Understanding How Pharmacokinetic Data and CDISC Standards Work Together

By Ashley Kesler, MSc (Sr. Director, Statistical Programming, Biostatistics) and Gwen Wyatt (Director, PK/PD Data Programming, Clinical Pharmacology Modeling and Simulation)

CDISC (Clinical Data Interchange Standards Consortium) has established the industry-wide standards for how clinical research data is organized and submitted, streamlining regulatory review and enabling more consistent decision making (see What Is CDISC and What Are CDISC Data Standards?). Within this framework, pharmacokinetic (PK) data plays a unique role in clinical research and PK analysis. PK describes how a drug is absorbed, distributed, metabolized, and excreted (ADME), providing critical insights into a drug’s concentration, exposure, and overall behavior in the body. Converting pharmacokinetic data into CDISC-compliant formats ensures that these insights are captured in a way that is both scientifically rigorous and aligned with regulatory expectations and CDISC standards.

Sources of PK CDISC Data

It is important to understand the differences between the sources and types of pharmacokinetic data collected during a trial. (For a deeper look at PK and ADME, see Pharmacokinetics: Definition & Use in Drug Development.) Equally valuable is understanding how to combine those different data sources in the correct format to develop a dataset that can be used in a noncompartmental PK analysis (NCA). An NCA provides the most elementary PK information of a drug, such as peak concentration, elimination half-life, etc. The two main sources of data necessary for an NCA are:

1. Case report form (CRF) data
2. Bioanalytical lab data

CRF Data

Data collected directly at the clinical study site is recorded in CRFs or eCRFs. These data are then entered into or managed within an electronic data capture (EDC) system. The CRF/eCRF includes the dates and times of each sample collected, which is necessary for PK analysis. Examples of the types of CRF data captured in the EDC include:

• Participant enrollment and randomization
• Demographics
• Drug exposure and accountability
• Vital signs and medical history
• Other protocol-specific data

Bioanalytical Lab Data

Unlike the study data collected (such as demographics) and stored solely in the EDC system, participants’ PK samples, such as blood, urine, plasma, or other biological fluids like cerebrospinal fluid, require additional processing by a bioanalytical (BA) lab for PK analysis. PK samples collected from the clinical study site are transported to a BA lab to be processed. The BA lab analyzes the PK samples and provides drug concentration values that are not captured in the EDC system which are needed for PK analysis. Data generated by the BA lab tends to contain minimal study information, limited to only what is necessary to uniquely identify the PK samples along with the drug concentration results.

SDTM and ADaM Datasets

Adhering to CDISC standards, data from the EDC system and external sources are then organized into SDTM (Study Data Tabulation Model) datasets.   From those SDTM datasets, ADaM (Analysis Data Model) datasets are developed to support PK analysis, ensuring analysis-ready and regulatory-compliant datasets in line with the study protocol and Statistical Analysis Plan (SAP), or Pharmacokinetics Analysis Plan (PKAP) when developed separately.

PC and ADPC Domains

The Pharmacokinetic Concentrations (PC) SDTM domain is created by combining information from the BA lab data, EDC data, and other SDTM domains, which can include Exposure (EX), Demographics (DM), Protocol Deviations (DV), Trial Arms (TA), Subject Visits (SV), and Trial Visits (TV).  The BA lab data and raw sampling time data from the EDC system are merged based on the unique identifiers for each sample available in both the BA lab and EDC datasets. This often includes variables such as participant identifier, matrix, sampling day, and nominal sampling times. This serves as the foundation for the PC domain, which is built by combining this information with the additional necessary SDTM sources. The Analysis Dataset of Pharmacokinetic Concentrations (ADPC) ADaM domain is a direct translation of the information in the PC domain and is generated following PC. The ADPC domain adds information needed to perform analyses, such as NCA. Generation of an ADPC domain can include the addition of:

• Participant demographics
• Treatment and dosing information
• Calculated elapsed time following dosing
• Imputation of concentration values that were below the limit of quantification
• Flagging for both the analysis and associated tables, listings, and figures (TLFs)

PP and ADPP Domains

Upon completion of the NCA, results are compiled to generate the Pharmacokinetic Parameters (PP) domain. Like the PC domain, the PP domain is a reorganization of the source data into CDISC standards. It describes the PK parameters calculated from time-concentration profiles in PC. This process makes use of the raw parameter export(s) as well as other SDTM domains (typically just PC, but on occasion others may be needed). The analysis counterpart to the PP domain is the Analysis Dataset of Pharmacokinetic Parameters (ADPP) domain and is a direct translation of the information in the PP domain, generated following the PP domain. This dataset includes the same core demographic and treatment variables added to ADPC, as well as any necessary variables to support analysis, as outlined in the SAP or PKAP.

Relating Records Domain

The Related Records (RELREC) domain is often overlooked but is an important part of a PK CDISC package. The PK-specific RELREC dataset is used to relate the PC domain to the PP domain in an effort to highlight the PK concentrations used in calculating the PK parameters. The identifying variable used to relate the records between datasets is normally the Unique Subject Identifier (USUBJID) and the sequence number (SEQ) in the PC and PP domains. The RELREC domain aids in making the final CDISC package cohesive and traceable as it specifies cross-domain relationships.

Conclusions

In order to create a clean and comprehensible clinical trial submission that includes PK data and analysis, it is important for the analyst to understand both the sources of data received that will be used in the analysis, as well as the specific formatting and dataset requirements that are part of a CDISC submission package. Effective communication between analysts and data managers helps ensure that the submitted package provides a clear representation of the data that was collected and analyzed. Allucent is an industry leader in PK CDISC standards and has extensive experience generating PK datasets for legacy, planned, and ongoing studies. Allucent can help guarantee your program’s datasets are in compliance with the FDA’s required CDISC standards.