ADaM datasets play a central role in preparing pharmacokinetic, or PK, data for analysis and reporting. In this QCast episode, Jullia and Tom explore how ADPC and ADPP are created, how they relate to SDTM PC and PP, and why PK concentration data and derived PK parameters need different dataset structures.
The discussion also looks at the operational details that can affect PK analysis, including sample timing, dosing information, lab data transfers, missing values, below-quantification-limit results and study-specific analysis rules. Getting these details right helps teams create datasets that are easier to interpret, reproduce and review.
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Key Takeaways
How ADPC Supports Concentration Analysis
ADPC is typically built from SDTM PC and is used for PK concentration data. It is usually structured as one record per subject, per analyte and per time point, with analysis values, timing information, flags and subject-level details added to support reporting. This structure helps teams work with concentration profiles in a consistent way.
Why ADPP is Separate from Concentration Data
ADPP is generally derived from SDTM PP and focuses on PK parameters rather than individual concentration results. Parameters such as Cmax, Tmax and AUC summarise the concentration-time profile, so ADPP is usually structured as one record per subject and parameter. Keeping these outputs separate from concentration records helps clarify what has been observed and what has been derived.
Where Timing and Traceability Affect PK Reporting
PK analysis depends on knowing when a sample was taken in relation to dosing. Relative time, planned and actual collection times, pre-dose samples and timing windows can all influence how data are interpreted. Analysis flags and criteria variables make those decisions visible, so the dataset shows what was analysed, what was excluded and why.
Full Transcript
Jullia
Welcome to QCast, the show where biometric expertise meets data-driven dialogue. I’m Jullia.
Tom
I’m Tom, and in each episode, we dive into the methodologies, case studies, regulatory shifts, and industry trends shaping modern drug development.
Jullia
Whether you’re in biotech, pharma or life sciences, we’re here to bring you practical insights straight from a leading biometrics CRO. Let’s get started.
Tom
Today we’re talking about the creation of ADaM datasets for pharmacokinetic analysis, or PK analysis. PK data can feel quite specialised, so maybe start us off simply. What these datasets doing?
Jullia
They’re preparing PK data for analysis and reporting. In PK, we’re usually dealing with concentration data first, then derived parameters such as Cmax, Tmax, AUC and half-life.
The two ADaM datasets that usually come up are ADPC and ADPP. ADPC supports PK concentration data, while ADPP supports PK parameter data. The aim is to make the data structured, traceable and ready for tables, listings, figures and further analysis.
Tom
What happens before that?
Jullia
A biological sample is collected at the site, often blood or plasma, and the concentration is measured by a bioanalytical lab. Those results may arrive as a transfer file, while the clinical database holds subject, visit, treatment and dosing information.
On the SDTM side, concentration results sit in the PC domain, which stands for pharmacokinetic concentrations. Derived PK parameters sit in the PP domain, which stands for pharmacokinetic parameters.
Tom
Can you give an example of that PC and PP split?
Jullia
Yes, so if a subject has PK samples taken pre-dose, then one hour, two hours, four hours and eight hours after dosing, each measured concentration belongs in PC. PC is about the sample result and the time it was collected.
Then those concentration-time data are used to calculate parameters. The highest observed concentration might become Cmax. The time that maximum occurred might become Tmax. The area under the curve becomes AUC. Those calculated results belong in PP.
Tom
And ADPC and ADPP are built from those SDTM domains?
Jullia
Yes. ADPC is generally derived from SDTM PC, and ADPP is generally derived from SDTM PP. But ADaM is not just a copy of SDTM. It adds the analysis structure needed for reporting.
For PK, that can mean bringing in treatment and demographic information from ADSL, and dosing date-time information from EX.
Tom
That timing point feels central. What kinds of timing variables are we talking about?
Jullia
A key one is relative time. For example, the dataset may derive the time between the start of dosing and the PK sample collection time. A pre-dose sample may even have a negative relative time, which is expected.
