NONMEM USER GUIDE SOFTWARE

Д«eal SL (2001) Ways to fit a PK model with some data below the quantification limit. NONMEM Plc, DublinДЄlmquist J, Leander J, Jirstrand M (2015) Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the population likelihood. Д«auer RJ (2016) Technical guide on various methods in NONMEM 7. ДЄn G, Murry DJ, Gajurel K, Bach T, Deye G, Stebounova LV, Codd EE, Horton J, Gonzalez AE, Garcia HH, Ince D, Hodgson-Zingman D, Nomicos EYH, Conrad T, Kennedy J, Jones W, Gilman RH, Winokur P (2019) Pharmacokinetics, Safety, and Tolerability of Oxfendazole in Healthy Volunteers: a Randomized, Placebo-Controlled First-in-Human Single-Dose Escalation Study.

NONMEM USER GUIDE SOFTWARE

Д¬han PL, Jacqmin P, Lavielle M, McFadyen L, Weatherley B (2011) The use of the SAEM algorithm in MONOLIX software for estimation of population pharmacokinetic-pharmacodynamic-viral dynamics parameters of maraviroc in asymptomatic HIV subjects. Plan EL, Maloney A, Mentre F, Karlsson MO, Bertrand J (2012) Performance comparison of various maximum likelihood nonlinear mixed-effects estimation methods for dose-response models. Дuffull SB, Kirkpatrick CM, Green B, Holford NH (2005) Analysis of population pharmacokinetic data using NONMEM and WinBUGS.

Johansson AM, Ueckert S, Plan EL, Hooker AC, Karlsson MO (2014) Evaluation of bias, precision, robustness and runtime for estimation methods in NONMEM 7. Liu X, Wang Y (2016) Comparing the performance of FOCE and different expectation-maximization methods in handling complex population physiologically-based pharmacokinetic models. Gibiansky L, Gibiansky E, Bauer R (2012) Comparison of Nonmem 7.2 estimation methods and parallel processing efficiency on a target-mediated drug disposition model. ICON Plc, DublinД«auer RJ, Guzy S, Ng C (2007) A survey of population analysis methods and software for complex pharmacokinetic and pharmacodynamic models with examples. NONMEM Project Group, San FranciscoД«auer RJ (2017) NONMEM users guide-Introduction to NONMEM 7.4.1. Д«eal S, Sheiner L (1998) NONMEM users guide-Part VII: Conditional estimation methods. Michaelis-Menten model: routine clinical pharmacokinetic data. Sheiner LB, Beal SL (1980) Evaluation of methods for estimating population pharmacokinetics parameters. With parallel computing introduced in NONMEM 7.2, modeling speed increased less than proportionally with the increase in the number of CPUs from 1 to 16. IMP was more sensitive to model misspecification without pre-systemic metabolism, IMP analysis failed to converge. For the most complex model (i.e., model of all three analytes, one of which having high level of data below quantification limit), FOCE failed to reliably assess parameter precision, while parameter precision obtained by IMP and SAEM was similar with SAEM being the faster method. FOCE FAST was about two times faster than FOCE but it was prone to premature termination.

For simpler models (i.e., models of oxfendazole and/or oxfendazole sulfone), FOCE and FOCE FAST were more efficient than EM methods with shorter run time and comparable parameter precision. All methods yielded similar parameter estimates, but great differences were observed in parameter precision and modeling time. In this study, we compared the performance of FOCE, FOCE FAST, and two EM methods, namely importance sampling (IMP) and stochastic approximation expectationвЂ“maximization (SAEM), utilizing the rich pharmacokinetic data of oxfendazole and its two metabolites obtained from the first-in-human single ascending dose study in healthy adults. In NONMEM 7, expectationвЂ“maximization (EM) estimation methods and FOCE with FAST option (FOCE FAST) were introduced.

However, with growing data complexity, the performance of FOCE is challenged by long run time, convergence problem and model instability. First-order conditional estimation (FOCE) has been the most frequently used estimation method in NONMEM, a leading program for population pharmacokinetic/pharmacodynamic modeling.

Nonmem user guide