Make nonclinical data easier to interpret and easier to act on
Effective data visualization is essential for visualizing nonclinical study data, helping nonclinical teams interpret complex datasets, communicate findings clearly, and make timely, confident safety decisions.
Nonclinical teams often need to synthesize large volumes of diverse study data to assess compound safety and align quickly across functions. With the FDA’s requirement for nonclinical submissions in SEND (Standard for Exchange of Nonclinical Data) format, standardized individual-animal data can now be reviewed and analyzed more consistently. SEND data visualization enables scientists to spend less time on data manipulation and more time on interpretation.
This guide outlines best practices for building visualizations that surface meaningful dose-response and time-course trends, make outliers and missing data easier to spot, and connect signals across endpoints and domains (for example, exposure versus clinical pathology). It also highlights the context every figure should include, clear labeling, meaningful ordering of categorical values, consistent axes, and helpful reference lines, plus common pitfalls to avoid so visual outputs are easier to trust, share, and act on.
What you’ll learn
- Are your data hiding important dose-response signals?
- Which outliers actually matter, and which don’t?
- How to connect exposure, pathology, and safety findings
- What makes a visualization trustworthy at a glance
Ready to improve how you visualize and interpret nonclinical study data? Fill out the form to access the guide and get practical, real-world best practices.
Author
Joyce Zandee
Vice President, Product Management
