Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.
The development of solid dosage forms that are both convenient for administration and allow precise drug dosing for pediatric patients is one of the great challenges in contemporary pharmaceutical technology. The presented study has utilized propranolol hydrochloride, as one of the most frequently prescribed drugs that require manipulation of the conventional dosage forms to be administered to children. Multiparticulate oral formulations, powderand granulefilled capsules, as well as mini tablets, were prepared and characterized in terms of their mass and content uniformity and compared to conventional marketed tablets split into halves and quarters. The obtained results have demonstrated the superiority of the multiparticulate formulations, in terms of their average mass and drug content uniformity. It has also been demonstrated that, due to improved flowability, granule-filled capsules are more conveniently compounded and provide higher content uniformity compared to powder-filled capsules. The presented compounding method could be easily employed in community pharmacy settings. Mini tablets with high and uniform content of propranolol hydrochloride have been successfully prepared, thereby presenting a viable strategy for efficient drug dose adjustment.
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