New 'Lab OS' Aims to Unify Automation

Sigmatic Sciences, which launched SigmaticOS, is a Sapio Sciences company and was formerly known as HelixAI. The platform aims to address the significant challenge of integrating the 840+ separate applications typically used in biopharma organizations, which often operate in disconnected silos. The push for such integration comes as AI spending in life science R&D is projected to accelerate from $4 billion in 2025 to $20 billion by 2030. The "lab-in-the-loop" concept central to SigmaticOS connects computational modeling with physical lab experiments in an automated feedback cycle. This approach is a practical application of digital twin technology, which creates a virtual replica of a bioprocess using real-time data to predict outcomes and optimize performance, from cell culture parameters to downstream purification steps. Digital twins have been shown to reduce process development costs and the number of runs required for process validation. In cell and gene therapy (CGT) manufacturing, this addresses critical challenges of scalability and process variability. Many CGT processes rely on complex, manual steps which are difficult to scale and introduce variability, impacting quality and cost. Platforms that integrate automated bioprocessing with data management are crucial for creating the robust, reproducible workflows needed for commercial-scale viral vector production and other CGT modalities. Unlike traditional Laboratory Information Management Systems (LIMS), which are primarily sample-centric systems for managing and tracking sample data, SigmaticOS is positioned as an orchestration engine. It uses over 100 software agents to actively manage and execute workflows across different systems, from in-silico design to robotic lab hardware and model training, rather than just passively storing data. The platform's native inclusion of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) allows it to create reference data stores from an organization's specific scientific data. In bioprocess development, such AI and Machine Learning (ML) tools are used to analyze high-dimensional data, predict the effects of process variables, and optimize for yield and quality, accelerating the timeline for developing new biologics. This drive for automation and efficiency is occurring as the cell and gene therapy CDMO market is forecast to grow significantly, from approximately $4.31 billion in 2024 to over $27 billion by 2033. However, the current risk-averse funding environment has caused many drug developers to delay programs and focus on fewer assets, increasing pressure on CDMOs to offer more efficient, cost-effective development and manufacturing services to win business.