This page presents the rules applied to pre-process the data set and the calculation rules applied for the NCA analysis.

• Data processing
  • Ignored data
  • Forbidden situations
  • Additional points
  • BLQ data
  • Steady-state data
  • Urine data
• Calculation rules
  • Lambda_z
  • AUC calculation
  • Interpolation for partial AUC
  • Calculating steady-state parameters after each dose
  • Ratios for NCA parameters

 

Data processing

Ignored data

All observation points occurring before the last dose recorded for each individual are excluded. Observation points occurring at the same time as the last dose are kept, irrespective of their position in the data set file.

Note that for plasma data, negative or zero concentrations are not excluded.

Forbidden situations

For plasma data, mandatory columns are ID, TIME, OBSERVATION, and AMOUNT. For urine data, mandatory columns are ID, TIME, OBSERVATION, AMOUNT and one REGRESSOR (to define the volume).

Two observations at the same time point will generate an error.

For urine data, negative or null volumes and negative observations generate an error.

Additional points

For plasma data, if an individual has no observation at dose time, a value is added:

• Extravascular and Infusion data: For single dose data, a concentration of zero. For steady-state, the minimum value observed during the dosing interval.
• IV Bolus data: the concentration at dose time (C0) is extrapolated using a log-linear regression (i.e log(concentration) versus time) with uniform weight of first two data points. In the following cases, C0 is taken to be the first observed measurement instead (can be zero or negative):
  • one of the two observations is zero
  • the regression yields a slope >= 0

BLQ data

Measurements marked as BLQ data with a “1” in the CENSORING column will be replaced by zero, the LOQ value or the LOQ value divided by 2, or considered as missing (i.e excluded) depending on the setting chosen. They are then handled as any other measurement. The LOQ value is indicated in the OBSERVATION column of the data set.

Steady-state

Steady-state is indicated using the STEADY-STATE and INTERDOSE INTERVAL column-types. Equal dosing intervals are assumed. Observation points occurring after the dose time + interdose interval are excluded for Cmin and Cmax, but not for lambda_z. Dedicated parameters are computed such as the AUC in the interdose interval, and some specific formula should be considered for the clearance and the volume for example. More details can be found here.

Urine

Urine data is assumed to be single-dose, irrespective of the presence of a STEADY-STATE column. For the NCA analysis, the data is not used directly. Instead the intervals midpoints and the excretion rate for each interval (amount eliminated per unit of time) are calculated and used:

\( \textrm{midpoint} = \frac{\textrm{start time } + \textrm{ end time}}{2} \)

\( \textrm{excretion rate} = \frac{\textrm{concentration } \times \textrm{ volume}}{\textrm{end time } – \textrm{ start time}} \)

 

Calculation rules

Lambda_z

PKanalix tries to estimate the slope of the terminal elimination phase, called \( \lambda_z \), as well as the intercept called Lambda_z_intercept. \( \lambda_z \) is calculated via a linear regression between Y=log(concentrations) and the X=time. Several weightings are available for the regression: uniform, \( 1/Y\) and \(1/Y^2\).

Zero and negative concentrations are excluded from the regression (but not from the NCA parameter calculations). The number of points included in the linear regression can be chosen via the “Main rule” setting. In addition, the user can define specific points to include or exclude for each individual (see Check lambda_z page for details). When one of the automatic “main rules” is used, points prior to Cmax, and the point at Cmax for non-bolus models are never included. Those points can however be included manually by the user. If \( \lambda_z \) can be estimated, NCA parameters will be extrapolated to infinity.

R2 rule: the regression is done with the three last points, then four last points, then five last points, etc. If the R2 for n points is larger than or equal to the R2 for (n-1) points – 0.0001, then the R2 value for n points is used.  Additional constrains of the measurements included in the \( \lambda_z \) calculation can be set using the “maximum number of points” and “minimum time” settings. If strictly less than 3 points are available for the regression or if the calculated slope is positive, the \( \lambda_z \) calculation fails.

