10 Best Practices to Streamline SEND Submissions and Reduce Regulatory Risk
4 minute read
Introduction
The Standard for Exchange of Nonclinical Data (SEND), developed by the Clinical Data Interchange Standards Consortium (CDISC), is a globally recognized standard for the submission of nonclinical (animal) study data to regulatory authorities. Regulators increasingly expect sponsors to submit nonclinical study data in the SEND format to support review efficiency and data traceability. Organizations must implement consistent and well-governed processes to ensure accuracy, compliance, and timeliness, given the expansion of SEND adoption globally.
Submitting nonclinical study data without SEND compliance can lead to regulatory delays, reduced assessment efficiency, data integrity issues, and increased costs.
Efficient creation and submission of SEND datasets require structured planning, strict adherence to standards, and disciplined data management practices. This white paper outlines standards-aligned best practices to support organizations in the development of high-quality SEND datasets that are compliant, traceable, and submission-ready.
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About SEND
SEND is a standard format designed by CDISC based on the CDISC Study Data Tabulation Model (SDTM), which was designed for clinical studies in humans, but SEND includes only the variables relevant to nonclinical or preclinical studies.
The Food and Drug Administration requires the SEND format for most nonclinical studies included in regulatory submissions. The European Medicines Agency also encourages applicants to submit SEND data with their submissions and assessors to gain more experience using the data during their evaluations.
SEND captures structured, animal-level data endpoints across dedicated datasets, including dosing, clinical observations, body weights, clinical pathology, organ weights, pathology findings, and necropsy details. The primary objective of SEND is nonclinical safety assessment, enabling regulators to evaluate toxicities, dose–response relationships, and target organ effects prior to first-in-human exposure.
SEND, while based on SDTM principles, was adapted to reflect nonclinical study workflows such as dose groups, study days, necropsy timing, and recovery phases. These differences matter in practice because Food and Drug Administration reviewers rely on well-defined SEND datasets and endpoints for analysis, cross-study comparisons, and efficient identification of safety signals. Inconsistencies or gaps in these datasets can lead to validation issues and delays in regulatory review.
Practical Considerations
The process of creating SEND datasets helps to determine which domains are in scope and which are not, and frequently reveals discrepancies between the study protocol, raw/source data, and the final study report. Common issues include examinations or assessments that were planned but not performed; inconsistencies in animal numbers, study days, and incident counts across reports; missing or unexpected findings; and deviations from the planned visit or assessment schedule. Such discrepancies sometimes remain unnoticed until the data standardization process begins.
Identifying and addressing these issues early in the SEND process enables timely clarification with study teams and toxicologists, thereby reducing rework during validation, minimizing query cycles, and avoiding last-minute surprises during regulatory submission and review.
Best Practices
Understanding SEND standards
A thorough understanding of CDISC SEND standards is foundational to successful dataset creation and submission. Organizations should familiarize themselves with applicable SEND Implementation Guides (SENDIGs), required domains, variables, controlled terminology, and dataset structures relevant to each study type.
Early alignment with SEND standards reduces rework and supports consistent interpretation across studies.
Study assessment
Effective SEND development begins with a detailed study assessment. Organizations should:
- Understand study design, objectives, and endpoints.
- Identify required SEND domains and variables.
- Assess data sources and study-specific complexities.
Also, identify data inconsistencies like report errors and deviations and assess data requirements for SEND conversion.
Data collection and integration
Nonclinical data are often sourced from multiple systems and formats. A robust data collection and integration strategy should:
- Consolidate study reports, raw data files, and electronic data sources.
- Standardize data structures to align with SEND requirements.
A well-defined integration approach improves efficiency and reduces the risk of data inconsistencies.
Data quality management
Data quality is central to SEND compliance and regulatory confidence. Establish systematic domain-level quality checks to:
- Identify missing, inconsistent, or invalid data.
- Verify compliance with SEND standards and controlled terminology.
- Ensure completeness and traceability of all datasets.
Early identification and resolution of data issues reduce validation findings and support reliable regulatory submissions.
Use of automation and supporting tools
Automation tools can significantly enhance SEND efficiency and consistency when used appropriately. Such tools may support:
- Data extraction.
- Data mapping.
- Application of validation rules.
- Standardized dataset generation.
Automation can complement expert reviews and strengthen overall data quality.
Dataset validation
Comprehensive validation is required before submission. Organizations should:
- Run SEND validation checks using established validation tools.
- Review findings against SENDIG requirements.
- Document and resolve all identified issues.
Treat validation as an integral, ongoing activity and part of the dataset lifecycle rather than a final stage activity.
Documentation and archiving
Comprehensive documentation supports transparency and regulatory readiness. Maintain records of:
- Dataset creation processes, including any modifications or updates made along the way.
- Data transformations and assumptions.
- Validation results and resolutions.
Archive final validated SEND datasets, metadata, and supporting documentation for future reference or regulatory submissions.
Change management and standard updates
SEND standards evolve over time through global updates to Study Data Technical Conformance Guide, SENDIG versions, and controlled terminology. To remain compliant, organizations should:
- Monitor updates to CDISC SEND standards.
- Assess the impact of changes on existing processes.
- Incorporate updates into standard operating procedures.
Proactive change management supports long-term compliance and operational efficiency.
Expertise and cross-functional collaboration
Successful SEND delivery requires collaboration across functions, including toxicology, pathology, biostatistics, and regulatory teams. Engage SEND subject matter experts to ensure:
- Accurate interpretation of standards.
- Efficient issue resolution.
- Consistent application of best practices.
Cross-functional collaboration enhances both data quality and submission readiness.
Submission readiness
Prior to submission, confirm that the SEND data package is complete and submission-ready. A final completeness and consistency check supports smooth regulatory submission and efficient review.
Conclusion
Sponsors and service providers can achieve efficient SEND dataset submission through early planning, disciplined data management, rigorous quality control, and adherence to both CDISC SEND standards and the latest regulatory requirements.
Streamline SEND development, reduce rework, and deliver high-quality, submission-ready nonclinical datasets that support effective regulatory evaluation by implementing the best practices outlined in this white paper.
About Alira Health SEND Capabilities
At Alira Health, we deliver fully validated, submission-ready SEND datasets that help you meet regulatory expectations efficiently and confidently. We transform complex nonclinical study data into fully compliant, submission-ready SEND datasets, accelerating your path to approval and ensuring your data speaks the regulator’s language.
Alira Health offers:
- Preparation of standardized datasets compliant with SENDIG v3.1, 3.1.1, SENDIG-DART v1.1, and SENDIG-Genetox v1.0.
- Controlled Terminology mapping to CDISC SEND Terminology.
- Creation of Define.xml and Nonclinical Study Data Reviewer’s Guide (nSDRG).
- End-to-end validation using Pinnacle 21 and internal quality control workflows.
- Standardization of historical nonclinical study data into SEND format to support data analysis.
- Expert guidance and ongoing regulatory support.
Author:
Rupesh Katta, SEND Data Analyst Team Lead
Author:
Rupesh Katta, SEND Data Analyst Team Lead
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