Development of a new pre- and post-processing tool (SADAPT-TRAN) for nonlinear mixed-effects modeling in S-ADAPT

Abstract

Mechanistic modeling greatly benefits from automated pre- and post-processing of model code and modeling results. While S-ADAPT provides many state-of-the-art parametric population estimation methods, its pre- and post-processing capabilities are limited. Our objective was to develop a fully automated, open-source pre- and post-processor for nonlinear mixed-effects modeling in S-ADAPT. We developed a new translator tool (SADAPT-TRAN) based on Perl scripts. These scripts (a) automatically translate the core model components into robust Fortran code, (b) perform extensive mutual error checks across all input files and the raw dataset, (c) extend the options of the Monte Carlo Parametric Expectation Maximization (MC-PEM) algorithm, and (d) improve the numerical robustness of the model code. The post-processing scripts automatically summarize the results of one or multiple models as tables and, by generating problem specific R scripts, provide an extended series of standard and covariate-stratified diagnostic plots. The SADAPT-TRAN package substantially improved the efficiency to specify, debug, and evaluate models and enhanced the flexibility of using the MC-PEM algorithm for parallelized estimation in S-ADAPT. The parameter variability model can take any combination of normally, log-normally, or logistically distributed parameters and the SADAPT-TRAN package can automatically generate the Fortran code required to specify between occasion variability. Extended estimation features are available to avoid local minima, estimate means with negligible variances, and estimate variances for fixed means. The SADAPT-TRAN package significantly facilitated model development in S-ADAPT, reduced model specification errors, and provided useful error messages for beginner and advanced users. This benefit was greatest for complex mechanistic models.

Fig. 1

Flowchart for model code, raw data, results, and plots as implemented in the translator, pre-processor, and post-processor scripts of SADAPT-TRAN that interact with S-ADAPT for nonlinear mixed-effects modeling and R for plotting. Programs and scripts are shown in circles, data files as boxes, and error checks or operations as arrows. Blue arrows denote operations by the SADAPT-TRAN translator and pre-processor, purple arrow denotes operations by S-ADAPT, dark red arrows denote operations by the post-processor and plotting scripts, and the green arrow refers to R

Fig. 2

Application of a variance burn phase to prevent convergence of the MC-PEM algorithm to a local minimum and of a variance burn then shrink phase to estimate a population mean with small or negligible final variance

Fig. 3

Steps of a population PK/PD modeling analysis using the streamlined procedure with SADAPT-TRAN compared to the traditional procedure in S-ADAPT. The text in purple describes steps that require user input

Fig. 4

Complete model code in SADAPT-TRAN format for a one compartment PK model with zero-order infusion [R(1)] combined with indirect response model I

Fig. 5

Command file (run.txt) to define the run-name (NFILE), delete all preexisting files, load the raw dataset and the covariate file, set the estimation options, initialize the Beowulf cluster for parallelized estimation, define the initial means, initial variances and parameter types, and include the estimation commands

Fig. 6

Command file (finish.txt) for calculation of standard errors, population predictions, individual prediction (post hoc analysis), correlations, and export of results

Fig. 7

Command file (beo.txt) for setting up a parallelized analysis using the Beowulf system and content of the first four lines of the beo table (beo.csv file)