Population pharmacokinetic modelling
The structural model was developed using 374 data of GBP plasma
concentration. It consisted of a one-compartment with the inclusion of
IIV on absorption constant (Ka), lag time, volume of distribution (Vd)
and clearance (CL). The PK profiles are shown in Figure 1A. A
correlation between the IIV of Vd and CL also improved the model. A
proportional error model was best to estimate the unexplained residual
variability rather than combined or additive error models. The estimates
of lag time, Ka, Vd and CL were 0.32 h, 1.13 h-1, 140
L and 14.7 L/h, respectively. The estimates of IIV expressed as RSE were
16.4% (lag time), 17.4% (Ka), 14.1% (Vd) and 13.4% (CL),
respectively (Table 2).
Covariates selection was based on variables showing parameter-covariates
relationship with p-value <0.05 (models No. 2-6, Table 3).
Forward inclusion ended with four covariates; the OCTN1 genotype was not
included because it resulted in high RSE (Table 3). Backward elimination
was performed on the full model obtained and included the following
covariates: a) eGFR on CL; b) body height and FGL on Vd. The addition of
body height on Vd improved the model. Interestingly, other covariates
with a strong correlation with height, such as BMI and weight, or
covariates with better clinical explanation, such as weight, did not
improve the model. Even though sex was included as a covariate on Ka on
the forward inclusion step, it was removed on the backward elimination
step.
The pharmacokinetic parameters of the final model and the bootstrap are
presented in Table 2. The final model showed good predictive performance
since RSE values were below 40%. The precision of the parameter
estimates evaluated through bootstrap analysis showed that the zero
value was not included in the 95% confidence intervals in any case. The
absence of bias in the goodness-of-fit plots presented in Figure 1B-C
illustrates the acceptable predictive value of the model.