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Systematical investigation of a combinative particle size reduction technology for production of resveratrol nanosuspensions

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Abstract

Nanosizing is frequently used as formulation approach to increase the bioavailability of poorly water-soluble drugs. However, standard size reduction processes can be relatively time-consuming. It was found that the modification of the physical properties of a starting material by means of spray-drying can be used to improve the effectiveness of a subsequently performed high pressure homogenization. Such a process belongs to the combinative particle size reduction methods and is also referred to as H 42 process. Based on previous studies, it was hypothesized that the improved efficiency was a result of reduced crystallinity of the modified drug. The present study was conducted in order to asses this hypothesis in a systematical manner by applying design of experiment (DoE) principles. Resveratrol was selected as model compound for this study. It was processed by both standard high pressure homogenization and by a combinative particle size reduction process (the H42 process). An optimized resveratrol/surfactant ratio for the spray-dried intermediate was identified by using the response-surface methodology. The optimization led to a nanosuspension with a mean particle size of 192 nm, which is much smaller than the mean particle size of 569 nm when standard high pressure homogenization was used. Both predominately crystalline and predominately amorphous solids resulted from the spray-drying process. In contrast to the initial hypothesis, the smallest particle sizes were achieved by processing predominately crystalline intermediate with high pressure homogenization.

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ACKNOWLEDGMENTS

Tao Liu would like to thank China Scholarship Council (CSC) for the financial support.

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Correspondence to Jan P. Möschwitzer.

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Liu, T., Müller, R.H. & Möschwitzer, J.P. Systematical investigation of a combinative particle size reduction technology for production of resveratrol nanosuspensions. AAPS PharmSciTech 18, 1683–1691 (2017). https://doi.org/10.1208/s12249-016-0612-1

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