Methods
Study design
The PBPK modeling was conducted in PK-Sim® (version 11.0, part of the
Open Systems Pharmacology suite), which has a system- and drug-dependent
component. The simulated trials were conducted in virtual healthy adult
and pediatric population groups. For adults, healthy volunteers between
18 and 60 years were selected. Virtual pediatric subjects were aged 0-18
years and divided into 5 groups: 0-1 years, 1-3 years, 5-8 years,
8-12years, 12-18 years. All simulations were performed with virtual
population of 100 individuals. The proportion of female was the default
0.5 in each simulation. Both p.o. and i.v. administration of VRCZ were
simulated in healthy adults and pediatrics.
PBPK model establishment and validation of TAC
General physiochemical properties (molecular weight, LogP, compound
type, pKa) of TAC and in vitro data from blood, plasma protein were
utilized for building the PBPK model [13-15]. System-dependent
physiological parameters (organ volumes, blood flow rates, hematocrit,
etc.) were provided in PK‐Sim® with the small molecule model. The input
parameters for specific intestinal permeability were optimized using the
PK profiles of single 5mg TAC in healthy subjects. According to the
clinical routine drug regimens, a simulation of TAC was performed at a
single dosage of 2 mg for adults. The predictive performance was
evaluated by visually comparing predicted concentration-time data with
the observed data from the literature for initial verification
[16]. Predicted concentration-time profiles were obtained using the
developed PBPK model of TAC. Observed concentration–time data were
obtained in graphical form using GetData Graph Digitizer version
2.25.0.32. The ratio of predicted to observed pharmacokinetics (PK)
values was used to evaluate model performance. Next, the quantitative
assessment was conducted by calculating the mean fold error (MFE) of PK
parameters such as the area under the plasma concentration-time curve
(AUC) and maximum concentrations (Cmax), expressed as the ratio of
predicted to observed mean values. The model was acceptable if it met
the 0.5- to 2.0-fold limit. After model validation, the adult PBPK model
was scaled to children (pediatric PBPK model). Drug-specific parameters
defined in the adult PK data were kept constant for the pediatric PBPK
model. The physiological parameter values for pediatric individuals were
taken from the PK-Sim population database. The PBPK model performance in
pediatrics was evaluated using the quantitative verification described
in adult model verification[17].
PBPK model establishment and validation of VRCZ
Drug-specific physicochemical properties were obtained from the
literature [18, 19]. Organ-plasma partition coefficients were
determined using Poulin and Theil’s method based on the literature
[20]. The model was verified with clinical data of adults and
pediatrics in different formulations[21, 22]. The PBPK model
performance in children was evaluated using the quantitative
verification described in TAC model verification.
DDI simulations of TAC and VRCZ in adults and pediatrics
The DDI of TAC combined with VRCZ in different formulations and
different dosing regimens were simulated in adults and pediatrics.
Verified PBPK models were used to predict the DDI using PK-Sim®. The
dosage and dosing interval of TAC and VRCZ were prescribed on the basis
of the clinical routine drug regimens. The detailed dosage regimens are
shown in Table 1. DDI simulations were performed in turn following these
dosing schedules.
Sensitivity Analysis
Sensitivity of the DDI between TAC and oral VRCZ to single parameters
(local sensitivity analysis) was calculated as relative change of AUC
using the Sensitivity Analysis tool implemented in PK-Sim®. Sensitivity
analysis was performed applying a relative perturbation of 1000 %
(variation range 10.0, maximum number of 9 steps). Parameters selected
for the sensitivity analysis fulfilled one of the
following criteria: (1) optimized; (2) related to optimized parameters;
(3) a strong influence in the model. Sensitivity to a parameter was
calculated as the ratio of the relative change of the simulated AUC to
the relative variation of the parameter around its value used in the
final model according to the Eq. (1):
\begin{equation}
S=\frac{AUC}{\text{AUC}}\bullet\frac{P}{P}\nonumber \\
\end{equation}where S = sensitivity of the simulated AUC0–24 to the examined model
parameter value, ∆AUC= change of the simulated AUC0–24, AUC = simulated
AUC0–24 with the original parameter value, ∆p = change of the examined
parameter value, p = original parameter value. A sensitivity value of
+1.0 means that a 10% change in the examined parameter causes a 10%
alteration of the predicted AUC.