Veronique Francois, Project Manager, Pistoia Alliance
Toxicology has had a standardized submission format for years. It turns out to be the perfect springboard for efficacy data, and that’s what we’re building.
Who should read this
If you run in vivo efficacy studies, or exchange that data with CROs, partners, or your own teams across sites and divisions, this one’s for you.
Toxicology data has been standardized for years. We’re using it to jumpstart efficacy standards
Since 2016, the FDA has required nonclinical toxicology data in one structured format: SEND (SDTM applied to nonclinical toxicology). Submitted toxicology data is structured, machine-readable, and cross-comparable.
In vivo efficacy studies still arrive in bespoke, one-off formats. No shared standard means that data is hard to compare, reuse, or deploy for AI/ML applications.
So we start from what already works
Rather than inventing a standard from scratch, the Pistoia Alliance In Vivo Efficacy Data Standards project uses SEND as the initial foundation to map efficacy data onto the same CDISC backbone (SEND v4.0 and the Animal Rules Implementation Guide). We began with oncology as the representative first use case and will extend to immunology and neuroscience/CNS. The project will concretely deliver a suite of SDTM-based efficacy data models with validated templates, based on data from at least three Pistoia Alliance partners for each of those areas.
Building on a proven standard has two big advantages: the new standards come together far more rapidly, and interoperability is built in from the start because we’re extending a proven data structure rather than bolting on compatibility afterwards.
And it maps extensively
In one mapping exercise, a Pistoia Alliance member and project partner took in vivo oncology efficacy data and mapped it onto existing SEND domains. They found significant overlap among core parameters, which is strong evidence that the “springboard” from toxicology standards to efficacy standards holds. The next step is to expand that baseline with domains and variables that are unique to in vivo efficacy data.
Honest scope: this is data exchange, not regulatory submission
At this point, our work is deliberately focused on data exchange. We maintain, however, alignment with regulatory requirements with a view toward streamlining submission workflows. And while this is not an AI model, it does make data AI-ready.
So, in the near term, teams can compare results collected at different sites, exchange in vivo efficacy data with partners without reformatting, and learn from larger analyses of merged datasets.
This is all part of a bigger picture
This is one of three preclinical data standards projects the Pistoia Alliance runs currently in parallel, with the goal of ultimately moving each toward a standards architecture that makes preclinical data interoperable. In vivo efficacy is one stream. A related effort is examining an SDTM-like data structure for studies using in vitro New Approach Methodologies (NAMs). The focus now is data exchange, but the vision is a more integrated, interoperable data ecosystem for preclinical data.
If you work on in vivo efficacy research and want to share data across sites and with CROs or partners, come help shape these data models and templates.
Get involved
The data models and templates are being shaped now, which is exactly when new partners have the most influence over how they turn out. If your organization runs in vivo efficacy studies, this is the moment to help define the standards with others.
Join or learn more: In Vivo Efficacy Data Standards
About the Pistoia Alliance preclinical data-standards projects
The Pistoia Alliance is a global, not-for-profit alliance that lowers barriers to innovation in life science R&D through pre-competitive collaboration. In Vivo Efficacy Data Standards is one of three preclinical projects, alongside In Vitro Pharmacology and In Vitro NAM Data Standards, building the standards that let preclinical data be exchanged, compared, and interoperable.
Consistent, well-structured data builds on a shared infrastructure the whole industry benefits from building once, together. That’s why this work happens in a pre-competitive setting: no single company has to solve it alone.
Your turn
Where does exchanging efficacy data cost your team the most time today, reformatting between sites, reconciling terminology, or something else?
Share your answers with us!