Noncompartmental Analysis

Introduction

Non-compartmental analysis (NCA) is a simple and quick method for evaluating the exposure of a drug. It allows you to evaluate things like linearity and in vivo exposure. To illustrate this consider an antibody given in a subcutaneous injection. The actual profile a patient might experience is given in the solid black line. But we don’t yet have the ability to sample in a continuous fashion. What we might observer is given by the blue points.

Generally NCA will determine the following directly from the data:

• Cmax - Maximum observed concentration (units=concentration)
• Tmax - The time where the maximum concentration was observed (units=time)
• AUC - The area under the curve (\(units=time \times concentration\))
• AUMC - The area under the first moment curve (\(units=time^2 \times concentration\))

These properties are all based on observational data. So the Cmax and Tmax will most certainly not be at the actual maximum concentration but as long as we sample judiciously it will give us a good approximation. Similarly, the calculated AUC and AUMC will be different than the actual values. To calculate the areas you need to dig back into your calculus text books to the trapezoid method. Basically each sampling interval is a trapezoid and the area of each is calculated and added up for all of the n samples:

\[ AUC = \int_0^{t_f} Cdt \approx \sum_{i=1}^{n-1}{\frac{C_i+C_{i+1}}{2}\times (t_{i+1}-t_{i})}, \ \ \ \ \ AUMC = \int_0^{t_f} t\times Cdt \approx \sum_{i=1}^{n-1}{\frac{t_iC_i+t_{i+1}C_{i+1}}{2}\times (t_{i+1}-t_{i})} \]

This can be done in Excel pretty easily. Depending on the data and the analysis other properties can be calculated. For example we can calculate the clearance, steady-state volume of distribution and terminal half-life:

• Clearance: \(CL = \frac{Dose}{AUC}\)
• Mean residence time: \(MRT = \frac{AUMC}{AUC}\)
• Steady state volume of distribution: \(V_{ss} = MRT \times CL\)
• Half-life: Terminal slope of the natural log of the data

Properties like AUC and AUMC can also be be calculated using extrapolation from the last time point to infinity to account for data beyond the observations at hand. The subsequent values of clearance, volumes of distribution, etc can also change with extrapolation.

There is a lot of nuance associated with these calculations, and it is good to rely on software that focuses on this type of analysis. The PKNCA package has been developed with this in mind. Ubiquity provides a set of functions to automate NCA and reporting for preclinical data. These functions act as a wrapper for the PKNCA package which does most of the heavy lifting. Only a small subset of the PKNCA functionality is used here. If more extensive analysis is necessary then PKNCA can be used directly.

This vignette contains a series of examples on how to perform NCA in ubiquity. To make a copy of these scripts and other supporting files in the current working directory run the following:

library(ubiquity)
fr = workshop_fetch(section="NCA", overwrite=TRUE)

This creates several files in the working directory. First are data sets:

• pk_all_sd.csv - PK data for multiple subjects dosed either IV or SC at different levels
• pk_all_md.csv - Multiple dose data with intensive sampling on the first and last dose
• pk_sparse_sd.csv - Single dose data with sparse sampling

Next the following scripts demonstrate how to perform NCA:

• analysis_nca_sd.R - Single dose data for individuals
• analysis_nca_md.R - Multiple dose data for individuals
• analysis_nca_sparse.R- Average NCA when analyzing sparsely sampled data

Quick Template for Running NCA

You should read the rest of the vignette below to understand the required data format and how the functions work. But if you are returning to this and you just want a template for running NCA you can use the following:

library(ubiquity)
cfg = build_system()
fr = system_fetch_template(cfg, template = "NCA")

This will create the file analysis_nca.R in the current working directory. You can just uncomment/edit that script to get started.

