Use of Placket–Burman Statistical Design to Study Effect of Formulation Variables on the Release of Drug from Hot Melt Sustained Release Extrudates

Jain, Sanmati K.; Singh, Pirthi Pal; Javeer, Sharad; Amin, Purnima D.

doi:10.1208/s12249-010-9444-6

Cited by 25 publications

(13 citation statements)

References 42 publications

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“…The magnitude of the statistically significant parameters is depicted in a half‐normal plot (Figure a), which displays the absolute value of all effects, both positive and negative. Instead of putting the negative effects to the left and the positive effects to the right, all of the significant effects were placed on the right side of the origin in order to compare their relative magnitudes . The Pareto chart (Figure b) is a pictorial representation of the results of a Plackett‐Burman experimental design.…”

Section: Resultsmentioning

confidence: 99%

“…The Plackett‐Burman analysis is a two‐level fractional factorial design that can be used to screen large number of variables with least number of experiments . Further, the response surface methodology can be described as collection of experimental strategies, mathematical methods, and statistical interference that estimates the approximate relation between variables in order to determine the optimal conditions for the system .…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural network‐genetic algorithm (ANN‐GA) based medium optimization for the production of human interferon gamma (hIFN‐γ) in Kluyveromyces lactis cell factory

et al. 2019

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In the current investigation, we have adapted response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA) based optimization to develop a defined medium for maximizing human interferon gamma production from recombinant Kluyveromyces lactis (K. lactis).In the initial screening studies, sorbitol and glycine emerged as a carbon and nitrogen source respectively having higher influence on hIFN-g production. Substrate inhibition studies were performed by varying the initial substrate concentration, and we found maximum hIFN-g concentration at 50 g L À1 of sorbitol. Inhibition kinetics studies were carried out using 3 and 4-parametric models. Among the estimated models, the Moser model was observed as the best fitted model followed by the Luong model with R 2 values of 0.882 and 0.75, respectively. The model acceptability test was carried out using the extra sum of squares F-test and Akaike information criterion (AIC). The Plackett-Burman multifactorial design identified sorbitol, glycine, Na 2 HPO 4 , and MgSO 4 .7H 2 O as the parameters significantly influencing the hIFN-g production. Further, the Box-Behnken design (BBD) followed by the artificial neural network coupled with genetic algorithm (ANN-GA) was employed for the precise optimization of medium components. With ANN-GA a maximum hIFN-g yield of 2.1 AE0.3 mg L À1 in shake flask level and 3.5 AE0.1 mg L À1 in reactor level was achieved. The findings of this study serve as a model for a process development strategy (bench scale to reactor scale) to achieve a high productivity of the desired protein from a microbial cell factory.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial neural network‐genetic algorithm (ANN‐GA) based medium optimization for the production of human interferon gamma (hIFN‐γ) in Kluyveromyces lactis cell factory

et al. 2019

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“…A 12‐run, Resolution III Plackett–Burman design was selected to analyze the effect of 10 amino acids chosen according to three criteria: consumption rate by measurement of their concentration in culture medium, mass fraction in the mAb produced by the cells, and reports from the literature. In addition, a dummy variable was included to account for the experimental error and two‐factor interactions, as recommended in the literature 12‐14, 19, 25…”

Section: Methodsmentioning

confidence: 99%

Use of a Plackett–Burman statistical design to determine the effect of selected amino acids on monoclonal antibody production in CHO cells

González-Leal

Carrillo-Cocom

Ramírez-Medrano

et al. 2011

Biotechnology Progress

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Culture media design is central to the optimization of monoclonal antibody (mAb) production. Although general strategies do not currently exist for optimization of culture media, the combined use of statistical design and analysis of experiments and strategies based on simple material balances can facilitate culture media design. In this study, we evaluate the effect of selected amino acids on the growth rate and monoclonal antibody production of a Chinese hamster ovary DG-44 (CHO-DG44) cell line. These amino acids were selected based on their relative mass fraction in the specific mAb produced in this study, their consumption rate during bioreactor experiments, and also through a literature review. A Plackett-Burman statistical design was conducted to minimize the number of experiments needed to obtain statistically relevant information. The effect of this set of amino acids was evaluated during exponential cell culture (considering viable cell concentration and the specific growth rate as main output variables) and during the high cell-density stage (considering mAb final concentration and specific productivity as relevant output variables). For this particular cell line, leucine (Leu) and arginine (Arg) had the highest negative and positive effects on cell viability, respectively; Leu and threonine (Thr) had the highest negative effect on growth rate, and valine (Val) and Arg demonstrated the highest positive impact on mAb final concentration. Results suggest the pertinence of a two-stage strategy for amino acid supplementation, with a mixture optimized for cell growth and a different amino acid mixture for mAb production at high density.

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“…In this design six factors were evaluated. Hence by applying Plackett-Burman factorial design, influence of six independent variables, osmotic agents (NaCl, MCC), pore forming agents (SLS, sucrose), and coating agents (cellulose acetate, ethyl cellulose), was studied over three dependent variables' drug release at 2 hr, 6 hr, and 12 hr and was checked [ 6 , 7 ]. Formulation of osmotic tablets of factorial batches is shown in Tables 3 and 4 .…”

Section: Methodsmentioning

confidence: 99%

Design, Characterization, and Optimization of Controlled Drug Delivery System Containing Antibiotic Drug/s

Patel¹,

Dodiya²,

Shelate³

et al. 2016

Journal of Drug Delivery

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The objective of this work was design, characterization, and optimization of controlled drug delivery system containing antibiotic drug/s. Osmotic drug delivery system was chosen as controlled drug delivery system. The porous osmotic pump tablets were designed using Plackett-Burman and Box-Behnken factorial design to find out the best formulation. For screening of three categories of polymers, six independent variables were chosen for Plackett-Burman design. Osmotic agent sodium chloride and microcrystalline cellulose, pore forming agent sodium lauryl sulphate and sucrose, and coating agent ethyl cellulose and cellulose acetate were chosen as independent variables. Optimization of osmotic tablets was done by Box-Behnken design by selecting three independent variables. Osmotic agent sodium chloride, pore forming agent sodium lauryl sulphate, and coating agent cellulose acetate were chosen as independent variables. The result of Plackett-Burman and Box-Behnken design and ANOVA studies revealed that osmotic agent and pore former had significant effect on the drug release up to 12 hr. The observed independent variables were found to be very close to predicted values of most satisfactory formulation which demonstrates the feasibility of the optimization procedure in successful development of porous osmotic pump tablets containing antibiotic drug/s by using sodium chloride, sodium lauryl sulphate, and cellulose acetate as key excipients.

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Use of Placket–Burman Statistical Design to Study Effect of Formulation Variables on the Release of Drug from Hot Melt Sustained Release Extrudates

Cited by 25 publications

References 42 publications

Artificial neural network‐genetic algorithm (ANN‐GA) based medium optimization for the production of human interferon gamma (hIFN‐γ) in Kluyveromyces lactis cell factory

Artificial neural network‐genetic algorithm (ANN‐GA) based medium optimization for the production of human interferon gamma (hIFN‐γ) in Kluyveromyces lactis cell factory

Use of a Plackett–Burman statistical design to determine the effect of selected amino acids on monoclonal antibody production in CHO cells

Design, Characterization, and Optimization of Controlled Drug Delivery System Containing Antibiotic Drug/s

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