As many sponsors can attest, the preparation of CDISC-compliant datasets is time-consuming and costly. It is important to consider current and future data regulations when planning studies for your development program.
The PC domain, containing PK concentrations, and the PP domain, containing PK parameters data calculated by PK scientists, are two examples of key SDTM domains for PK analyses and pharmacometrics. At PhInc. Modeling, we understand that using CDISC standards might represent additional cost but it can be used to the sponsor’s advantage by:
- Standardizing analysis and reporting of data
- Facilitating pooling of data for future analyses such as PK population modeling
- Facilitating study data review by regulators
- Improving transferability of study results (including PK)
An example workflow incorporating SDTM datasets in a typical CDISC compliant PK is illustrated in the flow chart on the left.
Implementing PK CDISC standards
The main challenge is first to efficiently coordinate with all stakeholders in the CDISC process: data management, bioanalysis and regulatory submission.
Hence, we always suggest involving experienced PK scientists early in the process, to ensure good communications between players and to avoid having the different teams working in isolation (in silos).
How PhInc. Modeling can help?
- By involving pharmacometricians and PK experts in the process of data transfers, as well as analysis dataset creation (processes typically reserved to data managers and clinical programmers). PhInc. Modeling ensures that transferred data comprise all the essential elements required for performing analyses/modeling. This reduces delays after analysis start by reducing back and forth and increases the compliance to CDISC standards. For instance, we have observed that very few CDISC/data management teams process the internal resources to ensure delivery of high-quality PK datasets in CDISC format, particularly for complex PK studies.
- By using a native CDISC process for PK analyses, we ensure the seamless delivery of CDISC compliant results (such as SDTM datasets) that are easily reproducible and consider all the aspects of the analysis (e.g. reasons why some PK parameters are not calculable, flags, populations). Both the source (bioanalysis) data and the analysis results (pharmacokinetics) are generated directly in CDISC format as opposed to the alternative approach of retroactively converting data to a CDISC format (mapping), a process which is prone to errors and may reduce the reusability value of the PK results.
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