SEND harmonization & cross-study analysis: A proposal to better harvest the value from SEND data

Abstract

The Standard for Exchange of Nonclinical Data (SEND) identifies an approach for representing nonclinical data in a structured format which has been widely adopted by the pharmaceutical industry as it is required for data submission to the United States Food & Drug Administration (US FDA). The SEND Implementation Guide (SENDIG) allows for considerable flexibility in how data is represented; interpretation of these guidelines has led to significant variability in the approach to SEND dataset creation. The purposes of this manuscript are to identify common variability in certain SEND domains and to describe how variability can be managed to enable valuable cross-study analysis use cases. The example of extracting a commonly used data point, animal age, is used to illustrate the complexity and variability of SEND datasets. Developing a solution framework to the variability problem that includes all stakeholders involved in the creation and use of SEND datasets may enable future, routine analysis of warehoused SEND data. Harmonizing the implementation and use of SEND is expected to benefit all involved stakeholders and to ultimately contribute to the goal of increased productivity in nonclinical research.