48 E. Russek-Cohen et al.
drug administered in food versus drinking water can be compared.
4. Random dosing schedule: The random aspect of this input function is that the user can specify
a given number of ‘‘doses’’ or ‘‘hits’’ during a specified time interval, and each ‘‘hit’’ can be
random both in terms of its time of occurrence and the amount administered. This can be useful
for simulating release from growth promoting implants, or drug delivery in feed and water.
For example, let us assume that the total dose totdose is 100.0 mg and that this dose is ad-
ministered once daily for three consecutive days or 33.3 mg per day nfractn = 3 dosing intervals,
dfrac
= totdosenfractn = 1003 = 33.3 mg, last- time
= 72 h, tfrac = lasttimenfractn = 723 = 24.
The dose received over each interval is ran- domly administered five times numimp = 5. A
uniform random number generator determines when during the 24 h dosing interval the five
inputs are administered. These times are then sorted. The amount of dose given at each of
the five times is determined by generating four numimp − 1 = 5 − 1 = 4 random numbers to set
the fraction of the 33.3 mg dose to be given. The fifth or last dose is the remainder of the
33.3 mg amount. In our current scenario, the dosing interval is
72 h lasttime = 72, which is divided into three daily doses nfractn = 3, causing tfrac = 24. The
user then specifies the number of random inputs numimp = 5 that occur within each period. So,
if numimp = 5 and nfractn = 3 then there are 15 total doses numimp × nfractn = 5 × 3 = 15
per animal. The total dose is 100 mg tot- dose
and the dose per period is 33.3 mg
dfrac = totdosenfractn = 1003 = 33.3. This is spelled it out within the routine termed
rantime
.
3.11. Sampling times
The sampling times for the test and reference prod- ucts are identical. Time designations are expressed
relative to the initial dose.
3.12. Test for product bioequivalence
The 90 confidence intervals about the ratio of treatment means are calculated for the AUC and
C
max
values estimated for the test and reference products. As currently written, AUC and C
max
values are transformed to a natural log scale prior to sta-
tistical analysis. The standard error of the estimate of the difference between treatment means is used
to calculate the 90 confidence interval. The pro- gram subtracts one from both L
t
and U
t
as defined in Eqs.
22 and
23 prior to assessing rejection cri-
teria. Rejection of bioequivalence is currently set as having confidence bounds that fall outside the
boundaries of −0.20 to +0.25, as seen by the cod- ing:
if U
t
.25 or L
t
−.2 then reject bioequivalence The program counts the number of rejections.
The codes are specified for AUC and C
max
sepa- rately. By modifying this code, the limits defining
product bioequivalence can be made as narrow or as broad as desired.
3.13. Output
If the number of replications is less than 10, the program prints a large volume of intermediate cal-
culations. When the number of replications are less than or equal to five, the concentration versus time
data for each study subject will also be printed. When rep ≤ 5, the output will also include the fol-
lowing information for each replication and treat- ment: actual versus expected means for the input
parameters, and mean and standard deviation for AUC, C
max
, and T
max
values for each treatment. This restriction can be modified as deemed appropriate
by the user. For additional manipulation of an existing SAS
dataset, whether created on the current or a previous run, the user can use the set com-
mand statement to import a previously generated
dataset or one created in a previous data step or procedure. By changing the libname, multiple
datasets can be stored simultaneously. If the user elects to save the dataset generated from multi-
ple runs of the program, the code instructing the program to delete previous output proc delete
data
= set.conct should be deleted and a new file name e.g., user.conct2 provided.
With each set of runs, the following output in- formation is provided:
1. Input parameters: a. number of replicates;
b. number of subjects per treatment; c. initial seeds;
d. compartmental model employed; e. dose information total dose, amount re-
ceived at each input time for fixed dosing schedules, nfractn, and numimp random
schedule; f. amount of dose received at each input time
for fixed dosing schedules; g. number of subpopulations;
A SASIML program for simulating pharmacokinetic data 49
Table 1
Example of the program’s summation of sim- ulation results
The means procedure Variable
Mean Sum
Std Error rejauc
0.9980000 998.0000000
0.0014135 rejcmax
1.0000000 1000.00
bothacc
h. simulated sampling times. 2. Treatment summary:
a. parameter correlation coefficients; b. computed parameter standard deviations;
c. covariance matrix; d. expected versus observed parameter means
and standard deviations. 3. Output summary:
a. mean concentration versus time profiles, mean and standard deviation for AUC, C
max
, and T
max
; b. proc univariate output for all parameters and
concentrations at each sampling time; c. schematic plots comparing treatment popu-
lation characteristics for the input parame- ters, drug concentrations at each sampling
time, and bioavailability parameters; d. to facilitate the evaluation of the results over
a large number of replicates, the number of trials and proportion of the total number of
replications that fail or succeed in meeting the bioequivalence criteria are summarized
at the end of the simulation output. A sample output summary is featured is
Table 1 .
Under the column titled ‘‘Mean’’, this table pro-
vides a synopsis of the fraction of the total number of replicates that failed to have confidence inter-
vals contained within the bounds defining equiva- lence with respect to AUC rejauc and C
max
rejc- max
. The proportion of simulated replicates when both AUC and C
max
are contained within the bioe-
quivalence criteria bothacc is also provided. The output also indicates the total number of replicates
that fall within each of these categories Sum, and the standard error about the mean ratio Std Error.
4. Typical sample runs of the program
