Standard For Exchange Of Nonclinical Data Research Articles

SendigR: an R package to leverage the value of CDISC SEND datasets for cross-study analysis.

The CDISC Standard for Exchange of Nonclinical Data (SEND) data standard has created new opportunities for collaborative development of open-source software solutions to facilitate cross-study analyses of toxicology study data. A public-private partnership between BioCelerate and the FDA/Center for Drug Evaluation and Research (CDER) was established in part to develop and publicize novel methods to facilitate cross-study analysis of SEND datasets. As part of this work in collaboration with the Pharmaceutical Users Software Exchange (PHUSE), an R package sendigR has been developed to enable users to construct a relational database from a collection of SEND datasets and then query that database to perform cross-study analyses. The sendigR package also includes an integrated Python package, xptcleaner, which can be used to harmonize the terminology used in SEND datasets by mapping to CDISC controlled terminologies. The sendigR R package is freely available on the comprehensive R Archive Network (CRAN) and at https://github.com/phuse-org/sendigR. An R Shiny web application was included in the R package to enable toxicologists with no coding experience to perform historical control analyses. Experienced R programmers will be able to integrate the package functions into their own custom scripts/packages and potentially contribute improvements to the functionality of sendigR. sendigR reference manual: https://phuse-org.github.io/sendigR/. sendigR R Shiny demo app: https://phuse-org.shinyapps.io/sendigR/.

Guide for Combining Primary Tumors for Statistical Analysis in Rodent Carcinogenicity Studies.

The Tumor Combination Guide was created at the request of the U. S. Food and Drug Administration (FDA) by a Working Group of biopharmaceutical experts from international societies of toxicologic pathology, the Food and Drug Administration (FDA), and members of the Standard for Exchange of Nonclinical Data (SEND) initiative, to assist pharmacology/toxicology reviewers and biostatisticians in statistical analysis of nonclinical tumor data. The guide will also be useful to study and peer review pathologists in interpreting the tumor data. This guide provides a higher-level hierarchy of tumor types or categories correlating the tumor names from the International Harmonization of Nomenclature and Diagnostic Criteria (INHAND) publications with those available in the NEOPLASM controlled terminology (CT) code list in SEND. The version of CT used in a study should be referenced in the nonclinical study data reviewer's guide (SDRG) (section 3.1) of electronic submissions to the FDA. The tumor combination guide instructions and examples are in a tabular format to make informed decisions for combining tumor data for statistical analysis. The strategy for combining tumor types for statistical analysis is based on scientific criteria gleaned from the current scientific literature; as SEND and INHAND terminology and information evolve, this guide will be updated.

P24-27: Appropriate Responses to the Standard for Exchange of Nonclinical Data (SEND) and the activities of the Global SEND Alliance (G-SEND)

P24-27: Appropriate Responses to the Standard for Exchange of Nonclinical Data (SEND) and the activities of the Global SEND Alliance (G-SEND)

Harmonization of criteria and terminology in fetal rat skeletal evaluations.

A survey of laboratories in North American and Europe that routinely conduct fetal skeletal examinations was performed with the purpose of (1) understanding current terminology used for classifying skeletal findings in developmental toxicity (DT) studies and (2) understanding the criteria used to identify relatively common findings that sufficiently deviate from normal. The goal was to promote terminology harmonization and improve interlaboratory consistency in the criteria used to identify developmental anomalies. The survey, designed based on terminology for developmental anomalies recommended by an international collaboration (Makris et al., Congenital Anomalies, 2009;49(3):123-246), was conducted by a subgroup (authors of this publication) of the Royal Society of Biology's International Register of Fetal Morphologists (IRFM). Individual and summarized anonymized responses are provided here. The authors, who are expert fetal morphologists with experience performing fetal examinations, reviewed the responses and generated recommendations on preferred terminology and criteria for determining when morphological variations deviate from normal and warrant recording of the findings for skeletal observations in Sprague Dawley (SD) fetal rats. The objective of these recommendations is to complement Makris et al. (Congenital Anomalies, 2009;49(3):123-246). The broad application will improve interlaboratory harmonization of recording fetal skeleton findings in developmental toxicity studies intended for regulatory submissions, including SEND (Standard for Exchange of Nonclinical Data).

Hurdles and signposts on the road to virtual control groups-A case study illustrating the influence of anesthesia protocols on electrolyte levels in rats.

