Abstract

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. FDA CDER's repository of >1800 sponsor-submitted studies in SEND format was queried using the statistical programming language R to gain insight into how the CDISC SEND Implementation Guides are being applied across the industry. For each component needed to answer the question (defined as "query block"), the frequency of data population was determined and ranged from 6 to 99%. For fields populated <90% and/or that did not have Controlled Terminology, data extraction methods such as data transformation and script development were evaluated. Data extraction was successful for fields such as phase of study, negative controls, and histopathology using scripts. Calculations to assess accuracy of data extraction indicated a high confidence in most query block searches. Some fields such as vehicle name, animal supplier name, and test facility name are not amenable to accurate data extraction through script development alone and require additional harmonization to confidently extract data. Harmonization proposals are discussed in this manuscript. Implementation of these proposals will allow stakeholders to capitalize on the opportunity presented by SEND data sets to increase the efficiency and productivity of nonclinical drug development, allowing the most promising drug candidates to proceed through development.

Highlights

  • The Standard for Exchange of Nonclinical Data (SEND) developed by the Clinical Data Interchange Standards Consortium (CDISC) provides a structured electronic format for organizing and exchanging nonclinical study data between sponsor companies, contract research organizations (CROs), and health authorities

  • In 2016, the United States Food and Drug Administration Center for Drug Evaluation and Research (US FDA CDER) started requiring the submission of SEND data sets along with nonclinical study reports for certain nonclinical study types submitted in support of commercial Investigational New Drug (IND) Applications and New Drug Applications (NDAs)

  • The study types that are within the scope of the SEND requirement are described in the CDISC SEND Implementation Guides (SENDIGs) that are supported by the US FDA

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Summary

Introduction

The Standard for Exchange of Nonclinical Data (SEND) developed by the Clinical Data Interchange Standards Consortium (CDISC) provides a structured electronic format for organizing and exchanging nonclinical study data between sponsor companies, contract research organizations (CROs), and health authorities. In 2016, the United States Food and Drug Administration Center for Drug Evaluation and Research (US FDA CDER) started requiring the submission of SEND data sets along with nonclinical study reports for certain nonclinical study types submitted in support of commercial Investigational New Drug (IND) Applications and New Drug Applications (NDAs). The study types that are within the scope of the SEND requirement are described in the CDISC SEND Implementation Guides (SENDIGs) that are supported by the US FDA. US FDA CDER requirements for SEND will expand as CDISC develops additional SENDIGs. There are no known public sources for SEND data, so companies will likely need to build databases with their own data or form data sharing arrangements with other companies to realize the benefits of cross-study analysis. There is a tremendous opportunity to leverage existing and future data to perform large-scale data analytics and cross-study analyses to make more informed decisions on Special Issue: Computational Toxicology

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