In this recorded presentation, a member of Quanticate's statistical programming team explores the creation of two ADaM datasets; ADPC and ADPP for Pharmacokinetic (PK) Analysis.
Video Transcript
Hello everyone! Welcome to this presentation on the creation of the ADPC and ADPP datasets, the two ADaM datasets of PK analysis. I’ll start with a brief introduction to our two datasets: PC and PP. Next, I’m going to take you through the PK process flow implemented at the various stages of PK submission. Then we will look at the creation of the ADPC dataset, followed by the ADPP dataset. I have outlined some of the specific variables in each dataset which are vital for the PK analysis.
The FDA encourages the use of CDISC standards for the regulatory submission of clinical trial data. "Let’s talk about how Pharmacokinetic, or PK, data is handled in clinical data submissions. First, on the SDTM side, PK data is captured in two domains: the PC domain for Pharmacokinetic Concentrations, which are actual measurements taken from biological samples like plasma or blood, and the PP domain for Pharmacokinetic Parameters, which are derived metrics which are calculated from the data in the PC domain. Now, when it comes to analysis, we move into the ADaM layer, where datasets are structured based on the Basic Data Structure, or BDS, format. Here ADPC is the ADaM dataset for PK Concentrations, derived from SDTM PC, and ADPP is for PK Parameters, derived from SDTM PP. These datasets contain both collected data, as well as derived data, to enable PK analysis and for the creation of Tables, Listings, and Figures on the PK results for regulatory submission.
Here we come to the PK Process Flow. The sample is drawn at the site, and the concentration is measured and sent in the form of a CSV file. So we have the concentration data in the form of a CSV file, and on the other hand we have the collected data in the form of the dataset. We merge both of these to create the SDTM PC domain. The ADPC dataset is then derived from the SDTM PC Domain. When deriving the ADPC dataset, the ADSL (subject level analysis) dataset is merged to retrieve treatment group information and demographic information. This ADPC dataset can be directly used to create some of the concentration Tables, Listings, and Figures, and the dataset is also sent as a CSV file for WNL PK Parameter derivation. The summarized PK parameters from pharmacokineticists are fed back to us, and then converted to SDTM PP Domain. The ADPP dataset is then derived from the SDTM PP Domain with additional information from ADSL in the same way as it was done with ADPC dataset.
Next, we will take a look at the creation of the ADPC dataset. The ADaM basic data structure or BDS provides sufficient flexibility to support PK analysis. By working according to the ADaM rules for BDS, we can create a standardized PK dataset. In order to create the ADPC dataset, we merge the PC domain with the EX domain, which is the exposure dataset, to retrieve treatment data and time data. The ADSL dataset is also integrated into the ADPC dataset to import general subject level information. Additionally, some derived variables are added to ADPC which have their own purposes such as the handling of missing data, handling of values below threshold, and other sponsor specific data handling.
Now let’s look into the structure of the ADPC dataset. ADPC is used for the submission of all concentration results, and has the structure of one record per subject, per analyte, and per time point. Hence, concentration time profiles are stored across multiple records. ADPC typically contains all relevant variables from SDTM PC like USUBJID, VISIT information, and each time point within a particular VISIT. The primary subject level data like treatment information from ADSL are imported to ADPC dataset, and some additional variables are required for the PK analysis such as the relative time variable.
The relative time variable is the relative time elapsed since the start of dosing for each PK profile, which is added to the ADPC dataset. The actual concentrations measured from the PC domain are rederived at ADPC with AVALC variable being the character value of the concentration measure, and AVAL being the numeric equivalent. Here the missing concentration measure value is imputed to zero in AVAL variable. The qualifying records for analysis are flagged with the analysis flag variable ANL0FL.
Now let’s look into some of the important variables which we created at the ADPC level. Those variables are highlighted here are PARAM, PARAMN, PARAMCD, ADTM, ASTDTM, AENDTM, AVISIT, AVISITN, ATPT, ATPTN, ARELTM, ARELTMU, ANLzzFL, AVAL, AVALC, CRITy, CRITyFL, and CRITyFN.