Introduction: Virtual Control Groups (VCGs) represent the concept of using historical control data from legacy animal studies to replace concurrent control group (CCG) animals. Based on the data curation and sharing activities of the Innovative Medicine Initiatives project eTRANSAFE (enhancing TRANSlational SAFEty Assessment through Integrative Knowledge Management) the ViCoG working group was established with the objectives of i) collecting suitable historical control data sets from preclinical toxicity studies, ii) evaluating statistical methodologies for building adequate and regulatory acceptable VCGs from historical control data, and iii) sharing those control-group data across multiple pharmaceutical companies. During the qualification process of VCGs a particular focus was put on the identification of hidden confounders in the data sets, which might impair the adequate matching of VCGs with the CCG. Methods: During our analyses we identified such a hidden confounder, namely, the choice of the anesthetic procedure used in animal experiments before blood withdrawal. Anesthesia using CO2 may elevate the levels of some electrolytes such as calcium in blood, while the use of isoflurane is known to lower these values. Identification of such hidden confounders is particularly important if the underlying experimental information (e.g., on the anesthetic procedure) is not routinely recorded in the standard raw data files, such as SEND (Standard for Exchange of Non-clinical Data). We therefore analyzed how the replacement of CCGs with VCGs would affect the reproducibility of treatment-related findings regarding electrolyte values (potassium, calcium, sodium, and phosphate). The analyses were performed using a legacy rat systemic toxicity study consisting of a control and three treatment groups conducted according to pertinent OECD guidelines. In the report of this study treatment-related hypercalcemia was reported. The rats in this study were anesthetized with isoflurane. Results: Replacing the CCGs with VCGs derived from studies comprising both anesthetics resulted in a shift of control electrolyte parameters. Instead of the originally reported hypercalcemia the use of VCG led to fallacious conclusions of no observed effect or hypocalcemia. Discussion: Our study highlights the importance of a rigorous statistical analysis including the detection and elimination of hidden confounders prior to the implementation of the VCG concept.

Large scale meta-analysis of preclinical toxicity data for target characterisation and hypotheses generation.

Recent technological advances in the field of big data have increased our capabilities to query large databases and combine information from different domains and disciplines. In the area of preclinical studies, initiatives like SEND (Standard for Exchange of Nonclinical Data) will also contribute to collect and present nonclinical data in a consistent manner and increase analytical possibilities. With facilitated access to preclinical data and improvements in analytical algorithms there will surely be an expectation for organisations to ensure all the historical data available to them is leveraged to build new hypotheses. These kinds of analyses may soon become as important as the animal studies themselves, in addition to being critical components to achieve objectives aligned with 3Rs. This article proposes the application of meta-analyses at large scale in corporate databases as a tool to exploit data from both preclinical studies and in vitro pharmacological activity assays to identify associations between targets and tissues that can be used as seeds for the development of causal hypotheses to characterise of targets. A total of 833 in-house preclinical toxicity studies relating to 416 compounds reported to be active (pXC50 ≥ 5.5) against a panel of 96 selected targets of interest for potential off-target non desired effects were meta-analysed, aggregating the data in tissue-target pairs. The primary outcome was the odds ratio (OR) of the number of animals with observed events (any morphology, any severity) in treated and control groups in the tissue analysed. This led to a total of 2139 meta-analyses producing a total of 364 statistically significant associations (random effects model), 121 after adjusting by multiple comparison bias. The results show the utility of the proposed approach to leverage historical corporate data and may offer a vehicle for researchers to share, aggregate and analyse their preclinical toxicological data in precompetitive environments.

Qualitative analysis of actual Standard for Exchange of Nonclinical Data (SEND) datasets for Data Domains: Proposition from Japan Pharmaceutical Manufacturers Association SEND Taskforce Team on standardization of nonclinical data

The Standard for Exchange of Nonclinical Data (SEND) has been adopted by the US FDA, which has required pharmaceutical companies who are developing new drugs for the US market to implement SEND. The Japan Pharmaceutical Manufacturers Association (JPMA) SEND Taskforce Team responded to this situation by starting a project to better understand the contents of SEND datasets. The project focused on domains generally included in the SEND domains for single- and repeat-dose general toxicology studies, and surveyed what kind of information are populated in which domains and in what way. The qualitative analysis of the results indicated that variations exist based on whether or not an individual variable was populated and on how the variable was populated. The Taskforce Team recommends reducing variations not only in the SEND datasets but also in the descriptions in the study protocol and/or final study report. Reduction of such variations should lead to higher quality datasets with powerful and increased searchability so that accumulated SEND datasets should become more valuable. These efforts would provide regulatory agencies with easier review of SEND datasets, which contributes to efficient development of new drug candidates.

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.

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. The example of extracting a commonly used data point, animal age, is used to illustrate the complexity and variability of SEND datasets. Developing a solution framework to the variability problem that includes all stakeholders involved in the creation and use of SEND datasets may enable future, routine analysis of warehoused SEND data. Harmonizing the implementation and use of SEND is expected to benefit all involved stakeholders and to ultimately contribute to the goal of increased productivity in nonclinical research.

