2006
DOI: 10.1080/03639040600685167
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D-Optimal Mixture Design: Optimization of Ternary Matrix Blends for Controlled Zero-Order Drug Release From Oral Dosage Forms

Abstract: The objective of the present study was to develop a tablet formulation with a zero-order drug release profile based on a balanced blend of three matrix ingredients. To accomplish this goal, a 17-run, three-factor, two-level D-Optimal mixture design was employed to evaluate the effect of Polyox (X1), Carbopol (X2), and lactose (X3) concentrations on the release rate of theophylline from the matrices. Tablets were prepared by direct compression and were subjected to an in vitro dissolution study in phosphate buf… Show more

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Cited by 49 publications
(21 citation statements)
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“…The one-factor-at-a-time optimization also ignores interaction between factors and may call for an unnecessarily large number of runs. 9 Currently more and more attention has been paid to the formulation optimization in the course of establishing SLN dispersion systems. Some studies [10][11][12] have optimized nanoparticulate formulations using factorial design.…”
mentioning
confidence: 99%
“…The one-factor-at-a-time optimization also ignores interaction between factors and may call for an unnecessarily large number of runs. 9 Currently more and more attention has been paid to the formulation optimization in the course of establishing SLN dispersion systems. Some studies [10][11][12] have optimized nanoparticulate formulations using factorial design.…”
mentioning
confidence: 99%
“…The mixture components cannot range in an independent way since their sum has to be equal to 100% [18] . D-optimal mixture design is commonly used to reveal main effects and interaction effects between the independent variables of the experiment [20] .…”
Section: D-optimal Designmentioning
confidence: 99%
“…Traditional formulation screening and optimization process like univariate one-factor-at-a-time methods, however, may call for an unnecessarily large number of runs or give an unreliable result (El-Malah et al, 2006;Hao et al, 2011). Statistical experimental designs including Plackett-Burman and response surface methodologies can collectively eliminate these limitations of a singlefactor optimization process.…”
Section: Introductionmentioning
confidence: 99%