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# Data format for NCA and CA analysis

The data set format used in PKanalix is the same as for the entire MonolixSuite, to allow smooth transitions between applications. The dosing information and the concentration measurements are recorded in a single data set. The dose information must be indicated for each individual, even if it is identical for all.

A data set typically contains the following columns: ID, TIME, OBSERVATION, AMOUNT. For IV infusion data, an additional INFUSION RATE or INFUSION DURATION is necessary. For steady-state data, STEADY-STATE and INTERDOSE INTERVAL column are added. Cells that do not contain information (e.g AMOUNT column on a line recording a measurement) have a dot. If a dose and a measurement occur at the same time, they can be encoded on the same line or on different lines. Sort and carry variables can be defined using the OCCASION, and CATEGORICAL COVARIATE and CONTINUOUS COVARIATE column-types. BLQ data are defined using the CENSORING and LIMIT column-types.

Headers are free but the columns must be assigned to one of the available column-types. The full list of column-types is available at the end of this page and a detailed description is given on the dataset documentation website.

Units are not recorded in PKanalix and it is not possible to convert the original data to adapt the units. It is the user’s responsibility to ensure that units are consistent in the data set (for instance if the amount is in mg, the concentrations in mg/L, then the calculated volumes will be in L). Similarly, the indicated amount must be an absolute value. It is not possible to give in a dose in mg/kg and let the software calculate the dose in mg based on the weight. This calculation must be done outside PKanalix.

Note that the NCA application does not allow to have in the dataset several observations at the same time for the same id.

Below we show how to encode the date set for most typical situations.

# Plasma concentration data

## Extravascular

For extravascular administration, the mandatory column-types are ID (individual identifiers as integers or strings), OBSERVATION (measured concentrations), AMOUNT (dose amount administered) and TIME (time of dose or measurement).

To distinguish the extravascular from the IV bolus case, in “Tasks>Run” the administration type must be set to “extravascular”.

If no measurement is recorded at the time of the dose, a concentration of zero is added for single dose data, the minimum concentration observed during the dose interval for steady-state data.

Example:

• demo project_extravascular.pkx

This data set records the drug concentration measured after single oral administration of 150 mg of drug in 20 patients. For each individual, the first line records the dose (in the “Amount” column tagged as AMOUNT column-type) while the following lines record the measured concentrations (in the “Conc” column tagged as OBSERVATION). Cells of the “Amount” column on measurement lines contain a dot, and respectively for the concentration column. The column containing the times of measurements or doses is tagged as TIME column-type and the subject identifiers, which we will use as sort variable, are tagged as ID. Check the OCCASION section if more sort variables are needed. After accepting the dataset, the data is automatically assigned as “Plasma”.

In the “Tasks/Run” tab, the user must indicate that this is extravascular data. In the “Check lambda_z”, on linear scale for the y-axis, measurements originally present in the data are shown with full circles. Added data points, such as a zero concentration at the dose time, are represented with empty circles. Points included in the $$\lambda_z$$ calculation are highlighted in blue.

After running the NCA analysis, PK parameters relevant to extravascular administration are displayed in the “Results” tab.

## IV infusion

Intravenous infusions are indicated in the data set via the presence of an INFUSION RATE or INFUSION DURATION column-type, in addition to the ID (individual identifiers as integers or strings), OBSERVATION (measured concentrations), AMOUNT (dose amount administered) and TIME (time of dose or measurement). The infusion duration (or rate) can be identical or different between individuals.

If no measurement is recorded at the time of the dose, a concentration of zero is added for single dose data, the minimum concentration observed during the dose interval for steady-state data.

Example:

• demo project_ivinfusion.pkx:

In this example, the patients receive an iv infusion over 3 hours. The infusion duration is recorded in the column called “TINF” in this example, and tagged as INFUSION DURATION.

In the “Tasks/Run” tab, the user must indicate that this is intravenous data.

## IV bolus

For IV bolus administration, the mandatory column-types are ID (individual identifiers as integers or strings), OBSERVATION (measured concentrations), AMOUNT (dose amount administered) and TIME (time of dose or measurement).

To distinguish the IV bolus from the extravascular case, in “Tasks>Run” the administration type must be set to “intravenous”.

