The effect of using a robust optimality criterion in model based adaptive optimization.

Optimizing designs using robust (global) optimality criteria has been shown to be a more flexible approach compared to using local optimality criteria. Additionally, model based adaptive optimal design (MBAOD) may be less sensitive to misspecification in the prior information available at the design stage. In this work, we investigate the influence of using a local (lnD) or a robust (ELD) optimality criterion for a MBAOD of a simulated dose optimization study, for rich and sparse sampling schedules. A stopping criterion for accurate effect prediction is constructed to determine the endpoint of the MBAOD by minimizing the expected uncertainty in the effect response of the typical individual. 50 iterations of the MBAODs were run using the MBAOD R-package, with the concentration from a one-compartment first-order absorption pharmacokinetic model driving the population effect response in a sigmoidal EMAX pharmacodynamics model. The initial cohort consisted of eight individuals in two groups and each additional cohort added two individuals receiving a dose optimized as a discrete covariate. The MBAOD designs using lnD and ELD optimality with misspecified initial model parameters were compared by evaluating the efficiency relative to an lnD-optimal design based on the true parameter values. For the explored example model, the MBAOD using ELD-optimal designs converged quicker to the theoretically optimal lnD-optimal design based on the true parameters for both sampling schedules. Thus, using a robust optimality criterion in MBAODs could reduce the number of adaptations required and improve the practicality of adaptive trials using optimal design.

The effect of Fisher information matrix approximation methods in population optimal design calculations.

With the increasing popularity of optimal design in drug development it is important to understand how the approximations and implementations of the Fisher information matrix (FIM) affect the resulting optimal designs. The aim of this work was to investigate the impact on design performance when using two common approximations to the population model and the full or block-diagonal FIM implementations for optimization of sampling points. Sampling schedules for two example experiments based on population models were optimized using the FO and FOCE approximations and the full and block-diagonal FIM implementations. The number of support points was compared between the designs for each example experiment. The performance of these designs based on simulation/estimations was investigated by computing bias of the parameters as well as through the use of an empirical D-criterion confidence interval. Simulations were performed when the design was computed with the true parameter values as well as with misspecified parameter values. The FOCE approximation and the Full FIM implementation yielded designs with more support points and less clustering of sample points than designs optimized with the FO approximation and the block-diagonal implementation. The D-criterion confidence intervals showed no performance differences between the full and block diagonal FIM optimal designs when assuming true parameter values. However, the FO approximated block-reduced FIM designs had higher bias than the other designs. When assuming parameter misspecification in the design evaluation, the FO Full FIM optimal design was superior to the FO block-diagonal FIM design in both of the examples.

Simultaneous pharmacokinetic modeling of alkylresorcinols and their main metabolites indicates dual absorption mechanisms and enterohepatic elimination in humans.

BACKGROUND: Alkylresorcinols have proven to be useful biomarkers of whole-grain wheat and rye intake in many nutritional studies. To improve their utility, more knowledge regarding the fate of alkylresorcinols and their metabolites after consumption is needed. OBJECTIVE: The objective of this study was to develop a combined pharmacokinetic model for plasma concentrations of alkylresorcinols and their 2 major metabolites, 3,5-dihydroxybenzoic acid (DHBA) and 3-(3,5-dihydroxyphenyl)-propanoic acid (DHPPA). METHODS: The model was established by using plasma samples collected from 3 women and 2 men after a single dose (120 g) of rye bran and validated against fasting plasma concentrations from 8 women and 7 men with controlled rye bran intake (23, 45, or 90 g/d). Alkylresorcinols in the lymph and plasma of a pig fed a single alkylresorcinol dose (1.3 mmol) were quantified to assess absorption. Human ileostomal effluent and pig bile after high and low alkylresorcinol doses were analyzed to evaluate biliary alkylresorcinol metabolite excretion. RESULTS: The model contained 2 absorption compartments: 1 that transferred alkylresorcinols directly to the systematic circulation and 1 in which a proportion of absorbed alkylresorcinols was metabolized before reaching the systemic circulation. Plasma concentrations of alkylresorcinols and their metabolites depended on absorption and formation, respectively, and the mean ± SEM terminal elimination half-life of alkylresorcinols (1.9 ± 0.59 h), DHPPA (1.5 ± 0.26 h), and DHBA (1.3 ± 0.22 h) did not differ. The model accurately predicted alkylresorcinol and DHBA concentrations after repeated alkylresorcinol intake but DHPPA concentration was overpredicted, possibly because of poorly modeled enterohepatic circulation. During the 8 h following administration, <2% of the alkylresorcinol dose was recovered in the lymph. DHPPA was identified in both human ileostomal effluent and pig bile, indicating availability of DHPPA for absorption and enterohepatic circulation. CONCLUSION: Intact alkylresorcinols have advantages over DHBA and DHPPA as plasma biomarkers for whole-grain wheat and rye intake because of lower susceptibility to factors other than alkylresorcinol intake.

Chain length of dietary alkylresorcinols affects their in vivo elimination kinetics in rats.

Two phenolic acids, 3,5-dihydroxybenzoic acid (DHBA) and 3-(3,5-dihydroxyphenyl)- propanoic acid (DHPPA), are the major metabolites of cereal alkylresorcinols (ARs). Like their precursors, AR metabolites have been suggested as biomarkers for intake of whole-grain wheat and rye and as such could aid the understanding of diet-disease associations. This study estimated and compared pharmacokinetic parameters of ARs and their metabolites in rats and investigated differences in metabolite formation after ingestion of different AR homologs. Rats were i.v. infused for 30 min with 2, 12, or 23 µmol/kg DHBA or DHPPA or orally given the same amounts of the AR homologs, C17:0 and C25:0. Repeated plasma samples, obtained from rats for 6 h (i.v.) or 36 h (oral), were simultaneously analyzed for ARs and their metabolites by GC-mass spectrometry. Pharmacokinetic parameters were estimated by population-based compartmental modeling and noncompartmental calculation. A 1-compartment model best described C25:0 pharmacokinetics, whereas C17:0 and AR metabolites best fitted 2-compartment models. Combined models for simultaneous prediction of AR and metabolite concentration were more complex, with less reliable estimates of pharmacokinetic parameters. Although the AUC of C17:0 was lower than that of C25:0 (P < 0.05), the total amount and composition of AR metabolites did not differ between rats given C17:0 or C25:0. The elimination half-life of ARs and their metabolites increased with length of the side chain (P-trend < 0.001) and ranged from 1.2 h (DHBA) to 8.8 h (C25:0). The formation of AR metabolites was slower than their elimination, indicating that the rate of AR metabolism and not excretion of DHBA and DHPPA determines their plasma concentrations in rats.

PopED: an extended, parallelized, nonlinear mixed effects models optimal design tool.

Several developments have facilitated the practical application and increased the general use of optimal design for nonlinear mixed effects models. These developments include new methodology for utilizing advanced pharmacometric models, faster optimization algorithms and user friendly software tools. In this paper we present the extension of the optimal design software PopED, which incorporates many of these recent advances into an easily useable enhanced GUI. Furthermore, we present new solutions to problems related to the design of experiments such as: faster and more robust FIM calculations and optimizations, optimizing over cost/utility functions and diagnostic tools and plots to evaluate design performance. Examples for; (i) Group size optimization and efficiency translation, (ii) Cost/constraint optimization, (iii) Optimizations with different FIM approximations and (iv) optimization with parallel computing demonstrate the new features in PopED and underline the potential use of this tool when designing experiments.