This study proposes a novel non-linear modelling approach to predict the dissolution profiles of extended-release tablets, by combining a full-factorial design, curve fitting to the dissolution profiles, and artificial neural networks (ANN), with linear regression methods, partial least squares (PLS) and multiple linear regression (MLR) as benchmarks. Hydroxypropylmethylcellulose (HPMC) and carboxymethylcellulose (CMC) grades, active pharmaceutical ingredient (API) lubrication, and compression force were chosen as DoE factors. The resulting batches were tested to obtain their corresponding dissolution profile, and a first-order dissolution equation was fitted to each profile. ANN, PLS and MLR were used to model and predict the tablet-specific constant k which then served to simulate dissolution profiles. This study demonstrates how non-linear methods, specifically ANN, outperform traditional linear models in predicting the complex interactions affecting drug release from extended-release formulations.
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| http://dx.doi.org/10.1016/j.ejps.2024.106976 | DOI Listing |
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