Abstract
This study evaluated the incorporation of tetracaine into liposomes by RSM (Response Surface Methodology) and ANN (Artificial Neural Networks) based models. RCCD (rotational central composite design) and ANN were performed to optimize the sonication conditions of particles containing 100 % lipid. Laser light scattering was used to perform measure hydrodynamic radius and size distribution of vesicles. The liposomal formulations were analyzed by incorporating the drug into the hydrophilic phase or the lipophilic phase. RCCD and ANN were conducted, having the lipid/cholesterol ratio and concentration of tetracaine as variables investigated and, the encapsulation efficiency and mean diameter of the vesicles as response variables. The optimum sonication condition set at a power of 16 kHz and 3 minutes, resulting in sizes smaller than 800 nm. Maximum encapsulation efficiency (39.7 %) was obtained in the hydrophilic phase to a tetracaine concentration of 8.37 mg/mL and 79.5:20.5% lipid/cholesterol ratio. Liposomes were stable for about 30 days (at 4 °C), and the drug encapsulation efficiency was higher in the hydrophilic phase. The experimental results of RCCD-RSM and ANN techniques show ANN obtained more refined prediction errors that RCCD-RSM technique, therefore, ANN can be considered as an efficient mathematical method to characterize the incorporation of tetracaine into liposomes.
Highlights
The mathematical modeling of drugs encapsulation has been a research target in the recent years
The liposomes obtained in the LUV diameter were chosen for being able to extravasate blood into the interstitial space without causing pulmonary embolism (Lasic, 1993; Sapra, Allen, 2003)
Studies on the vesicles size show that the rate of recognition and removal by phagocytic defense system varies according to the diameter with a long half-life of liposomes (> 1000 nm) of 0.2 h while the shortest have a 1.5 h halflife (Laverman et al, 1999)
Summary
The mathematical modeling of drugs encapsulation has been a research target in the recent years. Methods based on the Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) have been showing satisfactory results (Sun et al, 2003). The use of statistical experimental planning and RSM explores the relationships between several explanatory variables and one or more response variables and is one of the most popular methods in the optimization of drug delivery systems. In general, the predictions of response variables are done by a second-order equation. Since this prediction is often limited to low levels, ANN was incorporated to overcome this limitation (Takayama, Fujikawa, Nagai, 1991; Sulaiman et al, 2017; Moussa et al 2017)
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