If no measurement is recorded at the time of the dose, the concentration of at time zero is extrapolated using a log-linear regression of the first two data points, or is taken to be the first observed measurement if the regression yields a slope >= 0. See the calculation details for more information.

Example:

• demo project_ivbolus.pkx:

In this data set, 25 individuals have received an iv bolus and their plasma concentration have been recorded over 12 hours. For each individual (indicated in the column “Subj” tagged as ID column-type), we record the dose amount in a column “Dose”, tagged as AMOUNT column-type. The measured concentrations are tagged as OBSERVATION and the times as TIME. Check the OCCASION section if more sort variables are needed in addition to ID. After accepting the dataset, the data is automatically assigned as “plasma”.

In the “Tasks/Run” tab, the user must indicate that this is intravenous data. In the “Check lambda_z”, measurements originally present in the data are shown with full circles. Added data points, such as the C0 at the dose time, are represented with empty circles. Points included in the $$\lambda_z$$ calculation are highlighted in blue.

After running the NCA analysis, PK parameters relevant to iv bolus administration are displayed in the “Results” tab.

Steady-state must be indicated in the data set by using the STEADY STATE column-type:

• “1” indicates that the individual is already at steady-state when receiving the dose. This implicitly assumes that the individual has received many doses before this one.
• “0” or ‘.’ indicates a single dose.

The dosing interval (also called tau) is indicated in the INTERDOSE INTERVAL, on the lines defining the doses.

Steady state calculation formulas will be applied for individuals having a dose with STEADY STATE = 1. A data set can contain individuals which are at steady-state and some which are not.

If no measurement is recorded at the time of the dose, the minimum concentration observed during the dose interval is added at the time of the dose for extravascular and infusion data. For iv bolus, a regression using the two first data points is performed. Only measurements between the dose time and dose time + interdose interval will be used.

Examples:

In this example, the individuals are already at steady-state when they receive the dose. This is indicated in the data set via the column “SteadyState” tagged as STEADY STATE column-type, which contains a “1” on lines recording doses. The interdose interval is noted on those same line in the column “tau” tagged as INTERDOSE INTERVAL. When accepting the data set, a “Settings” section appears, which allows to define the number of steady-state doses. This information is relevant when exporting to Monolix, but not used in PKanalix directly.

After running the NCA estimation task, steady-state specific parameters are displayed in the “Results” tab.

# BLQ data

Below the limit of quantification (BLQ) data can be recorded in the data set using the CENSORING column:

• “0” indicates that the value in the OBSERVATION column is the measurement.
• “1” indicates that the observation is BLQ.

The lower limit of quantification (LOQ) must be indicated in the OBSERVATION column when CENSORING = “1”. Note that strings are not allowed in the OBSERVATION column (except dots). A different LOQ value can be used for each BLQ data.

When performing an NCA analysis, the BLQ data before and after the Tmax are distinguished. They can be replaced by:

• zero
• the LOQ value
• the LOQ value divided by 2
• or considered as missing

For a CA analysis, the same options are available, but no distinction is done between before and after Tmax. Once replaced, the BLQ data are handled as any other observation.

A LIMIT column can be added to record the other limit of the interval (in general zero). This value will not be used by PKanalix but can facilitate the transition from an NCA/CA analysis PKanalix to a population model with Monolix.

