Development of three layered buccal compact containing metoprolol tartrate by statistical optimization technique

Narendra, C; Srinath, M. S.; Rao, B. Prakash

doi:10.1016/j.ijpharm.2005.07.021

Cited by 39 publications

(28 citation statements)

References 25 publications

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“…Therefore, in this work, central composite design was used to simultaneously study the effect of the two formulation variables of the chitosan-alginate delivery system on two response variables. Central composite design (CCD) and analysis of response surfaces were used because they are systematic and efficient methods to study the effect of multiple variables and to find an optimum formulation (16).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Chitosan/Alginate Beads Using Experimental Design: Formulation and In Vitro Characterization

2010

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Abstract. Bovine serum albumin-loaded beads were prepared by ionotropic gelation of alginate with calcium chloride and chitosan. The effect of sodium alginate concentration and chitosan concentration on the particle size and loading efficacy was studied. The diameter of the beads formed is dependent on the size of the needle used. The optimum condition for preparation alginate-chitosan beads was alginate concentration of 3% and chitosan concentration of 0.25% at pH 5. The resulting bead formulation had a loading efficacy of 98.5% and average size of 1,501 μm, and scanning electron microscopy images showed spherical and smooth particles. Chitosan concentration significantly influenced particle size and encapsulation efficiency of chitosan-alginate beads (p<0.05). Decreasing the alginate concentration resulted in an increased release of albumin in acidic media. The rapid dissolution of chitosan-alginate matrices in the higher pH resulted in burst release of protein drug.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Chitosan/Alginate Beads Using Experimental Design: Formulation and In Vitro Characterization

2010

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“…The selected model was further assessed for the lack of fit (LOF) and for the studied responses; LOF was not found to be significant (21)(22). The values of R 2 , adjusted R 2 , predicted R 2 , SD, % CV (Table II) and the analysis of variance results for each response (ANOVA) in Table II (12.036) were found to be higher than 4, indicating the adequacy and suitability of the model for the studied responses (23)(24).…”

Section: Data Optimization and Model Validationmentioning

confidence: 96%

Sustained release biodegradable solid lipid microparticles: Formulation, evaluation and statistical optimization by response surface methodology

et al. 2017

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Sustained release biodegradable solid lipid microparticles: Formulation, evaluation and statistical optimization by response surface methodologyFor preparing nebivolol loaded solid lipid microparticles (SLMs) by the solvent evaporation microencapsulation process from carnauba wax and glyceryl monostearate, central composite design was used to study the impact of independent variables on yield (Y 1 ), entrapment efficiency (Y 2 ) and drug release (Y 3 ). SLMs having a 10-40 µm size range, with good rheological behavior and spherical smooth surfaces, were produced. Fourier transform infrared spectroscopy, differential scanning calorimetry and X-ray diffractometry pointed to compatibility between formulation components and the zeta-potential study confirmed better stability due to the presence of negative charge (-20 to -40 mV). The obtained outcomes for Y 1 (29-86 %), Y 2 (45-83 %) and Y 3 (49-86 %) were analyzed by polynomial equations and the suggested quadratic model were validated. Nebivolol release from SLMs at pH 1.2 and 6.8 was significantly (p < 0.05) affected by lipid concentration. The release mechanism followed Higuchi and zero order models, while n > 0.85 value (Korsmeyer-Peppas) suggested slow erosion along with diffusion. The optimized SLMs have the potential to improve nebivolol oral bioavailability.

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“…The coefficients b 1 , b 2 , and b 11 were found to be significant at p < 0.05; hence they were retained in the reduced model. The reduced model was tested in portions to determine whether the coefficients b 12 Table 4. The reduced model was tested in portions to determine whether the coefficients b 11 , b 12 , and b 22 contribute significant information for the prediction of %F or not.…”

Section: Measurement Of Powder Flowmentioning

confidence: 99%

“…The reduced model was tested in portions to determine whether the coefficients b 12 Table 4. The reduced model was tested in portions to determine whether the coefficients b 11 , b 12 , and b 22 contribute significant information for the prediction of %F or not. The results for testing the model in portions are depicted in Table 4.…”

Section: Measurement Of Powder Flowmentioning

confidence: 99%

Experimental Design to Predict Process Variables in the Microcrystals of Celecoxib for Dissolution Rate Enhancement Using Response Surface Methodology

Jelvehgari¹,

Valizadeh²,

Montazam³

et al. 2015

Adv Pharm Bull

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Development of three layered buccal compact containing metoprolol tartrate by statistical optimization technique

Cited by 39 publications

References 25 publications

Evaluation of Chitosan/Alginate Beads Using Experimental Design: Formulation and In Vitro Characterization

Evaluation of Chitosan/Alginate Beads Using Experimental Design: Formulation and In Vitro Characterization

Sustained release biodegradable solid lipid microparticles: Formulation, evaluation and statistical optimization by response surface methodology

Experimental Design to Predict Process Variables in the Microcrystals of Celecoxib for Dissolution Rate Enhancement Using Response Surface Methodology

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