Interpolating concentration at the the start of an interval for a multiple doses study

[Azenox]

Hi everyone

I'm doing a NCA for a MAD study using both Winnonlin and PKNCA

One of the subjects is missing a concentration at the beginning of the last interval at steady state (predose), Winnonlin estimates a concentration at the start of the interval (time of dosing) and computes the parameters accordingly

PKNCA request a concentration at the start of the interval for AUC ( Requesting an AUC range starting (0) before the first measurement (1) is not allowed ), in this case I have tried to use the imputation function, and calculate the auc.int instead:

library(PKNCA)
df <- data.frame(time=c(312, 313, 313.5, 314, 314.5, 315, 316, 318, 320, 322, 326, 336),
                conc=c(NA, 9.69, 10.2, 10, 10.1, 10.1, 10.6, 10.9,  9.92,  8.22,  7.73,  6.39),
                subject=1)

conc_obj <- PKNCAconc(as.data.frame(df), conc ~ time)


intervals <- data.frame(start = 312, end = 336,
                        aucinf.obs=TRUE,
                        half.life = TRUE,
                        lambda.z = TRUE,
                        auclast=TRUE,
                        aucint.inf.obs =TRUE,
                        aucint.all =TRUE,
                        aucint.last =TRUE,
                        impute = "start_cmin, start_conc0"
)

#Using PKNCA package
PKNCA.options(auc.method = "lin up/log down")
data_obj <- PKNCAdata(conc_obj, intervals = intervals, impute="impute")
results_obj <- pk.nca(data_obj)
results <- as.data.frame(results_obj)

I don't get the same results as winnonlin naturally, I tried the PKNCA::interpolate.conc function to calculate a concentration at 312h, it either returns 0, or if I include the concentration immediately before (at 290.5h) I have an estimate that is slightly higher than what I would expect, and again the AUC parameters don't match those from winnonlin

For the record with Winnonlin I get auc.inf = 552.4472 and auc.last = 201.1562 for this subject

Any clue on the best way to proceed to get the same results without removing the interval between the dose and first concentration?

Thank you

[Azenox]

Well apparenly it was as simple as Wiinonlin using Cmin for first concentration

Is there a way to automate the pk.calc.c0.method.cmin() function in PKNCA, so the interpolation is only done when there is a missing data at the beginning of the interval?

[billdenney]

You found the correct answer which is using the impute argument to PKNCAdata().

For automating it, do you mean that you would like to have PKNCA impute using cmin for any multiple dose data? If so, that is a planned feature (likely available before the end of the year). More generally, the plan is to have PKNCA support interval definition sets that indicate parameters and imputation methods which should cover this (#157). If that isn't what you mean, can you please clarify the goal?

[Azenox]

Hi Bill, thank you for replying, and yes a feature like that would be great for multiple doses/ steady state, right now figuring out the right parameters takes a bit of time when you're doing it for the first time, adding definitions would simplify the process

In my case the issue is that I had an NA value at the start of the interval, so the start_cmin imputation method wasn't working, is that on purpose? should the data be cleaned of NA values before NCA?

#Imputation doesn't work, auc is not calculated:
df <- data.frame(time=c(312, 313, 313.5, 314, 314.5, 315, 316, 318, 320, 322, 326, 336),
conc=c(NA, 9.69, 10.2, 10, 10.1, 10.1, 10.6, 10.9, 9.92, 8.22, 7.73, 6.39),
subject=1)
#Imputation works, Cmin is added 312h:
df <- data.frame(time=c( 313, 313.5, 314, 314.5, 315, 316, 318, 320, 322, 326, 336),
conc=c(9.69, 10.2, 10, 10.1, 10.1, 10.6, 10.9, 9.92, 8.22, 7.73, 6.39),
subject=1)