Note: the proper encoding of BLQ data can easily be done using Excel or R.
With R, the “CENSORING” column can be added to an existing “data” data frame using data$CENS <- ifelse(data$CONC=="BQL", 1, 0), for the case where the observation column is called CONC and BLQ data are originally recorded in this column with the string “BQL”. Then replace the “BQL” string by the LOQ value (here 0.2 for instance): data$CONC <- ifelse(data$CONC=="BQL", 0.2, data$CONC). With Excel, type for instance =IF(E2="BQL",1,0) (assuming the column containing the observations is E) to generate the first value of the “CENSORING” column and then propagate to the entire column. Finally, replace the “BQL” string by the LOQ value. Examples: • demo project_censoring.pkx: two studies with BLQ data with two different LOQ In this dataset, the measurements of two different studies (indicated in the STUDY column, tagged as CATEGORICAL COVARIATE in order to be carried over) are recorded. For the study 101, the LOQ is 1.8 ug/mL, while it is 1 ug/mL for study 102. The BLQ data are marked with a “1” in the BLQ column, which is tagged as CENSORING. The LOQ values are indicated for each BLQ in the CONC column of measurements, tagged as OBSERVATION. In the “Task>NCA>Run” tab, the user can choose how to handle the BLQ data. For the BLQ data before and after the Tmax, the BLQ data can be considered as missing (as if this data set row would not exist), or replaced by zero (default before Tmax), the LOQ value or the LOQ value divided by 2 (default after Tmax). In the “Check lambda_z” tab, the BLQ data are shown in green and displayed according to the replacement value. For the CA analysis, the replacement value for all BLQ can be chosen in the settings of the “Run” tab (default is Missing). In the “Check init.” tab, the BLQ are again displayed in green, at the LOQ value (irrespective of the chosen method for the calculations). # Urine data To work with urine data, it is necessary to record the time and amount administered, the volume of urine collected for each time interval, the start and end time of the intervals and the drug concentration in a urine sample of each interval. The time intervals must be continuous (no gaps allowed). In PKanalix, the start and end times of the intervals are recorded in a single column, tagged as TIME column-type. In this way, the end time of an interval automatically acts as start time for the next interval. The concentrations are recorded in the OBSERVATION column. The volume column must be tagged as REGRESSOR column type. This general column-type of MonolixSuite data sets allows to easily transition to the other applications of the Suite. As several REGRESSOR columns are allowed, the user can select which REGRESSOR column should be used as volume. The concentration and volume measured for the interval [t1,t2] are noted on the t2 line. The volume value on the dose line is meaningless, but it cannot be a dot. We thus recommend to set it to zero. A typical urine data set has the following structure. A dose of 150 ng has been administered at time 0. The first sampling interval spans from the dose at time 0 to 4h post-dose. During this time, 410 mL of urine have been collected. In this sample, the drug concentration is 112 ng/mL. The second interval spans from 4h to 8h, the collected urine volume is 280 mL and its concentration is 92 ng/mL. The third interval is marked on the figure: 390mL of uring have been collected from 8h to 12h. The given data is used to calculate the intervals midpoints, and the excretion rates for each interval. This information is then used to calculate the $$\lambda_z$$ and calculate urine-specific parameters. In “Tasks/Check lambda_z”, we display the midpoints and excretion rates. However, in the “Plots>Data viewer”, we display the measured concentrations at the end time of the interval. Example: • demo project_urine.pkx: urine PK dataset In this urine PK data set, we record the consecutive time intervals in the “TIME” column tagged as TIME. The collected volumes and measured concentration are in the columns “VOL” and “CONC”, respectively tagged as REGRESSOR and OBSERVATION. Note that the volume and concentration are indicated on the line of the interval end time. The volume on the first line (start time of the first interval, as well as dose line) is set to zero as it must be a double. This value will not be used in the calculations. Once the dataset is accepted, the observation type must be set to “urine” and the regressor column corresponding to the volume defined. In “Tasks>Check lambda_z”, the excretion rate are plotted on the midpoints time for each individual. The choice of the lambda_z works as usual. Once the NCA task has run, urine-specific PK parameters are displayed in the “Results” tab. # Occasions (“Sort” variables) The main sort level are the individuals indicated in the ID column. Additional sort levels can be encoded using one or several OCCASION column(s). OCCASION columns contain integer values that permit to distinguish different time periods for a given individual. The time values can restart at zero or continue when switching from one occasion to the next one. The variables differing between periods, such as the treatment for a crossover study, are tagged as CATEGORICAL or CONTINUOUS COVARIATES (see below). The NCA and CA calculations will be performed on each ID-OCCASION combination. Each occasion is considered independent of the other occasions (i.e a washout is applied between each occasion). Note: occasions columns encoding the sort variables as integers can easily be added to an existing data set using Excel or R. With R, the “OCC” column can be added to an existing “data” data frame with a column “TREAT” using data$OCC <- ifelse(data\$TREAT=="ref", 1, 2).
With Excel, assuming the sort variable is encoded in the column E with values “ref” and “test”, type =IF(E2="ref",1,2) to generate the first value of the “OCC” column and then propagate to the entire column:

Examples:

• demo project_occasions1.pkx: crossover study with two treatments

The subject column is tagged as ID, the treatment column as CATEGORICAL COVARIATE and an additional column encoding the two periods with integers “1” and “2” as OCCASION column.

