46 E. Russek-Cohen et al.
the opportunity to superimpose measurement error to each observation.
Within the regulatory arena, specific recommen- dations have been forwarded with regard to as-
say performance [38]
. For example, the guidance specifies that the error and the CV should not ex-
ceed 15, except at the lower limit of quantifica- tion, where error and CV should not exceed 20.
Based upon these recommendations, the program includes an option to define confidence limits about
the ratio of the true versus observed values. The coding for this is as follows:
1. measerr = 0: this implies that no measurement
error will be added to the simulated drug con- centrations.
2. measerr = 1: this implies that a multiplicative
measurement error is added to each simulated drug concentration.
To have a 99 chance of obtaining ‘‘measured’’ values that fall between 80 and 125 of the true
concentration, the coding would be as follows: 99 chance of being within 80 and 125 of the
true concentration; ptail = 1 − 1 − .992;
zvalue = probitptail; logtop = log1.25;
note ln.8 = −logtop.
To specify greater error e.g., 80 chance of falling within the limits of 70 and 143, the coding
would be modified as follows: 80 chance of being within 70 and 143 of the
true concentration; ptail = 1 − 1 − .802;
zvalue = probitptail; logtop = log1.43;
note ln.7 = −logtop.
Alternatively, the user can modify this portion of the program to describe any type of error model
deemed appropriate. For example, one can employ the more complex error models e.g., quadratic
equations such as those described by Jelliffe et al. for the gentamycin emit assay
[39] . The char-
acteristics of the error pattern become particularly important when simulating the use of sparse blood
samples, as may occur when studying fish or other small animal species. In these cases, where one or
two samples represent the total information ob- tained on a particular subject, ignoring the pres-
ence of a nonlinear error pattern such as that asso- ciated with the emit assay can result in the genera-
tion of drug concentrations that incorrectly suggest the presence of multimodal distributions
[40] .
3.7. Setting the lower limit of quantification
Validated analytical methods specify an LOQ be- low which the quantification of drug concentrations
is deemed unreliable. Generally, CVM recommends that for single dose pharmacokinetic investigations,
the AUC estimate should not extend beyond the last blood sampling time associated with drug concen-
trations at or above the LOQ AUC
0—last
. Therefore, this program is designed to allow the user to spec-
ify an LOQ coded as ‘‘loquant’’ that serves as the boundary at which AUC estimates are truncated
i.e., for generating AUC
0—last
. The use of this trun- cation can be valuable for determining the fre-
quency and duration of blood samples, allowing the investigator to explore the percentage of the popu-
lation that will have quantifiable concentrations out to the specified sampling time. Alternatively, the
program allows for drug concentrations to be fac- tored into the AUC estimate as some specified value
when the simulated concentrations drop below the LOQ. This option is coded as ‘‘lowlim’’. If selected,
all concentrations falling below the ‘‘lowlim’’ are read as the concentration termed ‘‘lowlimv’’. For
example, if the user wishes all concentrations that fall below the limit of 5 gmL to be factored into
the AUC estimate as 1 gmL, the coding would be as follows:
lowlim = 5; lowlimv= 1;
The latter function should not be used when sim- ulating bolus administrations due to the error that
can be introduced into the AUC estimates i.e., profiles may be artificially extended well beyond
the time when concentrations truly approach zero. However, this function may be valuable when sim-
ulating conditions resulting in multiple peaks and troughs e.g., when using the random input func-
tion. By allowing for multiple simulations using the same seeds and parameter estimates, the investiga-
tor can determine ways to optimize blood sampling times and to minimize the bias associated with esti-
mating AUC values when some samples drop below the LOQ.
3.8. Number of replications
