The Pistoia Alliance is launching the third phase of its Chemistry, Manufacturing and Controls (CMC) Process Ontology project.
The global, not-for-profit alliance that advocates for greater collaboration in life sciences R&D is hoping the new phase will solve one of the biggest barriers to digital transformation in life sciences: inconsistent, siloed experimental and process data that limit automation, technology transfer and AI adoption.
The Alliance is supported by leading organisations including Eli Lilly, Amgen, ZS, Crown Point Technologies, Merck & Co, AstraZeneca, GSK and Johnson & Johnson.
It recently published results from its Lab of the Future 2025 survey, showing that while 81% of labs use electronic lab notebooks (ELNs) and 77% expect to adopt AI within two years, nearly half still cite data standards and ontologies as a major gap preventing data from being fully FAIR and reusable.
Without a shared semantic framework, information across R&D and manufacturing remains fragmented, requiring scientists to continually reinterpret and reformat data rather than focus on science.
"Data is the foundation for the next era of innovation in life sciences and no single organisation can solve the interoperability challenge alone,” said Dr Becky Upton, President of the Pistoia Alliance.
“The strength of the Alliance lies in uniting our members from across the industry to create shared frameworks that accelerate digital transformation."
"AI and automation rely on structured, interoperable data and through this next phase of the CMC Process Ontology we will deliver practical standards that make life sciences data AI-ready and interoperable from the lab through to manufacturing."
The CMC Process Ontology creates a common language for describing how medicines are developed and manufactured.
By defining processes and parameters in a consistent, vendor-neutral format, it facilitates structured data exchange between ELNs, laboratory information management systems (LIMS) and Manufacturing Execution Systems.
Building upon the ISA-88/95 standards, it supports more efficient technology transfer, process analytics and AI-driven decision-making.
Phase 3 will build on its existing coverages for small-molecule, biologic and synthetic processes, testing interoperability with vendor systems and releasing open resources to drive adoption.
By standardising how process data are described, it also improves repeatability, helping scientists reproduce and compare results across sites and systems facilitating technology transfer.
Following a successful proof of concept, Phase 2 broadened the CMC Process Ontology to encompass chemical, mAb, and CAR-T manufacturing, fully defining process steps, key parameters and P-S-O-A (process, stage, operation, action) designations to deliver a comprehensive, production-ready ontology.
The team refined how process steps and parameters are defined, creating a standardised vocabulary that allows recipes to be written, understood and shared consistently across systems.
This work laid the foundation for industry-wide data standardisation and smoother integration between laboratory, manufacturing and automation platforms.
"Phase 2 gave us a strong foundation for making data interoperable across the industry," said Dr Birthe Nielsen, Project Lead, Pistoia Alliance.
"Phase 3 is about putting that work into action by expanding the ontology, testing it with real systems and building the framework to sustain it long term."
"Our goal is to make experimental and process data easier to reuse, share and trust across the industry."
"By improving data consistency, we also make experiments more repeatable and results easier to verify, as well as ensuring optimal process scale-up and optimisation.”
The Pistoia Alliance is inviting pharmaceutical companies, technology vendors and research organisations to join Phase 3 as project sponsors and contributors.
Participants will have the opportunity to shape an industry-wide standard for interoperable, machine-readable CMC process data, influence vendor roadmaps for digital lab systems and accelerate the integration of automation and AI in R&D and manufacturing.