Bioequivalence and Bioavailability Forum

❝ ❝ Do you know more about the drug than its t_max?

❝ About the Drug - Since the study is a simulation-based experiment, I developed a hypothetical drug which is expected to have more colonic absorption to support my theoretical objective.

❝ I have pasted the Log-Conc profile of the drug for your reference below.

❝ I totally agree with you point regarding Cmax. But also I want to be realistic in PK sampling time points to clinical setting. I mean, no over-sampling. therefore, according to US FDA guidance "Bio­equivalence Studies With Pharmacokinetic End­points for Drugs Submitted Under an ANDA Guidance for Industry, 2021", I can go upto 18 time points.

-script

❝ In my case, I do not see flip-flop kinetics, therefore I will assume the Tmax = 8.4 h as reliable parameter and add further more (3-5) time points.

❝ I also would like to know, what are the criteria to choose the PK sampling time points as best as possible to capture the PK estimates well? (All I know to describe the ADE phase and at least three or more terminal elimination half-lives of the drug).

• Find a suitable PK model.
  
• Define different sampling schedules.
  
• With the parameters of the model, simulate the schedules.
  
• Inspect the variance inflation factors (the lower is better).
  
• Inspect plots of partial derivatives of parameters vs. time (optimal are sampling times close to the minima). See also this post for an example.

library(truncnorm)
roundClosest <- function(x, y) {     # Round x to the closest multiple of y
  return(y * round(x / y))
}
npSummary <- function(x) {           # Nonparametric summary of x
  y                <- summary(x)[-4] # remove the mean
  attr(y, "names") <- c("Min", "Q I", "Median", "Q III", "Max")
  return(y)
}
delta    <- 1                        # sampling every hour
tlag.exp <- 6                        # expected lag-time
tlag.min <- 2                        # fast gastric passage
tlag.max <- 72                       # maximum possible gastric passage
tlag.sd  <- 4                        # standard deviation (pretty large)
n        <- 1e3                      # number of simulations
set.seed(123456)                     # for reproducibility
tlag     <- rtruncnorm(n = n, a = tlag.min, b = tlag.max,
                       mean = tlag.exp, sd = tlag.sd) # continuous
tlag     <- roundClosest(tlag, delta)                 # discrete
npSummary(tlag)
hist(tlag, breaks = "FD", freq = FALSE, , xlim = c(0, npSummary(tlag)[["Max"]]),
     main = "", las = 1, xlab = expression(italic(t)[lag]))

Min    Q I Median  Q III    Max
  2      5      7      9     18