Adjusted R2 rule: the regression is done with the three last points, then four last points, then five last points, etc. For each regression the adjusted R2 is calculated as:

\( \textrm{Adjusted R2} = 1 – \frac{(1-R^2)\times (n-1)}{(n-2)} \)

with (n) the number of data points included and (R^2) the square of the correlation coefficient.
If the adjusted R2 for n points is larger than or equal to the adjusted R2 for (n-1) points – 0.0001, then the adjusted R2 value for n points is used. Additional constrains of the measurements included in the \( \lambda_z \) calculation can be set using the “maximum number of points” and “minimum time” settings. If strictly less than 3 points are available for the regression or if the calculated slope is positive, the \( \lambda_z \) calculation fails.

Interval: strictly positive concentrations within the given time interval are used to calculate \( \lambda_z \). Points on the interval bounds are included. Semi-open intervals can be defined using +/- infinity.

Points: the n last points are used to calculate \( \lambda_z \). Negative and zero concentrations are excluded after the selection of the n last points. As a consequence, some individuals may have less than n points used.

 

AUC calculation

 

The following linear and logarithmic rule apply to calculate the AUC and AUMC over an interval [t1, t2] where the measured concentrations are C1 and C2. The total AUC is the sum of the AUC calculated on each interval. If the logarithmic AUC rule fails in an interval because C1 or C2 are null or negative, then the linear interpolation rule will apply for that interval.

Linear formula: 

\( AUC |_{t_1}^{t_2} = (t_2-t_1) \times \frac{C_1+C_2}{2}  \)

\( AUMC |_{t_1}^{t_2} = (t_2-t_1) \times \frac{t_1 \times C_1+ t_2 \times C_2}{2}  \)

Logarithmic formula:

\( AUC |_{t_1}^{t_2} = (t_2-t_1) \times \frac{C_2 – C_1}{\ln(\frac{C_2}{C_1})}  \)

\( AUMC |_{t_1}^{t_2} = (t_2-t_1) \times \frac{t_2 \times C_2 – t_1 \times C_1}{\ln(\frac{C_2}{C_1})}  – (t_2-t_1)^2 \times \frac{C_2 – C_1}{\ln(\frac{C_2}{C_1})^2}  \)

 

Interpolation formula for partial AUC

When a partial AUC is requested at time points not included is the original data set, it is necessary to add an additional measurement point. Those additional time points can be before or after the last observed data point.

Note that the partial AUC is not computed if a bound of the interval falls before the dosing time.

 

Additional point before last observed data point

Depending on the choice of the “Integral method” setting, this can be done using a linear or log formula to find the added concentration C* at requested time t*, given that the previous and following measurements are C1 at t1 and C2 at t2.

Linear interpolation formula:     

\( C^* = C_1 + \left| \frac{t^*-t_1}{t_2-t_1} \right| \times (C_2-C_1) \)

Logarithmic interpolation formula:     

\( C^* = \exp \left( \ln(C_1) + \left| \frac{t^*-t_1}{t_2-t_1} \right| \times (\ln(C_2)-\ln(C_1))  \right) \)

If the logarithmic interpolation rule fails in an interval because C1 or C2 are null or negative, then the linear interpolation rule will apply for that interval.

 

Additional point after last observed data point

If \( \lambda_z \) is not estimable, the partial area will not be calculated. Otherwise, \( \lambda_z \) is used to calculate the additional concentration C*:

\( C^* = \exp(\textrm{Lambda_z_intercept} – \lambda_z \times t) \)

 

Calculating steady-state parameters after each dose

Since version 2024R1, steady state parameters can be calculated after each dose without the dataset requiring the steady state information, meaning that the interdoseinterval column no longer necessarily has to be accompanied by a steady state column.

We distinguish between 3 different dataset types. The dataset contains:

1. Multidose profiles/ multidose profiles per occasion only: The interdose interval is included in the data set for each dose administration. This means that both single dose parameters and steady-state parameters are listed in the NCA task tab in the NCA – BE parameters panel
2. Single dose profiles/ single dose profiles per occasion only: To be able to calculate steady state parameters, an interdose interval \tau must be indicated. Within the NCA tasks tab, a uniform \tau can be defined for all profiles. Enabling the “Interdose interval for single dose profiles” checkbox triggers the field for defining \( \tau \). By default, the suggested value is based on the last time recorded for the first subject. This action leads to the inclusion of steady state parameters within the list of NCA parameters.
3. Single dose profile/single dose profile per occasion & multidose Profile/ Multidose profile per Occasion: Since the dataset includes information about interdose intervals for multidose profiles, both single dose and steady-state parameters are displayed in the NCA – BE parameters panel.When the “Interdose interval for single dose profiles” checkbox is selected and an interdose interval \( \tau \) is specified for the single dose profiles, steady-state parameters can also be computed for them.
   If the “Interdose interval for single dose profiles” checkbox has NOT been ticked and therefore no \( \tau \) has been set, the steady-state parameters only refer to the multidose profiles and are therefore only calculated for these. Single dose profiles obtain NaN in the result table for steady-state parameters.

