Crystalline and amorphous carvedilol-loaded nanoemulsions: formulation optimisation using response surface methodology

Abstract

(2013) Crystalline and amorphous carvedilolloaded nanoemulsions: formulation optimisation using response surface methodology, Journal of Experimental Nanoscience, 8:7-8, 971-992, DOI: 10.1080/17458080.2011 This study aimed to evaluate the crystalline and amorphous carvedilol along with their lipidic mixtures using various instrumental techniques and to use response surface methodology in conjunction with factorial design to establish the functional relationships between operating variables (capmul GMS 50 K and cremophor RH 40). The response variables selected are spectroscopic absorbance (Y 1 ), mean particle size in distilled water (Y 2 ) and in phosphate buffer pH 6.8 (Y 3 ), polydispersibility index (PDI) (Y 4 ) and zeta potential (Y 5 ). The optimal formulations of crystalline and amorphous carvedilol-loaded nanoemulsions were composed of fixed levels, À0.41 and À0.42, of capmul GMS 50 K and cremophor RH 40, respectively. The predicted and observed values of Y 1 -Y 5 for blank nanoemulsions showed the percentage bias error of À12.12%, À10.25%, À18.47%, þ14.81 and À2.89, respectively. The bias percent ranged between À2.70% and À29.41% for the responses Y 1 -Y 4 for both crystalline and amorphous carvedilol-loaded nanoemulsions, indicating high degree of prognosis. However, the bias percent values for the response variable Y 5 were 294.2% and 262.6%, for the crystalline and amorphous carvedilol-loaded nanoemulsions, respectively, possibly due to cationisation of emulsion droplets. The transmission electron microscopy of selected optimal nanoemulsions showed the spherical shape of globules with no signs of coalescence and precipitation of drug. This study demonstrates the use of factorial design for the preparation of nanoemulsions of crystalline and amorphous carvedilol. The desirable goals can be obtained by systematic formulation approach in the shortest possible time.

Evaluation of carvedilol-loaded microsponges with nanometric pores using response surface methodology

“…The above equation represents the quantitative effect of process variables (X1 and X2) and their interaction on the response (Y1). A positive sign represents a synergistic effect, while a negative sign indicates an antagonistic effect [6]. The polynomial equation showed a good fit to the response variable as the predicted r 2 of 0.8260 is in reasonable agreement with the adjusted r 2 of 0.9103 (Table 2).…”

“…Carvedilol is practically insoluble in water and exhibits pH-dependent solubility. Its solubility is <1 mg/mL above pH 9.0, 23 mg/mL at pH 7, and about 100 mg/mL at pH 5 at room temperature [6]. Carvedilol undergoes significant stereoselective first-pass metabolism, resulting in low absolute bioavailability (30% or less) [7][8][9].…”

“…The responses are measured for each trial and then either simple linear or interactive or quadratic model is fitted by carrying out multiple regression analysis and F-statistics to identify statistically significant terms. The reduced equation, an equation containing only statistically significant terms, is then used for drawing response surface plots to visualise the impact of changing variables at a glance [6]. The optimum point may be identified using the desirability function and replicate trials may be run to verify the prediction of optimum response.…”

“…In addition to the art of formulation, the technique of factorial design is an efficient method of indicating the relative significance of a number of variables and their interactions [10]. The response surface method, first reported by Box and Wilson [11], has been applied to the dosage form design for various kinds of drugs by several other researchers [6,[12][13][14]. Factorial design is a very efficient tool to obtain an appropriate mathematical model with minimum experiments for the optimisation of formulation design.…”

“…Responses obtained from the reduced equation, an equation containing only statistically significant terms, are used for drawing response surface plots to visualise the impact of changing variables. The optimum point may be identified from the plot and replicate trials may be run to verify the prediction of optimum response [6].…”