The role of standard for exchange of nonclinical data (SEND) in safety pharmacology

The role of standard for exchange of nonclinical data (SEND) in safety pharmacology

Establishment of the Global SEND Alliance (G-SEND) in Japan and efficient creation of electronic SEND datasets between CROs.

The Standard for Exchange of Nonclinical Data (SEND), adopted by the US Food and Drug Administration (FDA), is a set of regulations for digitalization and standardization of nonclinical study data; thus, related organizations have begun implementing processes in support of SEND. The Global Editorial and Steering Committee (GESC), which provides oversight of the International Harmonization of Nomenclature and Diagnostic Criteria (INHAND), has prepared the SEND Controlled Terminology (CT) for toxicologic pathology. SEND provides electronic data standards created by the Clinical Data Interchange Standards Consortium (CDISC), and CDISC also collaborates in the implementation of SEND. Furthermore, the Pharmaceutical Users Software Exchange (PhUSE), which includes members of the US FDA, has conducted various activities to promote realistic and effective methods to implement SEND. As we reported in 2015, there is a significant variation in the efficiency and quality of SEND data implementation across pharmaceutical companies and contractors (CROs) globally. To address this problem, the Global SEND Alliance (G-SEND) was established in August 2018 to facilitate the coordination and standardization of SEND datasets across CROs in Asia. This paper reports the first method for organizationally and jointly creating consistent SEND datasets between CROs using G-SEND.

58th Annual Meeting of the Japanese Teratology Society: Congenital anomalies as variants

58th Annual Meeting of the Japanese Teratology Society: Congenital anomalies as variants

Brief Synopsis: Review of Renal Tubule and Interstitial Anatomy and Physiology and Renal INHAND, SEND, and DIKI Nomenclature.

This article provides a synopsis of the first two presentations from the second scientific session of the 37th Annual Symposium of the Society of Toxicologic Pathology in Indianapolis, Indiana, on June 18, 2018; the session focused on acute kidney injury. The first presentation, given by Dr. Kevin McDorman, focused on "Fundamentals of Renal Tubule and Interstitial Anatomy and Physiology." Several common background findings from toxicity studies were additionally discussed. Lastly, factors that impact the relevance and usefulness of historical control data, such as quality and consistency of histopathology, were discussed. The second presentation, given by Dr. Torrie Crabbs, provided a review of International Harmonization of Nomenclature and Diagnostic Criteria (INHAND), Standard for Exchange of Nonclinical Data (SEND), and drug-induced kidney injury (DIKI) nomenclature. INHAND is a global collaborative project that provides internationally accepted standardized nomenclature and diagnostic criteria for proliferative and nonproliferative changes in laboratory animals in toxicity and carcinogenicity studies. SEND is currently a required standard for data submission to the Food and Drug Administration (FDA). Since the FDA has indicated its preference for INHAND nomenclature, SEND will predominately use INHAND terminology; thus, familiarity with INHAND terminology is critical for toxicologic pathologists. The diagnostic features of three common DIKI findings, in addition to several complicated INHAND terminologies, were reviewed.

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

The Standard for Exchange of Nonclinical Data (SEND) is currently the preferred submission format for nonclinical animal data by the US FDA and became a requirement on the 18th December 2016. Application of these data standards is the first step to being able to perform cross-study querying and is expected to open up opportunities for data mining and meta-analysis by the pharmaceutical industry. This paper reports on our experiences in developing a tool to allow recent SEND formatted studies to be explored alongside historical nonclinical data already gathered as part of the eTOX project. Combining SEND data with historical data will positively impact the power of any analysis performed and increase the likelihood of being able to detect rare effects. It describes the use of KNIME in generating dose group averages and incidences from individual animal level data captured in SEND. There are a number of options for opening and reading SEND files but the benefits of using KNIME are that it is a free, open source data mining framework which allows the data to be viewed in a holistic manner rather than one domain at a time. Additionally it incorporates several nodes useful for aggregating and visualising the data to more easily identify patterns and trends.

Specific toxicologist/pathologist responses for Standard for Exchange of Nonclinical Data (SEND)

Specific toxicologist/pathologist responses for Standard for Exchange of Nonclinical Data (SEND)

Specific pathologist responses for Standard for Exchange of Nonclinical Data (SEND)

The Standard for Exchange of Nonclinical Data (SEND), introduced by the US Food and Drug Administration (FDA), is a scheme for the computerization, electronic application, and screening of preclinical data. Since its establishment, related organizations have been working together to implement SEND. However, it is difficult for individual pharmaceutical companies that often outsource to achieve complete compliance with SEND; hence, the cooperation of contract research organizations (CROs) and SEND Registered Solution Providers (RSPs) is indispensable. In SEND, most data, including those on pathology findings, are converted into controlled terminology (CT), but it is not a simple process to convert findings or levels of severity in the field of pathology, which is a descriptive science. The authors have successfully completed an FDA trial submission for a toxicology test conducted at a CRO and in doing so acquired important knowledge. This article presents a clear picture of such important knowledge from a pathologist’s viewpoint.

