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
Identical seeds were used for all simulations in the following examples so that the output can be iden-
tical except for changes specified by the coded pa- rameters.
4.1. One-compartment open body model
To demonstrate the data generation potential of this program, the various attributes are variants
of the output generated with a one-compartmental open body model.
Model specifications 1. Subject per treatment = 24.
2. Number of replications = 1000. 3. Total dose = 100 mg.
4. Reference product parameter mean values:
K
a
= 2 h
− 1
; V
c
= 1 Lkg; F = 0.50; K
el
= 0.327 h
− 1
. To demonstrate product inequivalence, the test
product parameter values are K
a
= 1.0 h
− 1
and F
= 0.35. 5. Correlation coefficients: The parameter output
for the test and reference products will be based upon the identical correlation matrix, where the
correlation coefficients for each contrast are set to 0.1. This minimizes the constraints about the
resulting parameter values. Clearly, in some sit- uations e.g., hepatic disease there can be con-
comitant changes in drug clearance and there- fore K
el
and volume of distribution as the con- centrations of serum albumin decrease
[43] . Al-
ternatively, there may be a relationship between K
a
and F, particularly if the drug is associated with a limited window of absorption within the
small intestine. In these cases, F and K
a
may be highly correlated
[44] . Consequently, the in-
vestigator needs to consider these physiological and pharmacokinetic properties when establish-
ing the correlation coefficients. 6. Variability estimates: The relative standard de-
viation of the reference product are as follow: K
a
= 0.25; V
c
= 0.15; F = 0.20; K
el
= 0.15. For the test product, the relative standard deviation are
K
a
= 0.35; V
c
= 0.15; F = 0.30; K
el
= 0.15. Parame- ters are normally distributed.
7. Measurement error: Under the basic simula- tion conditions, there was no assay error, no
pre-selected LOQ, and no subpopulations. Since the various dosing options have already been
demonstrated, all other simulations were con- ducted as a single bolus administration.
The mean concentrationtime profile for this simulation is shown in
Fig. 3 .
4.2. Two-compartment open body model