Single Dose Data

This example follows the script analysis_nca_sd.R, and uses the dataset pk_all_sd.csv.

cfg = build_system(system_file="system.txt")
cfg = system_load_data(cfg, dsname     = "PKDATA", 
                            data_file  = "pk_all_sd.csv")

cfg = system_nca_run(cfg,  dsname        = "PKDATA", 
                           dscale        = 1e6, 
                           analysis_name = "pk_single_dose", 
                           extrap_C0     = FALSE, 
                           dsmap         = list(TIME    = "TIME_HR", 
                                                NTIME   = "TIME_HR", 
                                                CONC    = "C_ng_ml", 
                                                DOSE    = "DOSE",
                                                ROUTE   = "ROUTE", 
                                                ID      = "ID"),
                           NCA_options   = list(max.aucinf.pext  = 10) )

NCA_results = system_fetch_nca(cfg, analysis_name = "pk_single_dose")

nca_cols = system_fetch_nca_columns(cfg, "pk_single_dose")
rhandsontable(nca_cols[["NCA_col_summary"]],  width="100%", height=200)

Automated Reporting

PowerPoint

Once the NCA has been run, the results can be appended to an open PowerPoint report. Here we initialize an empty report then use the function system_rpt_nca() and the analysis name assigned to the analysis above (pk_single_dose) to attach those results. Then we can write the file:

cfg = system_rpt_read_template(cfg, template="PowerPoint")
cfg = system_rpt_add_slide(cfg, 
  template = "title_slide",
  elements = list( title= list(content = "NCA Single Dose PK", type    = "text")))
cfg = system_rpt_nca(cfg=cfg, analysis_name="pk_single_dose")
system_rpt_save_report(cfg=cfg, output_file=file.path("output","pk_single_dose-report.pptx"))

For each dose a summary slide will be produced and a full timecourse will be created showing all of the data for that subject/group in the dataset:

• Actual data in gray
• Data used for NCA in green
• Initial extrapolated concentrations in orange (solid)
• Points used for extrapolating “iv bolus” data are shown in orange open circles

Below shows the slides generated for an individual subject and the first set of summary slides for the analysis. Notice that because extrap_C0 was set to FALSE C0 was not calculated (-1). Because this individual received a SC dose the estimate for Vp is also not calculated (-1). Also note that the data for this individual did not allow for extrapolation of AUC to infinity (NA). As a result the parameters that depend this value also resulted in (NA). The timecourse figure shows the data in the dataset (gray closed symbols, solid line) and the data used for NCA (green open symbols, dashed line). This way you can visually confirm at the subject level what data was used.

Reporting for single dose NCA

Word

Similarly, a Word report can be generated by appending the results to an already initialized Word report. This is done by setting the template to "Word" when calling system_rpt_read_template(). This will attach the same content to the report as with PowerPoint report above.

cfg = system_rpt_read_template(cfg, template="Word")
cfg = system_rpt_nca(cfg=cfg, analysis_name="pk_single_dose")
system_rpt_save_report(cfg=cfg, output_file=file.path("output","pk_single_dose-report.docx"))

For more information on integrated report generation see the Reporting vignette.

nca_table = system_nca_summary(cfg, 
       analysis_name    = "pk_single_dose",
       params_include   = c( "ID", "cmax", "tmax", "half.life", "auclast"),
       params_header    = list(cmax = c( "<label>", "(ng/ml)")),
       ds_wrangle       = "NCA_sum = NCA_sum %>% dplyr::filter(Dose == 30)",
       summary_stats    = list("<MEAN> (<STD>)" = c("auclast", "half.life"),
                               "<MEDIAN>"       = c("tmax")), 
       summary_labels   = list(MEAN             = "Mean", 
                               STD              = "Std Dev", 
                               N                = "N obs",
                               MEDIAN           = "Median", 
                               SE               = "Std Err."),
       summary_location = "ID")

nca_table[["vignette"]] = set_table_properties(nca_table$nca_summary_ft,layout = "autofit")

Multiple Dose Data

This example follows the script analysis_nca_md.R, and uses the dataset pk_all_md.csv. If we rebuild the system and load the multiple dose dataset we can see that the dataset looks almost identical to the single dose data set. The primary difference is that there are two extra columns: NTIME_HR and EXTRAP. The first column (NTIME_HR) contains the time since the last dose. The EXTRAP column is optional and allows the user to specify the number of points to back extrapolate C0 for iv bolus dosing. This will have no effect unless it is specified in the dsmap below. If you scroll through the data you can see that there is intensive sampling for DOSENUM 1 and 6. But for doses 2-5 there are only three samples per interval. These latter dose intervals (2-5) will be ignored with the default value of NCA_min (4) which defines the minimum number of samples required for analysis.

