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

Area of Science:

• Pharmacovigilance and Regulatory Science
• Data Standards in Drug Development
• Nonclinical Data Management

Background:

• The Standard for Exchange of Nonclinical Data (SEND) is crucial for US FDA submissions in the pharmaceutical industry.
• The SEND Implementation Guide (SENDIG) offers flexibility, leading to significant variability in dataset creation.
• This variability complicates data analysis and hinders cross-study comparisons.

Purpose of the Study:

• To identify common areas of variability within SEND datasets.
• To propose strategies for managing this variability.
• To enable valuable cross-study analysis use cases.

Main Methods:

• Analysis of common variability in specific SEND domains.
• Illustrative example using animal age data extraction to demonstrate complexity.
• Development of a conceptual solution framework.

Main Results:

• Significant variability exists in SEND dataset creation due to flexible guidelines.
• Extracting even common data points like animal age presents challenges.
• A framework involving all stakeholders is proposed to address variability.

Conclusions:

• Addressing SEND data variability is essential for effective data utilization.
• Harmonizing SEND implementation will benefit stakeholders and enhance nonclinical research productivity.
• Standardized data management facilitates routine analysis of archived SEND data.