Population PK Analysis
PopPK was performed to provide model predicted individual pharmacokinetic (PK) parameter estimates for exposure-response (ER) analyses. Data from seven Phase 1 studies, four Phase 2 studies, and three Phase 3 studies were analyzed using nonlinear mixed-effects modeling [NONMEM (Version 7.3.0 or later; GloboMax, Hanover, MD)] and Perl‑Speaks‑NONMEM (PsN; Uppsala University, Sweden). Summary of the data used in the PopPK modeling is shown in supplementary Table 1.
Filgotinib and GS-829845 models were developed separately. Various structural models and random effect models were evaluated to reach the base models. Stepwise forward addition and backward deletion was implemented in the covariate model building process, with evaluated covariates including demographics (age, sex, body weight, race), pathophysiological factors [baseline estimated creatinine clearance (CLcr), baseline bilirubin, baseline alanine aminotransferase, baseline aspartate aminotransferase, RA disease status (subjects with RA vs healthy subjects), baseline C-reactive protein (CRP), and RA duration], and fed status (always fed vs mixed fasted/fed vs always fasting; evaluated on absorption-related parameters). Model selection was done based on a log-likelihood ratio test at an acceptance p-value of 0.01 (forward addition) or 0.001 (backward elimination). The difference in -2 times the log of the likelihood (-2LL) between a full and reduced model was assumed to have a χ2 asymptotic distribution with degrees of freedom equal to the difference in number of parameters between the 2 models. Model performance evaluation was based on Goodness-of-Fit evaluation, Prediction Corrected Visual Predictive Check (pcVPC), and Bootstrap Resampling Techniques. Individual PK parameter estimates were predicted from the final models for ER analyses.