Nonmem

Bayesian forecasting was performed using NONMEM version 7.4.3 (ICON Development Solutions, Ellicott City, MD), and Perl‐speaks‐NONMEM version 4.8.1 was used for NONMEM run control.37, 38 Data management and graphical analyses were performed using R 3.5.1 (http://www.rproject.org/).

Modeling was performed using a Dell Power Edge R910 server with 4 × 10 core Xeon 2.26 Ghz processors running Windows Server 2008 R2 Enterprise 64‐bit. Model development used nonlinear mixed effect modeling using NONMEM (version 7.3; ICON Development Solutions, Ellicott City, MD)¹⁰ with the Wings for NONMEM (version 7.3) interface (http://wfn.sourceforge.net) and IFort compiler. Processing NONMEM output and generating plots was conducted with R Software version 3.1.1 or later¹¹ using ggplot2, plyr, and scales packages¹², ¹³, ¹⁴ and associated dependencies.

To take the interindividual variability into account, model development was performed using nonlinear mixed effects modeling, and the software NONMEM (version 7.3 by ICON Development Solutions, Ellicott City, MD) with the ADVAN 13 subroutine was used.24 Estimations were made by maximizing the likelihood of the data, using the stochastic approximation expectation maximization algorithm followed by importance sampling to obtain the objective function value for hypothesis testing.
The simultaneous modeling of continuous and count data was permitted in NONMEM by the indication variable F_FLAG.24 The delay, representing the CTC lifespan, was implemented in NONMEM with the Absorption LAG (ALAG) parameter and was calculated with the method of steps that allowed virtually solving a delay differential equations system by transforming it into an ordinary differential equations system.25 (link) Gamma and factorial functions were calculated using the GAMLN function implemented in NONMEM 7.3.24 Finally, data handling and graphical representations were performed in R, using the PsN suite and the Xpose package.26 (link)–28

We performed the PPK analysis through nonlinear mixed-effects modeling with NONMEM^® (version 7, Level 2, Icon Development Solutions, Ellicott City, MD, USA). Subroutine ADVAN 2 and first-order conditional estimation with interaction option (FOCE-I) method were used in modeling. PsN (version 3.4.2, Uppsala University, Sweden) was applied for model construction and validation. The diagnostic plots of NONMEM^® outputs were conducted using R version 4.0.5 (http://www.r-project.org).