Peer-Reviewed Journal Details
Mandatory Fields
Pishnamazi M.;Ismail H.;Shirazian S.;Iqbal J.;Walker G.;Collins M.
Application of lignin in controlled release: development of predictive model based on artificial neural network for API release
50 ()
Optional Fields
ANN model Controlled release Dry granulation Kriging interpolation Lignin Pharmaceuticals Tableting
2019, Springer Nature B.V. Predictive models for simulation of drug release from tablets containing lignin as excipient were developed in this work. Two predictive models including Artificial Neural Network (ANN) and hybrid ANN-Kriging were developed to simulate the tablet dissolution. Measured data was collected on the release rate of aspirin tablets prepared by dry granulation via roll compaction followed by milling and tableting. Two formulations were considered, one with lignin and one without. The main aim is to show the effect of lignin as a bio-based natural polymer in tablet manufacturing to control drug dissolution. For the ANN model development, process and formulation parameters including roll pressure and lignin content were considered as the input, while API dissolution was considered as response. The predictions were compared with measured data to calibrate and validate the model. To improve the predictability of the model, Kriging interpolation was used to enhance the number of training points for the ANN. The interpolated data was trained and validated. The final concentration and the dissolution rate were predicted by ANN as well as ANN-Kriging models, and the R2 of greater than 0.99 for most cases was obtained. The validated model was used to evaluate the effect of process parameters on the release rate and it was indicated that the tablets containing lignin have higher release rate compared to tablets without. Also, it was revealed that process parameters do not have significant effect on the tablet release rate, and the tablet release rate is mainly affected by the lignin content. The results indicated that ANN-based model is a powerful tool to predict the API release rate for tablets containing various formulations, and can be used as a predictive tool for design of controlled release systems.
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