 

Some parameters have identical names but are computed using different formulas, contingent upon whether it pertains to a single dose profile or a multiple dose profile:

Name	Formula/Description – SD	Formula/Description – SS
C0	Concentration at dosing time. If profile doesn’t contain obs at dosing time: Extravascular or Infusion: C0 = 0. For IV bolus: log-linear regression of first two data points to back-extrapolate C0.	Minimum observed during the dose interval.
Cmax	Maximum observed concentration, occurring at Tmax. If not unique, then the first maximum is used.	Maximum observed concentration between dose time and dose time + TAU.
Cmax_D	Cmax/Dose	Cmax (based on SS)/Dose
Tmax	Time of maximum observed concentration; entire curve is considered.	Tmax corresponds to points collected during a dosing interval.
MRTINF_obs	Intravascular: MRTINF_obs = AUMCINF_obs/AUCINF_obs – TI/2; TI $\widehat{=}$infusion duration Extravascular: MRTINF_obs = AUMCINF_obs/AUCINF_obs → AUMCINF_obs & AUCINF_obs based on Clast_obs.	Mean Residence Time extrapolated to infinity using predicted Clast, calculated using AUC_TAU.
MRTINF_pred	Same formulas as above but based on AUMCINF_pred & AUCINF_pred based on Clast_pred.
Vss_obs	Vss_obs=MRTINF_obs * Cl_obs with MRTINF_obs for SD profiles	Vss_obs=MRTINF_obs * Cl_obs with MRTINF_obs for SS profiles
Vss_pred	Vss_pred=MRTINF_pred * Cl_pred with MRTINF_pred for SD profiles	Vss_pred=MRTINF_pred * Cl_pred with MRTINF_pred for SS profiles

 

These parameters are distinguished by the postfix “_SS” in the list of parameters and in the results table. This extension is also echoed in the aliases by the postfix “, SS”.

Typically, parameters that share the same name are calculated differently based on whether the “Interdose interval for single dose profiles” checkbox is selected. However, there’s one exception: parameter C₀. In cases where both single dose and multiple dose profiles occur together, the order of profiles influences C₀‘s calculation. If the first profile is a single dose, followed by either more single dose profiles or multiple dose profiles, and the checkbox is enabled, C₀ for the first occasion follows the single dose formula, while subsequent C₀‘s follow the steady-state formula.

Ratios for NCA parameters

Starting from PKanalix version 2024R1 and above, ratios for each individual of NCA metrics along occasions can now be calculated. In the dedicated section “Ratios NCA” in the NCA tasks tab, ratios can be defined by clicking on the plus sign (red frame). This action prompts a window to appear, allowing you to specify the ratio in greater detail.

The definition is organised in several steps:

• Assignment of a name for the new ratio parameter
• Selection of the stratification variable. Variables tagged in the dataset either as occasion or as categorical variable are available for selection
• Selection of the comparator (specific modality in Occasion or covariate vriable)
• Selection of test modality (specific modality in Occasion or covariate variable)
• Selection of the NCA parameter

The formula for the specified ratio is displayed in the blue panel beneath the definition options.

Ratios can only be created using parameters that are also calculated for the NCA.

If there are subjects who have multiple values for one of the test or comparison modality, the arithmetic mean of the values for this modality is calculated.

If there are subjects without a parameter value for one of the test or comparison modalities, no ratio can be calculated for this parameter. Consequently, the subject obtains NaN as a result.

The computed parameter ratios are presented in two distinct results tables within the results tab. One table provides an individual listing, while the other offers a summary table with basic descriptive statistical metrics.