Graphical display of histopathology data from toxicology studies for drug discovery and development: An industry perspective

Histopathology data comprise a critical component of pharmaceutical toxicology studies and are typically presented as finding incidence counts and severity scores per organ, and tabulated on multiple pages which can be challenging for review and aggregation of results. However, the SEND (Standard for Exchange of Nonclinical Data) standard provides a means for collecting and managing histopathology data in a uniform fashion which can allow informatics systems to archive, display and analyze data in novel ways. Various software applications have become available to convert histopathology data into graphical displays for analyses. A subgroup of the FDA-PhUSE Nonclinical Working Group conducted intra-industry surveys regarding the use of graphical displays of histopathology data. Visual cues, use-cases, the value of cross-domain and cross-study visualizations, and limitations were topics for discussion in the context of the surveys. The subgroup came to the following conclusions. Graphical displays appear advantageous as a communication tool to both pathologists and non-pathologists, and provide an efficient means for communicating pathology findings to project teams. Graphics can support hypothesis-generation which could include cross-domain interactive visualizations and/-or aggregating large datasets from multiple studies to observe and/or display patterns and trends. Incorporation of the SEND standard will provide a platform by which visualization tools will be able to aggregate, select and display information from complex and disparate datasets.

日本あるいは米国外事業者におけるFDA SEND 対応 －日本のCRO としてのFDA SEND 予備申請通過成功事例と教訓－

日本あるいは米国外事業者におけるFDA SEND 対応 －日本のCRO としてのFDA SEND 予備申請通過成功事例と教訓－

FDA SEND in non-US countries responses to the standard for exchange of nonclinical data (SEND) in non-US countries

The Biomedical Research Integrated Domain Group (BRIDG) project is a collaborative initiative between the National Cancer Institute (NCI), the Clinical Data Interchange Standards Consortium (CDISC), the Regulated Clinical Research Information Management Technical Committee (RCRIM TC) of Health Level 7 (HL7), and the Food and Drug Administration (FDA) to develop a model of the shared understanding of the semantics of clinical research.The BRIDG project is based on open-source collaborative principles and an implementation-independent, use-case driven approach to model development. In the BRIDG model, declarative and procedural knowledge are represented using the Unified Modeling Language (UML) class, activity and state diagrams.The BRIDG model currently contains harmonized semantics from four project use cases: the caXchange project and the patient study calendar project from caBIG™; the standard data tabular model (SDTM) from CDISC; and the regulated products submission model (RPS) from HL7. Scalable harmonization processes have been developed to expand the model with content from additional use cases.The first official release of the BRIDG model was published in June 2007. Use of the BRIDG model by the NCI has supported the rapid development of semantic interoperability across applications within the caBIG™ program.The BRIDG project has brought together different standards communities to clarify the semantics of clinical research across pharmaceutical, regulatory, and research organizations. Currently, the NCI uses the BRIDG model to support interoperable application development in the caBIG™, and CDISC and HL7 are using the BRIDG model to support standards development.

Responses to the Standard for Exchange of Nonclinical Data (SEND) in non-US countries

The Standard for the Exchange of Nonclinical Data (SEND), adopted by the US FDA, is part of a set of regulations and guidances requiring the submission of standardized electronic study data for nonclinical and clinical data submissions. SEND is the nonclinical implementation of SDTM (Study Data Tabulation Model), the standard electronic format for clinical regulatory submissions to FDA. SEND, SDTM, and the associated Controlled Terminology have been developed by CDISC (Clinical Data Interchange Standards Consortium). In order to successfully implement SEND, interdisciplinary contributions between sponsors and CROs, need a model for task allocation. This is being undertaken by the Pharmaceutical Users Software Exchange (PhUSE). Because SEND is currently the preferred submission format of the US FDA only and will become required by it starting in December 2016, only American academic societies and companies are actively involved. An exception to this is the INHAND initiative, which leads the way in standardizing terminology for toxicological pathology. On the other hand, international globalization of other clinical and nonclinical practices is not feasible because there are substantial differences between the US and non-US countries in CRO involvement in drug development. Thus, non-US countries must consider and develop approaches to SEND that meet their needs. This paper summarizes the activities of the major organizations involved in SEND development and implementation, discusses the effective use of SEND, and details a compliance scheme (research material of the Showa University School of Medicine) illustrating how pharmaceutical companies can complete a large amount of work up to an FDA application with the effective utilization of CROs and solution providers.