There can also be a difference between planned and actual collection times. The protocol may schedule a sample at two hours post-dose, but the actual sample might be taken a little early or late. ADPC needs to preserve enough detail for those timing decisions to be reviewed and used consistently.
Tom
A common misconception might be that once the lab file is loaded, the concentration dataset is basically done. What gets missed there?
Jullia
The lab result is only one part of the record. It still has to be linked to the right subject, visit, analyte, time point and dosing event. Units need to be clear. Numeric and character analysis values need to be handled correctly.
There may also be missing values or values below the lower limit of quantification. Those need defined handling rules, usually set out in the protocol or reporting and analysis plan. If that logic is not represented clearly, the same data can be interpreted in different ways later.
Tom
What does ADPC usually look like as a dataset?
Jullia
ADPC is typically one record per subject, per analyte, per time point. So one subject may have several concentration records across a dosing interval, and each analyte is represented separately.
It will usually include identifiers, visit information, time point variables, parameter variables, analysis values and analysis flags. It also carries key subject-level information from ADSL, so the dataset can support analysis directly.
Tom
And in ADPC, PARAM describes the analyte rather than a PK parameter like Cmax?
Jullia
Exactly. In ADPC, PARAM describes the analyte in the concentration data. PARAMCD is the short code, and PARAMN is the numeric version. That differs from ADPP, where PARAM describes a derived PK parameter such as Cmax.
The analysis value is often held in AVAL as a numeric value, with AVALC as the character equivalent. That distinction helps when source values are not straightforward numeric results, such as below-quantification-limit values.
Tom
So ADPC supports the concentration profile. What changes when we move to ADPP?
Jullia
ADPP is about the derived parameters. It is typically one record per subject, per parameter. So instead of a record for each concentration time point, you may see records for Cmax, Tmax, AUC and other PK parameters.
ADPP is derived from SDTM PP, with subject-level information added from ADSL. It carries the parameter name, code, result, unit and analysis-ready values needed for summaries and reporting.
Tom
Where do analysis flags and criteria variables fit into this?
Jullia
Analysis flags identify which records are used for a particular analysis. For example, ANLzzFL can select records for a defined output or analysis rule. That avoids burying record selection only in output programs.
Criteria variables, such as CRITy, CRITyFL and CRITyFN, can capture whether a record meets a defined condition. In PK, that might relate to an acceptable timing window or an exclusion criterion. The value is that the dataset shows what happened, rather than leaving the logic hidden.
Tom
Let me challenge one point. If ADPC and ADPP are standardised, does every study handle them in the same way?
Jullia
Not necessarily. While the standards give structure, the study still drives many decisions. The protocol, reporting and analysis plan, sponsor conventions and PK strategy all affect how variables are derived and how records are flagged.
For example, the handling of below-quantification-limit values may vary. Timing windows may vary. The analytes, dosing design and parameter set may also differ. Standardisation helps people understand the dataset, but it doesn’t remove the need for study-specific decisions.
Tom
Before we close, what should teams take away from this?
Jullia
First, ADPC and ADPP are analysis datasets, not just reformatted SDTM domains.
Second, timing is central. Dosing times, sample collection times and relative time variables all affect how the data are interpreted.
And third, traceability matters. The dataset should show what was analysed, what was excluded, and why.
Jullia
With that, we’ve come to the end of today’s episode on the creation of ADaM datasets for pharmacokinetic, or PK, analysis. If you found this discussion useful, don’t forget to subscribe to QCast so you never miss an episode and share it with a colleague. And if you’d like to learn more about how Quanticate supports data-driven solutions in clinical trials, head to our website or get in touch.
Tom
Thanks for tuning in, and we’ll see you in the next episode.
About QCast
QCast by Quanticate is the podcast for biotech, pharma, and life science leaders looking to deepen their understanding of biometrics and modern drug development. Join co-hosts Tom and Jullia as they explore methodologies, case studies, regulatory shifts, and industry trends shaping the future of clinical research. Where biometric expertise meets data-driven dialogue, QCast delivers practical insights and thought leadership to inform your next breakthrough.
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