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International Clinical Trials

Preparing for SEND

The advent of electronic submissions to the FDA has facilitated the streamlining of submissions to various agencies around the world, as well as harmonising data formats across the multiplicity of documents received by regulators from different companies. Since 2008, the FDA has been actively encouraging companies to submit new drug applications, biologic licence applications, investigational new drug applications and so on electronically, using the ICH’s electronic common technical document. Most submissions are now sent to the FDA in this way.

Formatting the Variables

How data are formatted is important. Back in 2004, the FDA implemented the study data tabulation method (SDTM) as the preferred standard for clinical submissions. Its binding guidance document splits relevant observations and information made in a clinical trial into four types of variable:
  • Identifier variables pinpoint data like the name of the study, the subject observed, the domain and the sequence number of each record
  • Topic variables include descriptions of the observation, such as the identity of a specific lab test
  • Timing variables cover anything involved in the timing of the observation
  • Qualifier variables can consist of any further important information, whether descriptive text, results or units used
Qualifier variables are broken down into a further five categories:
  • Grouping qualifiers aggregate observations within the same domain
  • Result qualifiers are used to describe specific results associated with the topic variable in question
  • A synonym qualifier can be employed to note an alternative name for any variable in an observation
  • Record qualifiers define additional attributes of the overall observation record
  • Variable qualifiers add further description to individual variables within an observation
While, in theory, the SDTM implementation guide (SDTMIG) can be used to submit non-clinical toxicology data, the format can be less than ideal for handling the fields required for data generated in animal tests. Its format focuses on safety domains, making the recording of information such as death diagnosis, organ measurements, palpable masses and macroscopic findings difficult.

New Approach


As a result, a modified standard – standard for exchange of non-clinical data (SEND) – has been developed, which includes specific domains, and others, for the information above. The FDA has now issued binding guidance, which means this standardised electronic format will have to be used for non-clinical data included in submissions from December 2016 onwards.

The latest version of the SEND implementation guide (SENDIG) can be found on the Clinical Data Interchange Standards Consortium website. The aim of the document is to lead users through the organisation, structure and formatting of non-clinical tabulation datasets that are consistent and suitable for sharing between all partners, including the trial sponsor, a CRO or the regulators.

SEND is, essentially, a more specific implementation of the same SDTM model. Data from animal trials and in vitro testing are captured and transformed into the tabulated form as prescribed in the guidance. Some domains are clearly going to be exactly the same as those in SDTMIG, such as vital signs findings, or laboratory or electrocardiographic test results. However, animal tests also include other factors that generate additional data fields like species, strain and sub-strain information in the demographics domain, as opposed to SDTMIG-specific fields such as race and ethnicity information. The trial sets (TX) domain allows the sponsor to define the planned sets of subjects, which result in a combination of the experimental factors of interest on a study.

Experience gained from presenting data in SDTM format will be extremely helpful in implementing SEND. Furthermore, some of the software tools developed and built for SDTMIG will be transferrable, either directly or with modifications. SEND implementation, therefore, is unlikely to involve having to start from scratch. Creating a new metadata library for SENDIG is entirely analogous to the creation process for a new library for either new or upgraded versions of SDTMIG, or a sponsor-specific implementation of SDTM.

Process Stages

There are many process considerations that must be taken into account when implementing SEND. The conversion process is important, and will include mapping, merging and converting source data, such as that garnered from electronic data capture, third-party data and raw data files. The complete conversion process must be scheduled and followed up.

Next, the datasets must be transferred – whether these are draft, interim or final datasets – and then formatted in the appropriate way, for example with XPT, SAS or Dataset-XML. The definition metadata must be created, validation checks will have to be made, and trial design tables generated.

As SDTMIG and SENDIG are based on the same model, the implementation of the two systems ought to be comparable. Domains will have to be created for SEND that will be populated with the appropriate data – and these will, in terms of organisation and structure, for the most part closely resemble those that exist for SDTMIG. Many of the automated processes that are already employed for SDTMIG should be translatable to SENDIG.

