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

Area of Science:

• Pharmacometrics
• Computational Biology
• Statistical Modeling

Background:

• Mechanistic modeling requires robust pre- and post-processing for efficient analysis.
• Existing S-ADAPT software has limited capabilities in automated model code and results processing.
• Nonlinear mixed-effects modeling (NLME) is crucial for population-level pharmacokinetic and pharmacodynamic analyses.

Purpose of the Study:

• To develop a fully automated, open-source pre- and post-processor for NLME in S-ADAPT.
• To enhance the efficiency and reduce errors in mechanistic model development and evaluation.
• To extend the capabilities of the Monte Carlo Parametric Expectation Maximization (MC-PEM) algorithm.

Main Methods:

• Developed SADAPT-TRAN, a Perl-based tool for automated translation of model components to Fortran code.
• Implemented extensive mutual error checks across input files and datasets.
• Enhanced MC-PEM algorithm options and improved numerical robustness of model code.

Main Results:

• SADAPT-TRAN significantly improved efficiency in specifying, debugging, and evaluating NLME models.
• Automated summarization of results and generation of diagnostic plots in R were achieved.
• Facilitated parallelized estimation and handling of complex parameter variability models.

Conclusions:

• The SADAPT-TRAN package substantially streamlines mechanistic model development in S-ADAPT.
• Reduced model specification errors and provided valuable error messages for users of all levels.
• Enhanced flexibility and efficiency for complex mechanistic modeling workflows.