized with a blue ribbon “Poster of Distinction” award at the Digestive Disease Week Conference (San Diego, CA, May 2-6, 2025). The work describes developing a robust, multicellular in vitro model that closely mirrors human intestinal biology, paving the way for safer and more effective therapies for inflammatory bowel disease (IBD).

The Company’s award-winning research showcased a physiologically relevant system built from primary human cells derived from both healthy donors and patients with Ulcerative Colitis or Crohn’s Disease. These models exhibit gene expression and phenotypic characteristics that strongly align with native human intestinal tissue, including hallmark features of IBD such as epithelial barrier dysfunction and fibrosis. Importantly, the Company’s platform demonstrated responsiveness to clinically used therapeutics, confirming its potential for identifying compounds that promote mucosal healing and prevent stricture formation – two critical goals in IBD treatment. Moreover, the system is showing early promise in distinguishing between older treatments and emerging therapies that may offer improved safety and efficacy,

VivoSim offers liver and intestinal toxicology insights using its premier new approach methodologies (NAM) models, following the announcement of FDA to phase out animal testing requirements in favor of these non-animal NAM methods. The FDA’s push to phase out animal models, announced on April 10, is expected to provide a powerful accelerant to VivoSim’s market adoption, disrupting a >$10B animal testing market with models that are more predictive and ethically sound.

VivoSim’s NAMkind intestine model is a physical wet lab model of intestine made using cells from human donors. VivoSim is also developing what it believes will be industry-best in silico predictions of intestinal tox. Artificial intelligence (AI) models in VivoSim’s NAMkind services suite will be trained on extensive set of proprietary, real-world data from NAM models made from human donor cells, giving much richer and more extensive information than is possible with data from human clinical trials.

VivoSim’s NAMkind intestine models can also deliver readouts on endpoints that are today not available to industry scientists selecting from many candidate drug molecules. As a result of the lack of such tools, a cancer patient might suffer nausea, vomiting, or intestinal problems, or a patient’s needed chemotherapy dose might be lower than it could be due to such conditions. VivoSim aims to provide transformative solutions for these challenges.

The FDA has laid out ambitious goals to phase out animal testing requirements as much as possible. VivoSim is launching to fill in the much-needed gap in commercially available solutions. The technology exists to achieve FDA Commissioner Marty A. Makary’s vision, which he expressed in March 2025 with the rollout of FDA’s push to move away from animal testing and towards NAM models. “By leveraging AI-based computational modeling, human organ model-based lab testing, and real-world human data, we can get safer treatments to patients faster and more reliably, while also reducing R&D costs and drug prices,” Makary noted. By leveraging AI-based computational modeling, human organ model-based lab testing, VivoSim is delivering on the promise of this technology, and will use its proprietary methods and cutting-edge capabilities for NAMkind models that displace use of animals and provide superior outcomes for its pharmaceutical customers, ultimately delivering better solutions for patients.

Forward Looking Statements
Any statements contained in this press release that do not describe historical facts constitute forward-looking statements as that term is defined in the Private Securities Litigation Reform Act of 1995. Any forward-looking statements contained herein are based on current expectations but are subject to a number of risks and uncertainties. Forward-looking statements include statements regarding the Company’s expectations that its model paves the way for safer and more effective therapies for IBD and that the models are more predictive and ethically sound, the Company’s expectations regarding its market adoption, the Company’s belief that it is developing an industry-best in silico predictions of liver tox, the Company’s expectations about its AI models in the Company’s NAMkind services suite, the Company’s plan to provide transformative solutions for certain challenges, including that a cancer patient might suffer nausea, vomiting, or intestinal problems, or a patient’s needed chemotherapy dose might be lower than it could be and that the Company will deliver on its end goal of providing better solutions for patients. Such forward-looking statements are not guarantees of performance and actual actions or events could differ materially from those contained in such statements. These risks and uncertainties and other factors are identified and described in more detail in the Company’s filings with the SEC, including its Annual Report on Form 10-K filed with the SEC on May 31, 2024, as such risk factors are updated in its most recently filed Quarterly Report on Form 10-Q filed with the SEC on February 19, 2025. You should not place undue reliance on these forward-looking statements, which speak only as of the date that they were made. These cautionary statements should be considered with any written or oral forward-looking statements that the Company may issue in the future. Except as required by applicable law, including the securities laws of the United States, the Company does not intend to update any of the forward-looking statements to conform these statements to reflect actual results, later events, or circumstances or to reflect the occurrence of unanticipated events. 

Contact
Investor Relations
info@vivosim.ai

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