Making sense of SEND; the Standard for Exchange of Nonclinical Data

Perspectives of data science in preclinical safety assessment

The data landscape in preclinical safety assessment is fundamentally changing because of not only emerging new data types, such as human systems biology, or real-world data (RWD) from clinical trials, but also technological advancements in data-processing software and analytical tools based on deep learning approaches. The recent developments of data science are illustrated with use cases for the three factors: predictive safety (new in silico tools), insight generation (new data for outstanding questions); and reverse translation (extrapolating from clinical experience to resolve preclinical questions). Further advances in this field can be expected if companies focus on overcoming identified challenges related to a lack of platforms and data silos and assuring appropriate training of data scientists within the preclinical safety teams.

Retrospective analysis of the potential use of virtual control groups in preclinical toxicity assessment using the eTOX database

Virtual Control Groups (VCGs) based on Historical Control Data (HCD) in preclinical toxicity testing have the potential to reduce animal usage. As a case study we retrospectively analyzed the impact of replacing Concurrent Control Groups (CCGs) with VCGs on the treatment-relatedness of 28 selected histopathological findings reported in either rat or dog in the eTOX database. We developed a novel methodology whereby statistical predictions of treatment-relatedness using either CCGs or VCGs of varying covariate similarity to CCGs were compared to designations from original toxicologist reports; and changes in agreement were used to quantify changes in study outcomes. Generally, the best agreement was achieved when CCGs were replaced with VCGs with the highest level of similarity; the same species, strain, sex, administration route, and vehicle. For example, balanced accuracies for rat findings were 0.704 (predictions based on CCGs) vs. 0.702 (predictions based on VCGs). Moreover, we identified covariates which resulted in poorer identification of treatment-relatedness. This was related to an increasing incidence rate divergence in HCD relative to CCGs. Future databases which collect data at the individual animal level including study details such as animal age and testing facility are required to build adequate VCGs to accurately identify treatment-related effects.

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. 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.

Scientific and Regulatory Policy Committee Points to Consider: Data Visualization for Clinical and Anatomic Pathologists

Assessment and communication of toxicology data are fundamental components of the work performed by veterinary anatomic and clinical pathologists involved in toxicology research. In recent years, there has been an evolution in the number and variety of software tools designed to facilitate the evaluation and presentation of toxicity study data. A working group of the Society of Toxicologic Pathology Scientific and Regulatory Policy Committee reviewed existing and emerging visualization technologies. This Points to Consider article reviews some of the currently available data visualization options, describes the utility of different types of graphical displays, and explores potential areas of controversy and ambiguity encountered with the use of these tools.