Surrogate modeling of dissolution behavior toward efficient design of tablet manufacturing processes

This paper presents surrogate models of dissolution behavior to identify the critical input parameters in tablet manufacturing processes. Dissolution behavior is a critical quality attribute, and is defined as the time profile of fraction of dissolved active pharmaceutical ingredients in a solvent from a tablet. Dissolution behavior was first calculated by mechanistic models using gPROMS® and fitted by the Weibull model. Random forest regression calculated the Weibull model parameters from the input model parameters.

The developed models enabled to reduce computational cost without losing accuracy. Case studies were performed for both dry and wet granulation processes using paracetamol. The results identified key input and intermediate parameters to the dissolution behavior, e.g., granule bulk density and porosity. In addition, wet granulation had a tendency to make dissolution faster than dry granulation. The proposed approach can contribute to enhance simulation capabilities in the limited time of pharmaceutical process development.

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Excipients used in the study: 312 NF Lactose, Foremost Farms (70 mg), and microcrystalline cellulose (Avicel DG, DuPont)

Kensaku Matsunami, Tomohiro Miura, Keita Yaginuma, Shuichi Tanabe, Sara Badr, Hirokazu Sugiyama, Surrogate modeling of dissolution behavior toward efficient design of tablet manufacturing processes, Computers & Chemical Engineering, 2023, 108141, ISSN 0098-1354, https://doi.org/10.1016/j.compchemeng.2023.108141.