Effect of disintegrants on the properties of multiparticulate tablets comprising starch pellets and excipient granules

Samata Mehta, Thomas De Beer, Jean Paul Remon, Chris Vervaet
International Journal of Pharmaceutics 2012 January 17, 422 (1-2): 310-7
A design of experiments (DOE) approach (2-level full factorial design) was used to investigate the effect of several formulation and process variables on the properties of fast disintegrating tablets comprising starch-based pellets and excipient granules and to optimize and validate the design space. The percentage of starch pellets (30-50%, w/w), type of disintegrants (Ac-di-sol, Explotab, Polyplasdone), percentage of external disintegrant (4-8%, w/w) and compression force (5-15 kN) were the evaluated factors (24 runs+9 centre points=33 experiments), while tablet hardness, friability and disintegration time were the studied tablet properties (responses). Starch pellets were prepared by extrusion-spheronisation. Excipient granules containing microcrystalline cellulose, lactose, internal disintegrant (8%) and polyvinylpyrrolidone K-30 (4%) were prepared by wet granulation. Pellets, granules (700-1000 μm) and external disintegrant were mixed and compressed into oblong tablets (17.1mm long, 8.2mm wide). Evaluation of the effects calculated from the DOE results showed that a lower concentration of starch pellets and higher compression force were required to yield tablets with a high hardness, a low friability (<1%) and short disintegration time (<3 min). Polyplasdone granules had the lowest porosity and friability which was reflected in the DOE study, where the Polyplasdone-containing tablets were harder, less friable and disintegrated faster compared to Ac-di-sol and Explotab-containing tablets. Monte carlo simulations at the optimal factor settings (30% starch pellets, 4% Polyplasdone and 10 kN compression force) indicated that a robust system was formed as the probability to exceed the limits was low for all responses. Validation of the design space (at optimal settings) showed that the results predicted via the DOE models correlated well with the observed experimental data.

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