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How PK CDISC Data Tech Solutions Can Accelerate Your Drug Development

If you work in pharma, you may have heard the following acronyms: CDISC, SDTM, ADaM. They sound much like alphabet soup. But, in reality, they relate to data standards for drug development.

So, what are data standards, and why were they created in the first place? And, how should pharmaceutical companies use them? In this blog, I’ll discuss the development of common pharmacokinetic (PK) CDISC data standards and the impact of building them for electronic regulatory submissions to the Food and Drug Administration (FDA).


Before we begin, here’s a short list of abbreviations for your reference while reading this post:

  • ADaM = Analysis Data Model
  • CDISC = Clinical Data Interchange Standards Consortium
  • SDTM = Study Data Tabulation Model
  • SEND = Standard for Exchange of Nonclinical Data

Since the late 1990s, data collection techniques have improved, and data acquisition has accelerated.  Similar advances in data analysis have also occurred. Despite all these advances, improvements in data collection and analysis were not harmonized across pharmaceutical companies, therapeutic areas, or countries. Thus, clinical trial data was not interchangeable or accessible to researchers with new hypotheses.

The rise of data standards

The need for interchangeable data resulted in the creation of the CDISC organization in 1997 to develop global standards and innovations to streamline medical research and ensure a link with healthcare. The CDISC mission is “to develop and support global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare.” The consortium includes members from pharmaceutical companies, medical device manufacturers, regulatory authorities, and service providers. This consortium publishes standards that further the goal of interoperability.

CDISC standards have now been adopted and implemented in over 90 countries and are required for drug submissions to the FDA and Pharmaceuticals and Medical Devices Agency (PMDA) in Japan. These standards apply to all new drug applications (NDAs), investigational new drug applications (INDs), abbreviated new drug applications (ANDAs), and certain biologics license applications (BLAs).

In particular, it is necessary to generate domains associated with PK analysis, which represents a unique challenge for electronic drug submissions. PK CDISC domains are required when developing the preclinical SEND and clinical SDTM datasets needed for a regulatory submission package.


SEND is an implementation of the CDISC’s SDTM for non-clinical studies. SEND applies to general toxicology (single- and repeat-dose) and carcinogenic studies with the goal to expand to other service areas, e.g., safety pharmacology and DART/EFT. SEND provides guidance for the organization, structure, and format of standard nonclinical tabulation data sets for interchange between organizations such as sponsors and CROs and for submission to the FDA.


After the data is collected into a clinical database, it must be converted into standard data tables to be used for analysis. The SDTM standard defines how individual observations from a clinical study are compiled. The basic concept is that each piece of data can be uniquely identified based on corresponding information (e.g., patient ID, date, time, study, study visit, procedure, measurement unit, etc.). Thus, each row contains one piece of data and many columns of identifying information. While this method may lead to bloated files due to many blank columns, it is comprehensive and consistent across studies. The data in SDTM are broken into multiple “domains” such as demographics (DM), subject visits (SV), concomitant medications (CM), exposure (EX), adverse events (AE), ECG results (EG), laboratory results (LB), PK concentrations (PC), PK parameters (PP), and vital signs (VS). Each domain usually is a single file with the domain as the filename (e.g., CM.xpt). These SDTM data sets can be used directly for analysis if no further calculations are necessary.


Analysis data sets are created to enable the statistical and scientific analysis of the study results. ADaM specifies the fundamental principles and standards to ensure that there is clear lineage from data collection to analysis. The ADaM data sets are the “authoritative source for all data derivations used in statistical analyses.” For example, if change from baseline in body weight was the primary efficacy variable, the SDTM would contain each body weight measurement. An ADaM data set would include the derived change from baseline body weight for each time point to be included in the statistical analysis. The ADaM data sets are not required unless data derivations are performed based on SDTM data. In addition, ADaM data sets should only be derived from SDTM datasets.

Tech-enabled solutions to address the burden of data management

When PK data isn’t in an easily convertible electronic CDISC format, it can significantly prolong time to submission, increasing the time for important medicines to reach patients. Fortunately, technology solutions can alleviate the data management burden on scientists and facilitate faster regulatory submissions.

For example, Certara’s PK Submit™ is a technology solution for automating the creation of electronic PK CDISC domains during non-compartmental analysis (NCA). When PK Submit is used with Phoenix WinNonlin™, this solution can significantly save time and resources. These domains can be generated within minutes, from the same source, by a PK scientist who does not need to be a CDISC expert.

Your trusted partner for modern drug development

Certara has expertise in pharmacokinetics and developing software tools and services for generating and managing PK data, is a platinum member of CDISC, and has a seat on the CDISC Advisory Council. In addition, we have a certified CDISC trainer on staff who is actively involved in creating the ADaM NCA standard.

Certara specializes in offering software and services that span the drug development life cycle – from discovery to patient access. The company’s expertise, modeling and simulation solutions, and regulatory-focused software are designed to speed the development timeline and accelerate regulatory approval. 

To learn more, read this article from Scientific Computing World.

About the author

Kevin Trimm
By: Kevin Trimm
Kevin brings over 17 years of experience in pharmaceutical research and software development. He spearheads product strategy for Certara’s Data Science platforms and leads implementation of technology solutions, collaborating closely with clients.

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