Optimization of Tablet Formulation using Artificial Neural Networks and Genetic Algorithm
DOI:
https://doi.org/10.48047/8c7wd926Abstract
The several elements influencing final product quality make pharmaceutical tablet formulation optimization still a difficult task. This paper investigates a novel method to simplify and improve tablet formulation development using artificial neural networks (ANNs) with genetic algorithms (GA). The work trained ANNs capable of predicting correlations between formulation factors (excipient kinds, concentrations, processing parameters) and critical quality attributes (dissolution rate, hardness, friability) using experimental data from many formulations. Within a GA framework, the trained neural network acted as a predictive model effectively searching the formulation design space for best solutions. By means of this combination technique, formulation parameters producing tablets with exceptional properties were effectively identified while optimizing development time and resources. Excellent match between expected and experimental results shown by the optimized formulations confirmed the potency of the model. Moreover, sensitivity analysis exposed the relative significance of every formulation factor, thereby offering insightful information on formulation mechanics. With reduced experimental burden and expedited time-to----market for novel drug products, this ANN-GA hybrid approach provides pharmaceutical researchers with a potent tool for fast tablet creation, allowing effective navigation of challenging formulation landscapes.
Keyword
Pharmaceutical formulation, Machine learning, Computational optimization, Drug development, Excipient selection, Quality by design (QbD)
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