Key Differences


Although the two standards have their roots in the same place, there are many high-level differences between the implementation guides for SEND and SDTM. For example, when looking at trial design, there are no trial visits, trial inclusion/exclusion criteria or trial disease assessment domains – but there is a TX domain for trial sets, in which there is the ability to subdivide arms, or group multiple arms. Figure 1 (see full PDF) shows the intersection between the domains included in the SDTMIG and SENDIG standards.

Subject pooling is another key difference, which does not appear in SDTMIG. In non-clinical studies, it is common that a single finding may be captured for multiple subjects – not just to a single unique subject, but a pool of them. In SENDIG, pools of subjects are defined in the POOLDEF domain. POOLID values are unique for a given set of subjects. Clinical signs for group-housed subjects may contain cage level findings, for which a particular individual cannot, or has not, been identified.

For example, the technician may notice liquid stool in the cage, but did not see which animal produced the stool. In small animal studies, there may be clinical chemistry tests scheduled where a single subject may not be able to provide the volume of blood needed for testing. Therefore, blood from multiple subjects may be drawn to get the appropriate volume. In some SENDIG domains (LB, FW, PC and CL), the POOLID may be used instead of the USUBJID. Those two fields are mutually exclusive, so either the POOLID or USUBJID is populated in those domains.

A number of domains are very specific to the nature of toxicology studies. The tumor.xpt file must be created according to regulatory guidelines for the submission of carcinogenicity data. Numerous domains are required to produce this tumor.xpt file, including demographics, disposition, exposure, microscopic findings, tumour findings and TX.

Other domains, where there are obvious differences between the two, include the demographics domain; if the exact age of an animal is unknown, it is possible to give an age range, creating variability in a domain that is not available in SDTMIG’s demographics domain.

Data Capture

The other main difference between clinical and non-clinical studies is the way in which data are captured. In a clinical trial, information is typically collected using some form of electronic data capture (EDC) system. All patient data are entered into this system – increasingly via automated processes – which makes generating and activating SDTM datasets relatively straightforward. This is not the case in the non-clinical world. There is rarely a single EDC system, plus data will be arriving from different laboratories across multiple sites rather than being collected at a single, central point. This already adds to the difficulty of data being presented in multiple different formats, posing the challenge of getting it all into a single standard form.

The tools used for formatting transferred datasets in SDTM should also be applicable to SEND. This will allow datasets suitable for internal exchange to be created, as well as those in XPT or Dataset-XML for submission to the regulators. Another compatible tool will allow define metadata to be generated, and it should be possible to extend validation checks from SDTM to SEND.

As an example, the process for mapping, merging and converting source data should be interchangeable between SDTMIG and SENDIG. These data could include EDC outputs, data from third parties and raw data files, and it also permits the conversion process to be scheduled in advance or followed up. Similarly, if the sponsor requires interim datasets, the transfer process facilitates their creation, whether from the whole dataset or a subset.

For the Benefit of All


Although the FDA is already accepting data in SEND format – and has been since 2011 – there is currently no enforced standardised format for non-clinical data, and companies are free to submit electronic documents in a number of specified formats. However, from December 2016, SEND will become mandatory for FDA submissions – making the approval process more streamlined, with less time being wasted on the process of studying the documents. Other regulatory agencies such as the EMA and the Pharmaceuticals and Medical Devices Agency in Japan have expressed interest in recommending the use of SEND.

Although it is creating more effort and work for companies submitting the data now, in the long run, it will save time and money for everyone by streamlining the data flow between sponsors, CROs and regulatory authorities, while standardising terminology across the industry and creating opportunities for harmonised and standardised software tools.

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Roman Radelicki is Clinical Data Programmer Coordinator in Clinical Research at SGS. He joined the company in 2009 and was promoted to his current role in January 2014. Roman has a BSc in Applied Computer Science and also in Multimedia Design.
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Roman Radelicki
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