Development and Evaluation of a New Efficiency Tool (SADAPT-TRAN) for Model Creation, Debugging, Evaluation, and Automated Plotting using Parallelized S-ADAPT, Perl and R
Jürgen B. Bulitta (1), Ayhan Bingölbali (1), Cornelia B. Landersdorfer (1)
(1) Ordway Research Institute, Albany, NY
Objectives: 1) To develop an efficiency tool (SADAPT-TRAN) as an add-on for S-ADAPT that greatly facilitates nonlinear mixed-effects modelling and provides fully automated diagnostic plots and summary tables using parallelized S-ADAPT, Perl, and R. 2) To evaluate the standard settings of SADAPT-TRAN with regard to estimation by the Monte Carlo Parametric Expectation Maximization (MC-PEM) algorithm.
Methods: We developed Perl scripts to translate the core components of pharmacokinetic / pharmacodynamic (PK/PD) models into Fortran code for S-ADAPT (v 1.56). The standard settings of SADAPT-TRAN were evaluated via simulation estimation studies using nine population PK/PD models. These cases included two models for antibacterials, one covariate effect model with two patient groups, and one model with between occasion variability (BOV) on Vmax and Km of a sequential mixed-order plus first-order absorption model combined with a parallel Michaelis-Menten and linear elimination model. For each model, between 20 and 80 datasets were simulated in Berkeley Madonna (version 8.3.14). Datasets contained frequent sampling at three dose levels (usually 500, 2000, and 8000 mg; n=32 subjects each). Initial estimates were set 2-fold off for every population mean. Initials for the between subject variability were set to large values (100% CV for log-normally distributed parameters) and forced to be large during the first 20 iterations.
Results: The SADAPT-TRAN Perl scripts support automatic specification of Fortran code for S-ADAPT, do not restrict the flexibility of S-ADAPT or its scripting language, and account for covariate effects and BOV. Individual parameter estimates can be automatically constrained via a logistic transformation. Summary tables and diagnostic plots are fully automatically prepared over one or multiple models, multiple dependent variables, and continuous & categorical covariates. Bias was <10% for all population means and covariate effects for 8 of 9 population PK/PD models and <26% for the ninth model. The Km of the complex absorption model described above showed a larger bias. Bias for variance estimates was below 40% for the vast majority of parameters.
Conclusion: The SADAPT-TRAN Perl scripts greatly facilitated model specification, debugging, and evaluation both for experienced and beginner users of S-ADAPT. The standard settings of the SADAPT-TRAN package provided robust and largely unbiased estimates over a diverse series of population PK/PD models.