Cross study analyses of SEND data: toxicity profile classification.

A SEND toxicology data transformation, harmonization, and analysis platform were created to improve the identification of unique findings related to the intended target, species, and duration of dosing using data from multiple studies. The lack of a standardized digital format for data analysis had impeded large-scale analysis of in vivo toxicology studies. The CDISC SEND standard enables the analysis of data from multiple studies performed by different laboratories.

Leveraging the Value of CDISC SEND Data Sets for Cross-Study Analysis: Incidence of Microscopic Findings in Control Animals.

Implementation of the Clinical Data Interchange Standards Consortium (CDISC)'s Standard for Exchange of Nonclinical Data (SEND) by the United States Food and Drug Administration Center for Drug Evaluation and Research (US FDA CDER) has created large quantities of SEND data sets and a tremendous opportunity to apply large-scale data analytic approaches. To fully realize this opportunity, differences in SEND implementation that impair the ability to conduct cross-study analysis must be addressed. In this manuscript, a prototypical question regarding historical control data (see Table of Contents graphic) was used to identify areas for SEND harmonization and to develop algorithmic strategies for nonclinical cross-study analysis within a variety of databases.

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

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.

FDA Engages Collaborators to Address Nonclinical Data Challenges.

FDA and PhUSE cohosted a Computational Science Symposium (CSS) in 2012 that brought stakeholders from the pharmaceutical industry and government to work collaboratively to solve common needs and challenges. A nonclinical informatics workgroup was formed, dedicated to improving nonclinical assessments and regulatory science by identifying, collecting, and prioritizing key needs and challenges in the field and then establishing an innovative framework for addressing them in a collaborative manner. This paper discusses the process and outcomes of the nonclinical informatics workgroup during the CSS and describes an approach which crossed organizational barriers to optimize computational science for nonclinical assessment.