4.7 Article

Simulating particle movement inside subcutaneous injection site simulator (SCISSOR) using Monte-Carlo method

Journal

INTERNATIONAL JOURNAL OF PHARMACEUTICS
Volume 605, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.ijpharm.2021.120824

Keywords

Monte Carlo simulation; Subcutaneous formulation; SCISSOR; Diffusion; Binding; Aggregation

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This study utilized Monte Carlo method to simulate particle movement inside the SCISSOR instrument and thoroughly investigated a series of parameters related to the instrument, injection device, operation, formulation, and medium. The results showed that factors such as membrane area and position, diffusivity, and injection position significantly affected release profile, while binding slowed down release and aggregation reduced release percentage and rate. The study suggests that Monte Carlo method could be a useful tool for supporting multiple development activities in experimental design, parameter sensitivity analysis, and control strategy development.
This study used Monte Carlo method to simulate particle movement inside a commercialized instrument called Subcutaneous Injection Site Simulator (SCISSOR). A series of parameters related to instrument, injection device, operation, formulation, as well as medium were thoroughly investigated. Also, several events that particles may occur in the subcutaneous (SC) space, including diffusion, binding, and aggregation, were implemented in our Monte Carlo based algorithms. The simulation result revealed that membrane area and position, diffusivity in the simulated SC medium, as well as injection position significantly affected release profile. Diffusivity in the injection volume could only influence release profile when this diffusivity was extremely low. Other factors, including initial injection shape, injection volume, and formulation concentration, had only minor impact on release profile. In addition, binding slowed down release, whereas aggregation reduced both total percentage of release and release rate. This study presented Monte Carlo method would potentially become a powerful tool to support multiple development activities related to experimental design, parameter sensitivity analysis, and control strategy development.

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