Now let’s take a look into each variable in detail. The PARAM variable contains the description of the analytes being analyzed in the pharmacokinetic concentration, and the abbreviation of the analyte is stored in PARAMCD. PARAMN is the numeric counterpart of each analyte.
The date time variable ADTM is the date and time associated with the analysis value AVAL, and is the numeric version of the PCDTC variable from the PC domain. ASTDTM and AENDTM are associated with start and end time of an analysis interval, for example urine collection.
The VISIT variables, AVISIT and its numeric counterpart, AVISITN are derived from the variables VISIT and VISITNUM from the PC domain. All PK concentrations that refer to the same dosing event (exposure) will have the same AVISITN value.
The planned analysis time points are represented in the ATPT (actual time point) and ATPTN (numeric representation) variables. Only for pre-dose values do they differ from the PCTPT (planned time point) and PCTPTNUM (the numeric representation) variables for the PC domain. Negative (pre-dose) values of PCTPTNUM can be set to 0 in ATPTN to maintain a non-negative time point scale for analysis. If PCTPTNUM contains the planned time points in minutes this can be converted into hours in the ATPTN variable of the ADPC dataset.
The relative time variables needed for PK analysis are stored in the variable ARELTM. This variable is calculated as the time difference between the PK sample collection datetime PCDTC and the reference start date time of treatment EXSTDTC from the Exposure (EX) domain. This value will be negative for pre-dose values and the unit of relative time is stored in the variable ARELTMU.
ANLzzFL is an analysis record flag that can be used to identify a specific subset of records for one or more analyses. The zz is an index that distinguishes between different record selection algorithms or analysis purposes, and can range from 01-99.
The analysis value in standard units is recorded in the AVAL variable, in most cases being equal to PCSTRESN from the PC domain. The character counterpart is recorded in AVALC. The AVAL variable may be adjusted according to various data handling techniques specified in the protocol or the reporting and analysis plan (RAP). For example, values below the lower limit of quantification may be imputed to zero in AVAL.
Analysis criteria are captured using the CRITy variable, where y ranges from 0 to 9 to represent different evaluation rules. For example, if more than 10% of samples are collected too early or too late relative to the planned time point, those samples can be flagged using a CRITy variable. The result of each criterion—such as whether a sample should be excluded from analysis—is recorded in the corresponding CRITyFL (flag, value Y or N) and CRITyFN (numeric flag, value 0 or 1) variables.
The ADPC dataset can then be directly fed into the Phoenix WinNonlin software or alternative PK software packages to derive pharmacokinetic parameters, such as Cmax, Tmax, AUC, half-life etc. These parameters are subsequently used to populate the SDTM PP domain which is in turn merged with the ADSL dataset to create the ADPP dataset. The ADPP dataset is used in the statistical analysis of the PK data.
The ADPP dataset has a structure of one record per subject per parameter. Similarly to ADPC, we retain relevant information from SDTM PP like USUBJID, the PK parameter variables like PPTESTCD, PPTEST, and the result of each parameter in PPSTRESN and PPSTRESC variables, along with their units in the PPSTRESU variable. All subject level data is imported from the ADSL dataset. To be in accordance with CDISC ADaM guidelines, we have PARAMCD, PARAM variables, and AVAL, AVALC variables. These values are rederived in the ADPP dataset, which contains the pharmacokinetic parameter descriptions and their corresponding results.
Below are some of the important variables within ADPP dataset. These are PARAM, PARAMN, PARAMCD, ANLzzFL, ANLzzFD, AVAL, AVALC, CRITy, CRITyFL, and CRITyFN.
The value of PARAM is the PK parameter with its unit (for example Cmax, with its unit ng/mL) and the abbreviation of the PK parameter is stored in the PARAMCD variable. The numeric counterpart is presented in PARAMN. The qualifying records for analysis can be flagged in the analysis flag variable, ANLzzFL, where the zz can range from 01 to 99. AVAL is derived from PPSTRESN, which is the numeric result in standard units from the PP domain, and AVALC is the character counterpart. PK parameters may be included or excluded from the analysis based on the criteria as specified in the protocol or the reporting and analysis plan (RAP) by using the CRIT variables CRITy, CRITyFL and CRITyFN where the y ranges from 0 to 9.
Thank you for listening.
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