Development of a New Pre- and Post-Processing Tool (SADAPT-TRAN) for Nonlinear Mixed-Effects Modeling in S-ADAPT

Abstract

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 covariatestratified 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, lognormally, 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.

p16INK4a, a Senescence Marker, Influences Tenofovir/Emtricitabine Metabolite Disposition in HIV‐Infected Subjects

“…Sparse sampling times were optimized based on the intensive pharmacokinetic (PK) study; detailed descriptions of methods and results for this group have been published 24. Briefly, to optimize collection times, a two‐compartment model with first‐order absorption and linear CL was fit to each drug using S‐ADAPT with the S‐ADAPT TRAN pre‐ and postprocessing package 25. The mean parameter estimates and their variability were used in designing the sampling scheme.…”

Population Pharmacokinetics Modeling of Unbound Efavirenz, Atazanavir, and Ritonavir in HIV‐Infected Subjects With Aging Biomarkers

“…Sparse sampling times were optimized based on the intensive PK study; detailed descriptions of methods and results for this group have been published 28. Briefly, to optimize collection times, a two‐compartment model with first‐order absorption and linear clearance was fit to each drug using S‐ADAPT with the S‐ADAPT TRAN pre‐ and postprocessing package 29. The mean parameter estimates and their variability were used in designing the sampling scheme.…”

Quantifying Subpopulation Synergy for Antibiotic Combinations via Mechanism-Based Modeling and a Sequential Dosing Design

“…Static time-kill studies assessed nisin (4,8,32, and 128 mg/liter), amikacin (1,4,16, and 64 mg/liter), and linezolid (2, 8, and 32 mg/liter) in monotherapy.…”

“…To provide a robust mathematical analysis, model development was performed independently in two software packages (S-ADAPT and NONMEM) which utilize different estimation algorithms. The importance sampling Monte Carlo parametric expectation-maximization method (pmethod ϭ 4) in S-ADAPT (version 1.57 [31]) using SADAPT-TRAN (32,33) and the first-order conditional estimation method with the interaction option (FOCEϩI) in NONMEM VI (level 1.2; using the ADVAN9 subroutine; NONMEM Project Group, Icon Development Solutions, Ellicott City, MD [34]) were applied. Models were evaluated based on the S-ADAPT objective function value (Ϫ1ϫ log likelihood), NONMEM objective function value (Ϫ2ϫ log likelihood), and a series of standard diagnostic plots as previously described (35)(36)(37).…”