VivoSim Labs, Inc.

https://www.vivosim.ai

VivoSim Labs, Inc. is a biotechnology company specializing in pharmaceutical and biotechnology services. The company's core business involves testing drugs and drug candidates using advanced three-dimensional (3D) human tissue models of the liver and intestine. Headquartered in San Diego, California, VivoSim Labs aims to revolutionize drug discovery and development by providing human-relevant safety insights, thereby reducing the reliance on animal testing and potentially lowering drug development costs and clinical trial failures.

The company's key offerings include its proprietary "New Approach Methodologies" (NAM) models, specifically the NAMkind™ 3D human tissue models of liver and intestine. These models are developed using primary human cells to create micro-liver and micro-intestine organoids, which are designed to provide more accurate biological responses than traditional toxicology tests. VivoSim Labs provides Liver Toxicology Services, Gastrointestinal Toxicology Services, and Investigative Toxicology Services, along with bespoke services for investigational toxicology and mechanism of drug action elucidation, catering to pharmaceutical and biotechnology companies at various stages of drug development.

VivoSim Labs emerged from stealth mode in April 2025, coinciding with the U.S. Food and Drug Administration's (FDA) initiatives to phase out animal testing requirements in favor of non-animal NAM methods. The company, formerly known as Organovo Holdings, Inc., rebranded to VivoSim Labs, Inc. in April 2025 and is publicly traded on NASDAQ under the ticker VIVS. Recent leadership appointments include Dr. Amar Sethi as Chief Scientific Officer in January 2026 and Tony Lialin as Chief Commercial Officer in August 2025. VivoSim Labs leverages AI-driven analytics combined with its 3D human tissue models to deliver decision-ready toxicology insights, and has recently validated its NAMkind™ models for predicting antibody drug conjugate (ADC) toxicity, as well as introduced an AI prediction tool for gastrointestinal toxicity.

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CID: 2926