Population PK modelling
The identification of structural differences in the PK properties of
s.c. and i.d. administration, while accounting for covariates such as
the presence of anti-adalimumab antibodies, was investigated using a
population non-linear mixed effects modelling approach in NONMEM (ICON
plc, V7.3). Based on literature information, a one compartment
structural model with linear absorption and linear elimination was used
during model development (23). For this structural model, the effect of
anti-drug antibodies on the CL of adalimumab was tested as a
time-varying covariate, increasing the CL of adalimumab at higher titre
levels with the following equation: CL = ΘTVCL * (1+
ΘTVTitre-slope * TITRE), Where individual TITRE levels
proportionally increase the CL of an individual over time.
When a structural misspecification was identified in the absorption
phase, modifications to the absorption part of the model were explored,
in which transit models, different absorption compartments, and a MTIME
function in which the ka changes after an estimated time
point, were investigated, modelled separately for each administration
route.
After identification of the best structural absorption models for each
route of administration, log-transformed inter-individual variability
(IIV) was included following a forward inclusion procedure
(p<0.01) and covariates (age, weight, body mass index, sex,
serum creatinine, and albumin) were explored following a
forward-inclusion (p<0.01) with backward-elimination (p≤0.001)
procedure. Continuous covariates were tested following a power
relationship centered around the median. Models were evaluated on basis
of the objective function value (OFV), the parameter uncertainty (judged
by the relative standard error [RSE]), goodness-of-fit figures,
individual model predictions versus observations over time, and
confidence interval visual predictive checks (ciVPC) based on 500 Monte
Carlo simulations. Bootstrapping was not considered of added value as
additional model evaluation tool. Data transformation was performed in R
(V3.6.1(22)) and models were executed in conjunction with
Perl-speaks-NONMEM (V4.8.1) (24).