cfg = build_system(system_file="system.txt")
cfg = system_load_data(cfg, dsname     = "PKDATA", 
                            data_file  = "pk_all_md.csv")

Next we perform the NCA and reporting as before. The only difference here is that we’ve removed the extrap_C0 option. The default value is TRUE so back-extrapolation will occur. For IV dosing this is log-linear extrapolation to a nominal time of zero. For SC dosing this will be 0 for the first dose and the last observation from the last dosing interval for subsequent dosing.

cfg = system_nca_run(cfg, dsname        = "PKDATA", 
                          dscale        = 1e6, 
                          analysis_name = "pk_multiple_dose", 
                          dsmap         = list(TIME    = "TIME_HR", 
                                               NTIME   = "NTIME_HR", 
                                               CONC    = "C_ng_ml", 
                                               DOSE    = "DOSE",
                                               ROUTE   = "ROUTE", 
                                               ID      = "ID",
                                               DOSENUM = "DOSENUM",
                                               EXTRAP  = "EXTRAP"))
cfg = system_rpt_read_template(cfg, template="PowerPoint")
cfg = system_rpt_nca(cfg=cfg, analysis_name="pk_multiple_dose")
system_rpt_save_report(cfg=cfg, output_file=file.path("output","pk_multiple_dose-report.pptx"))

The same files are generated with the pk_multiple_dose prefix in the out output folder. The summary can be seen here:

Below you can see the report slides for two subjects. The first subject was dosed IV. Again the gray markers/solid line show the full timecourse for that subject from the dataset. The data used for NCA (dose 1 and 6) is shown in green. The solid orange marker shows the extrapolated C0. If you look closely the open orange markers show the data points used for extrapolation connected with an orange dashed line. The second subject was given SC doses. For the first dose the extrapolated C0 was zero. This can be seen by the green dashed line extending down to zero. The C0 for dose 6 is simply the last observation carried forward as shown by the solid orange marker.

Reporting for multiple dose NCA

Sparse Sampling

This example follows the script analysis_nca_sparse.R, and uses the dataset pk_all_sparse.csv. First we rebuild the system and load the sparsely sampled dataset. This data is very similar to the single dose dataset above except each ID only has three samples.

cfg = build_system(system_file="system.txt")
cfg = system_load_data(cfg, dsname     = "PKDATA", 
                            data_file  = "pk_sparse_sd.csv")

When we run the NCA we need to tell the function that we want to perform a sparse analysis. This is done by setting the sparse input to TRUE. We also need to provide information on how to group cohorts. This is done by providing a SPARSEGROUP option in the dsmap. In this case we can just use the DOSE column, however a different column could have been used.

cfg = system_nca_run(cfg, dsname        = "PKDATA", 
                          dscale        = 1e6, 
                          analysis_name = "pk_sparse",
                          sparse        = TRUE,
                          dsmap         = list(TIME        = "TIME_HR", 
                                               NTIME       = "TIME_HR", 
                                               CONC        = "C_ng_ml", 
                                               DOSE        = "DOSE",
                                               ROUTE       = "ROUTE", 
                                               ID          = "ID",
                                               SPARSEGROUP = "DOSE"))
cfg = system_rpt_read_template(cfg, template="PowerPoint")
cfg = system_rpt_add_slide(cfg, 
  template = "title_slide",
  elements = list( title= list(content = "NCA of Sparsely Sampled PK", type    = "text")))
cfg = system_rpt_nca(cfg=cfg, analysis_name="pk_sparse")         
system_rpt_save_report(cfg=cfg, output_file=file.path("output","pk_sparse-report.pptx"))

Analysis of sparse data will calculate an average concentration at each time point and use those average values for NCA. The same files are generated from the analysis. You can see the summary here:

To confirm what was done and identify any outliers that may be causing problems, you can use the report output. Again the gray markers show the data, the green dashed line will show the value used for NCA, and the orange will show the C0 estimate.

Reporting for sparse sampling NCA