Pharmaceutical granulation and tablet formulation using neural networks

Pharmaceutical Development and Technology
J G Kesavan, G E Peck

Abstract

Current-day pharmaceutical formulation may be trial and error in nature due to the absence of a clear relationship between the formulation characteristics (output variables) and the material and process variables (input variables). Neural networks are networks of adaptable nodes, which through a process of learning from task examples, store experiential knowledge and make it available for prediction. Prediction of a model granulation and tablet system characteristics from the knowledge of material and process variables utilizing neural networks is the basis of this presentation. The formulation design contained the following variables: granulation equipment, diluent, method of binder addition, and the binder concentration. The material, process, granulation evaluation, and tablet evaluation data of the formulations were used as the data set for training and testing of the neural network models. A comparison of the neural network prediction performance with that of regression models was also done. Both the granulation model and the tablet model converged fairly rapidly in the training step. In the testing step, the predictions for all granulation model variables (geometric mean particle size, flow value, bulk density, and tap de...Continue Reading

References

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Citations

Sep 10, 2011·European Journal of Pharmaceutical Sciences : Official Journal of the European Federation for Pharmaceutical Sciences·E A ColbournP York
Sep 9, 2006·International Journal of Pharmaceutics·A GhaffariM Rafiee-Tehrani
Aug 10, 2000·Pharmaceutical Development and Technology·A Menon, N Nerella
Nov 1, 2011·Pharmaceutical Development and Technology·Firas AlshihabiGabriele Betz
Nov 20, 2015·Open Biology·Magdalena MachowskaRyszard Rzepecki
May 22, 2021·Advanced Drug Delivery Reviews·Pauric BanniganChristine Allen

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