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  • Title: Exploring the effect of raw material properties on continuous twin-screw wet granulation manufacturability.
    Author: Peeters M, Barrera Jiménez AA, Matsunami K, Van Hauwermeiren D, Stauffer F, Arnfast L, Vigh T, Nopens I, De Beer T.
    Journal: Int J Pharm; 2023 Oct 15; 645():123391. PubMed ID: 37696346.
    Abstract:
    Twin-screw wet granulation (TSWG) stands out as a promising continuous alternative to conventional batch fluid bed- and high shear wet granulation techniques. Despite its potential, the impact of raw material properties on TSWG processability remains inadequately explored. Furthermore, the absence of supportive models for TSWG process development with new active pharmaceutical ingredients (APIs) adds to the challenge. This study tackles these gaps by introducing four partial least squares (PLS) models that approximate both the applicable liquid-to-solid (L/S) ratio range and resulting granule attributes (i.e., granule size and friability) based on initial material properties. The first two PLS models link the lowest and highest applicable L/S ratio for TSWG, respectively, with the formulation blend properties. The third and fourth PLS models predict the granule size and friability, respectively, from the starting API properties and applied L/S ratio for twin-screw wet granulation. By analysing the developed PLS models, water-related material properties (e.g., solubility, wettability, dissolution rate), as well as density and flow-related properties (e.g., flow function coefficient), were found to be impacting the TSWG processability. In addition, the applicability of the developed PLS models was evaluated by using them to propose suitable L/S ratio ranges (i.e., resulting in granules with the desired properties) for three new APIs and related formulations followed by an experimental validation thereof. Overall, this study helped to better understand the effect of raw material properties upon TSWG processability. Moreover, the developed PLS models can be used to propose suitable TSWG process settings for new APIs and hence reduce the experimental effort during process development.
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