In the “Check lambda_z” (for the NCA) and the “Check init.” (for CA), each occasion of each individual is displayed. The syntax “1#2” indicates individual 1, occasion 2, according to the values defined in the ID and OCCASION columns.

In the “Individual estimates” output tables, the first columns indicate the ID and OCCASION (reusing the data set headers). The covariates are indicated at the end of the table. Note that it is possible to sort the table by any column, including, ID, OCCASION and COVARIATES.

The OCCASION values are available in the plots for stratification, in addition to possible CATEGORICAL or CONTINUOUS COVARIATES (here “TREAT”).

• demo project_occasions2.pkx: study with two treatments and with/without food

In this example, we have three sorting variables: ID, TREAT and FOOD. The TREAT and FOOD columns are duplicated: once with strings to be used as CATEGORICAL COVARIATE (TREAT and FOOD) and once with integers to be used as OCCASION (occT and occF).

In the individual parameters tables and plots, three levels are visible. In the “Individual parameters vs covariates”, we can plot Cmax versus FOOD, split by TREAT for instance (Cmax versus TREAT split by FOOD is also possible).

# Covariates (“Carry” variables)

Individual information that need to be carried over to output tables and plots must be tagged as CATEGORICAL or CONTINUOUS COVARIATES. Categorical covariates define variables with a few categories, such as treatment or sex, and are encoded as strings. Continuous covariates define variables on a continuous scale, such as weight or age, and are encoded as numbers. Covariates will not automatically be used as “Sort” variables. A dedicated OCCASION column is necessary (see above).

Covariates will automatically appear in the output tables. Plots of estimated NCA and/or CA parameters versus covariate values will also be generated. In addition, covariates can be used to stratify (split, color or filter) any plot. Statistics about the covariates distributions are available in table format in “Results > Cov. stat.” and in graphical format in “Plots > Covariate viewer”.

Note: It is preferable to avoid spaces and special characters (stars, etc) in the strings for the categories of the categorical covariates. Underscores are allowed.

Example:

• demo project_covariates.pkx

In this data set, “SEX” is tagged as CATEGORICAL COVARIATE and “WEIGHT” as CONTINUOUS COVARIATE.

The “cov stat” table permits to see a few statistics of the covariate values in the data set. In the plot “Covariate viewer”, we see that the distribution of weight is similar for males and females.

After running the NCA and CA tasks, both covariates appear in the table of individual estimated parameters estimated.

In the plot “parameters versus covariates”, the parameters values are plotted as scatter plot with the parameter value (here Cmax and AUCINF_pred) on on y-axis and the weight value on the x-axis, and as boxplots for sex female and sex male.

All plots can be stratified using the covariates. For instance, the “Data viewer” can be colored by weight after having created 3 weight groups. Below we also show the plot “Distribution of the parameters” split by sex with selection of the AUCINF_pred parameter.

# Other useful column-types

In addition to the typical cases presented above, we briefly present a few additional column-types that may be convenient.

• ignoring a line: with the IGNORED OBSERVATION (ignores the measurement only) or IGNORED LINE (ignores the entire line, including regressor and dose information)
• working with several types of observations: different type of observations can be recorded in the same dataset (for instance parent and metabolite concentrations). They are distinguished using the OBSERVATION ID column-type. One of the observation ids is then selected in the “Tasks>Run” section to perform the calculations.
• working with several types of administrations: a single data set can contain different types of administrations, for instance IV bolus and extravascular, distinguished using the ADMINISTRATION ID column-type. The setting “administration type” in “Tasks>Run” can be chosen separately for each administration id, and the appropriate parameter calculations will be performed.

# Overview of all column-types

Column-types used for all types of lines:

Column-types used for response-lines:

Column-types used for dose-lines:

# Excel macro to adapt data format for PKanalix

We provide an Excel macro that can be used to adapt the formatting of your dataset for use in PKanalix. The macro takes as input the original dataset, in Excel or text format, and the dosing information separately and produces an adapted dataset with the doses in a new column. The macro can also translate information on missing observations, censored observations, urine volume, and occasions into the PKanalix-compatible format.