title: Comparison of Nonmem 7.2 estimation methods and parallel processing efficiency on a target-mediated drug disposition model.
abstract: The paper compares performance of Nonmem estimation methods--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 Markov chain Monte Carlo Bayesian (BAYES), on the simulated examples of a monoclonal antibody with target-mediated drug disposition (TMDD), demonstrates how optimization of the estimation options improves performance, and compares standard errors of Nonmem parameter estimates with those predicted by PFIM 3.2 optimal design software. In the examples of the one- and two-target quasi-steady-state TMDD models with rich sampling, the parameter estimates and standard errors of the new Nonmem 7.2.0 ITS, IMP, IMPMAP, SAEM and BAYES estimation methods were similar to the FOCEI method, although larger deviation from the true parameter values (those used to simulate the data) was observed using the BAYES method for poorly identifiable parameters. Standard errors of the parameter estimates were in general agreement with the PFIM 3.2 predictions. The ITS, IMP, and IMPMAP methods with the convergence tester were the fastest methods, reducing the computation time by about ten times relative to the FOCEI method. Use of lower computational precision requirements for the FOCEI method reduced the estimation time by 3-5 times without compromising the quality of the parameter estimates, and equaled or exceeded the speed of the SAEM and BAYES methods. Use of parallel computations with 4-12 processors running on the same computer improved the speed proportionally to the number of processors with the efficiency (for 12 processor run) in the range of 85-95% for all methods except BAYES, which had parallelization efficiency of about 70%.
authors.affiliation: QuantPharm LLC, 49 Flints Grove Drive, North Potomac, MD, USA. LGibiansky@quantpharm.com
authors.forename: Leonid
authors.initials: L
authors.lastname: Gibiansky
authors.forename: Ekaterina
authors.initials: E
authors.lastname: Gibiansky
authors.forename: Robert
authors.initials: R
authors.lastname: Bauer
mesh_terms: D000465:Algorithms; D000911:Antibodies, Monoclonal / Q000493:pharmacokinetics; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D058990:Molecular Targeted Therapy; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D010599:Pharmacokinetics; D013269:Stochastic Processes
chemical_list: D000911:Antibodies, Monoclonal
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