BEVACIZUMAB EFFECT ON TOPOTECAN PHARMACOKINETICS ...

Eq. 6
Eq. 7
The variables X1, X2, X3, X4, X5 and X6 are to the drug amounts in the central,
absorption, peripheral, two transient compartments and tumor compartments; Ke is
systemic elimination rate constant; Kcp and Kpc are intercompartment rate constants
between peripheral and central compartments; Kct is elimination rate constant from
central compartment to tumor compartment; Kte is elimination rate constant leaving from
tumor compartment; Vc is volume of distribution in central compartment and Vt is
volume of distribution in tumor compartment.
2.2.8 Population pharmacokinetic analysis
Non linear mixed effects modeling using the MLEM algorithm implemented in
ADAPT 5 [181] was used to evaluate the TPT plasma and tumor ECF concentrations. In
the MLEM algorithm, ML is combined with an EM algorithm. The EM algorithm
consists of two steps. In the first step, each individual’s parameters are estimated using
the latest predicted parameter values and the observed data. In the second step, parameter
values are updated to maximize the log-likelihood function in the first step. These two
steps are then iterated until convergence. The initial values for population means and
population covariance matrix (inter-individual variability) were estimated by naïve
pooled analysis. Both population and individual estimates ware determined. The
model-fitted curve for each mouse was used to estimate the area under the
concentration-time curve from time zero to 6 hr in plasma (AUCp) and tumor ECF
(AUCt). The measures of penetration were expressed as AUC tumor-to-plasma ratio
(AUCt/AUCp).
2.2.9 Covariate analysis
A diagnostic screening was done to identify covariates such as the presence of
BEV in the treatment and the different schedules of the combination therapy that
potentially affected TPT PK parameters, including systemic elimination rate Ke,
elimination rate from tumor compartment Kte and volume of distribution in tumor
compartment Vt. These potential covariates were then included in the non-linear
mixed-effects population model to investigate their ability to significantly improve the
model fit (by a reduction of at least 3.84 [p