Self-nanoemulsifying Drug Delivery Systems: Formulation Insights, Applications and Advances

Abhijit A Date; Neha Desai; Rahul Dixit; Mangal Nagarsenker


Nanomedicine. 2010;5(10) 

In This Article

Component Screening, Optimization & Characterization of SNEDDS

As previously mentioned, the components of SNEDDS and their concentrations have profound effects on the various characteristics of nanoemulsions, such as droplet size, polydispersity index, self-nanoemulsification time and in vitro drug release. Hence, it is important to optimize the quantities of the SNEDDS components after initial selection. The initial selection of the components can be on the basis of their ability to solubilize the drug of interest and also on their ability to form spontaneous emulsions/nanoemulsions. We have developed a systematic protocol based on a simple turbidimetric method that evaluates emulsification efficiency or ease of emulsification ability of various excipients.[40] This protocol gives quick information regarding the potential components of SNEDDS and it has been adopted by a few other researchers.[48,67] After selecting potential components of SNEDDS, the phase behavior of the components should be studied to identify various phases and phase transitions. After studying phase behavior and identifying probable concentrations of the components that might yield spontaneous nanoemulsions, it is important to plot a ternary diagram with surfactant, oil and coemulsifier or solubilizer to identify the self-nanoemulsification region. The self-nanoemulsification region in the ternary diagram is identified by evaluating droplet size of the emulsions/nanoemulsions resulting after diluting various compositions in the ternary diagram with the fixed amount of water. All the points in the self-nanoemulsification region yield spontaneous nanoemulsion with droplet sizes of approximately 200 nm or lesser.

It is important to study the influence of the drug on the self-nanoemulsification region of the ternary diagram. We have observed that drugs such as cefpodoxime proxetil can considerably reduce the self-nanoemulsification region in the ternary diagram.[40] It is also important to study the influence of the pH of the aqueous phase on the self-nanoemulsification region. We have observed that the pH of the aqueous medium has considerable influence on the self-nanoemulsification region.[40] Thus, determination of the self-nanoemulsfication region (in addition to the phase behavior study) helps in the optimization of SNEDDS and also helps in finalizing the SNEDDS composition for in vitro and in vivo studies. The optimization of SNEDDS can also be accomplished with the help of optimization techniques, such as statistical experimental design or response surface methodology. The major advantage of the response surface methodology is that they can yield optimal SNEDDS (composition) with a minimal number of experiments without compromising the final product characteristics. In response surface methodology, the influence of several variables on the characteristics of SNEDDS (e.g., droplet size, self-nanoemulsification time and in vitro dissolution) can be studied with a limited number of experiments. The statistical analysis is used to identify the impact of each variable on the characteristics of the SNEDDS. Once the mathematical correlation is established between the variables and the response, response surface methodology can be used to develop a product with desired characteristics. Thus, SNEDDS composition with much reduced self-nanoemulsification time, small droplet size and higher dissolution rate can be obtained with statistical experimental design techniques. Various optimization techniques such as Box-Behnken design and D-Optimal design have been employed by the investigators to optimize various characteristics of SNEDDS.[48,68–71]

It is important to characterize the final SNEDDS for various parameters. The droplet size and polydispersity index, colloidal stability and self-nanoemulsification time of the SNEDDS as a function of extent of dilution and variation in the pH/electrolyte content of aqueous phase should be carefully studied. The zeta potential of the SNEDDS should be evaluated as it may further give an idea of the colloidal stability. The morphology of the nanoemulsion droplets can be evaluated by transmission electron microscopy. The SNEDDS should be characterized for in vitro dissolution profile in various dissolution media. The chemical stability of the drug in SNEDDS should be evaluated by carrying out long-term storage stability studies as per the guidelines suggested by the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH guidelines). The effect of the SNEDDS on Caco-2 cell permeability and toxicity, and also on GI permeability, can be studied by in vitro techniques.[72] Recently, Fatouros and coworkers have developed an in vitro lipolysis model, which simulates digestion in the small intestine.[73,74] They successfully applied this model to study the digestion of the SNEDDS and propensity of drug precipitation. Interestingly, they could rank order various SNEDDS formulations on the basis of in vitro lipolysis and drug precipitation. This method would also help in optimizing/correcting SNEDDS formulation before subjecting it to in vivo efficacy or pharmacokinetic studies. Furthermore, this model could also be used for establishing in vitro and in vivo correlation of SNEDDS formulations.


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