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Comparison of Nonmem 7.2 estimation methods and parallel processing efficiency on a target-mediated drug disposition
Leonid Gibiansky1, Ekaterina Gibiansky, Robert Bauer
1QuantPharm LLC, 49 Flints Grove Drive, North Potomac, MD, USA. LGibiansky@quantpharm.com
Journal of Pharmacokinetics and Pharmacodynamics
|November 22, 2011
Summary
New Nonmem estimation methods (ITS, IMP, IMPMAP, SAEM, BAYES) show comparable performance to FOCEI for monoclonal antibody TMDD models. Faster computation times were achieved with ITS, IMP, IMPMAP, and optimized FOCEI settings.
Area of Science:
- Pharmacometrics
- Computational Biology
- Drug Development
Background:
- Nonlinear Mixed-Effects (NLME) modeling is crucial for pharmacokinetic/pharmacodynamic (PK/PD) analysis.
- Target-Mediated Drug Disposition (TMDD) models are complex and require robust estimation methods.
- Evaluating the performance of various Nonmem estimation techniques is essential for efficient model development.
Purpose of the Study:
- To compare the performance of multiple Nonmem estimation methods for TMDD models.
- To assess the impact of optimizing estimation settings on performance and computational time.
- To compare Nonmem parameter estimate standard errors with predictions from PFIM 3.2 software.
Main Methods:
- Simulated data from one- and two-target quasi-steady-state TMDD models were used.
- Performance of First-Order Conditional Estimation with Interaction (FOCEI), Iterative Two-Stage (ITS), Monte Carlo Importance Sampling (IMP), Importance Sampling assisted by Mode a Posteriori (IMPMAP), Stochastic Approximation Expectation-Maximization (SAEM), and Bayesian (BAYES) methods were evaluated.
- Optimization of estimation options, including computational precision and parallel processing, was investigated.
Main Results:
- ITS, IMP, IMPMAP, SAEM, and BAYES methods yielded parameter estimates and standard errors similar to FOCEI.
- BAYES method showed larger deviations for poorly identifiable parameters.
- ITS, IMP, and IMPMAP with convergence testing were approximately 10 times faster than FOCEI; optimized FOCEI was 3-5 times faster. Parallel processing improved speed significantly for most methods.
Conclusions:
- Newer Nonmem estimation methods offer comparable accuracy to FOCEI for TMDD models.
- Optimizing estimation settings and utilizing parallel computing can substantially reduce computation time.
- Standard errors from Nonmem were generally consistent with PFIM 3.2 predictions.
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